HTF Market Intelligence published a new research document of 150+pages on Machine Learning as a Service Market Insights, to 2028 with self-explained Tables and charts in presentable format. In the Study you will find new evolving Trends, Drivers, Restraints, Opportunities generated by targeting market associated stakeholders. The growth of the Machine Learning as a Service market was mainly driven by the increasing R&D spending by leading and emerging player, however latest scenario and economic slowdown have changed complete market dynamics. Some of the key players profiled in the study are Google [United States], IBM Corporation [United States], Microsoft Corporation [United States], Amazon Web Services [United States], BigML [United States], FICO [United States], Yottamine Analytics [United States], Ersatz Labs [United States], Predictron Labs [United Kingdom], H2O.ai [United States], AT&T [United States], Sift Science [United States]

According to HTF Market Intelligence, the Global Machine Learning as a Service market to witness a CAGR of 39.25% during forecast period of 2023-2028. The market is segmented by Global Machine Learning as a Service Market Breakdown by Application (Network Analytics and Automated Traffic Management, Augmented Reality, Predictive Maintenance, Fraud Detection and Risk Analytics, Marketing and Advertising, Others) and by Geography (North America, South America, Europe, Asia Pacific, MEA). The Machine Learning as a Service market size is estimated to increase by USD 288.71 Million at a CAGR of 39.25% from 2023 to 2028. The report includes historic market data from 2017 to 2022E. Currently, market value is pegged at USD 13.95 Million.Get an Inside Scoop of Study, Request now for Sample Study @ https://www.htfmarketintelligence.com/sample-report/global-machine-learning-as-a-service-market

Definition:The Machine Learning as a Service (MLaaS) market refers to the provision of cloud-based platforms or services that enable organizations to leverage machine learning capabilities without the need for in-house expertise, infrastructure, or data storage. MLaaS providers offer a range of services, including tools for data preprocessing, model training and evaluation, deployment, and maintenance. The market also includes providers of pre-built machine learning models, APIs, and software development kits (SDKs) that enable developers to build intelligent applications and automate business processes. MLaaS can help organizations of all sizes to reduce the cost and complexity of adopting machine learning and accelerate their time-to-market for AI-powered solutions.

Market Trends:

Growing Adoption of Machine Learning Services in Healthcare and Research Oriented Marketing Campaigns and Customer-centric Communication

Market Drivers:

Lack of Technical Expertise to Deploy Machine Learning Services

MarketOpportunities:

Increasing Data Volume and Growing IoT Application and Consistent Retraining of Algorithms

The titled segments and sub-section of the market are illuminated below:

The Study Explore the Product Types of Machine Learning as a Service Market:

Key Applications/end-users of Machine Learning as a Service Market: Network Analytics and Automated Traffic Management, Augmented Reality, Predictive Maintenance, Fraud Detection and Risk Analytics, Marketing and Advertising, Others

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With this report you will learn:

Also included in the study are profiles of 15 Machine Learning as a Service vendors, pricing charts, financial outlook, swot analysis, products specification &comparisons matrix with recommended steps for evaluating and determining latest product/service offering.

List of players profiled in this report: Google [United States], IBM Corporation [United States], Microsoft Corporation [United States], Amazon Web Services [United States], BigML [United States], FICO [United States], Yottamine Analytics [United States], Ersatz Labs [United States], Predictron Labs [United Kingdom], H2O.ai [United States], AT&T [United States], Sift Science [United States]

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Quick Snapshot and Extracts from TOC of Latest Edition

Overview of Machine Learning as a Service Market

Machine Learning as a Service Size (Sales Volume) Comparison by Type (2023-2028)

Machine Learning as a Service Size (Consumption) and Market Share Comparison by Application (2023-2028)

Machine Learning as a Service Size (Value) Comparison by Region (2023-2028)

Machine Learning as a Service Sales, Revenue and Growth Rate (2023-2028)

Machine Learning as a Service Competitive Situation and Current Scenario Analysis

Strategic proposal for estimating sizing of core business segments

Players/Suppliers High Performance Pigments Manufacturing Base Distribution, Sales Area, Product Type

Analyse competitors, including all important parameters of Machine Learning as a Service

Machine Learning as a Service Manufacturing Cost Analysis

Latest innovative headway and supply chain pattern mapping of leading and merging industry players

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Machine Learning as a Service Market to Predicts Huge Growth by ... - Reedley Exponent

AI and ML in D365

Microsoft has embedded AI and ML capabilities across multiple modules of D365 to augment its functionality and provide users with intelligent insights and automation.

Lets explore some key areas where AI and ML are integrated:

Sales and Marketing

In the sales and marketing modules of D365, AI-powered features enable businesses to enhance customer engagement, personalize marketing campaigns, and identify sales opportunities. ML algorithms analyze customer data, social media interactions, and historical buying patterns to generate predictive lead scoring, recommend personalized product offerings, and optimize marketing strategies. This results in improved targeting, increased conversion rates, and more effective customer segmentation.

Customer Service

D365 leverages AI and ML to improve customer service experiences. Chatbots with natural language processing (NLP) capabilities can understand and respond to customer queries, providing quick and accurate resolutions. ML algorithms analyze customer feedback and sentiment to gauge customer satisfaction levels, allowing organizations to proactively address any issues. This leads to enhanced customer support, reduced response times, and increased customer satisfaction.

Field Service

ML algorithms in D365s Field Service module enable predictive maintenance by analyzing historical data and identifying patterns that indicate equipment failures. This helps organizations optimize maintenance schedules, reduce downtime, and improve operational efficiency. By leveraging AI-powered insights, field service teams can proactively address potential issues, minimize service disruptions, and deliver better service quality.

Finance and Operations

AI and ML capabilities in D365s Finance and Operations modules assist in automating and streamlining financial processes. These include fraud detection, predictive cash flow analysis, intelligent forecasting, and supply chain optimization based on demand patterns and market trends. By leveraging AI and ML, organizations can optimize financial decision-making, reduce risks, and improve overall operational efficiency.

Also Read: Streamlining business operations with D365 Business Central workflows

The integration of AI and ML in D365 offers several significant benefits to businesses:

Enhanced Efficiency

Automation and intelligent algorithms enable businesses to improve efficiency by reducing manual efforts and streamlining processes. Tasks such as data entry, report generation, and customer inquiries can be automated, allowing employees to focus on higher-value tasks and strategic decision-making.

Personalized Experiences

AI and ML capabilities enable D365 to deliver personalized experiences to customers. By analyzing vast amounts of customer data, businesses can tailor their interactions, offers, and recommendations based on individual preferences. This personalized approach increases customer satisfaction, strengthens customer relationships, and drives customer loyalty.

Smarter Decision-Making

With AI and ML insights, organizations can make data-driven decisions based on accurate predictions and trends. By analyzing large datasets, identifying patterns, and generating actionable insights, D365 empowers businesses to identify emerging opportunities, optimize operations, and proactively address potential risks.

Improved Customer Service

The integration of AI-driven chatbots and sentiment analysis tools in D365s customer service modules enhances response times, provides 24/7 support, and improves the overall customer experience. Chatbots can handle routine inquiries and provide instant support, while sentiment analysis tools help organizations gauge customer satisfaction levels and address any issues promptly.

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Exploring the integration of artificial intelligence (AI) and machine learning (ML) capabilities in D365 - Security Boulevard

Data

This paper selects parts of China and surrounding areas as the research area. The research data selects the NDVI data of MODIS (NDVIm) and AVHRR (NDVIa) sensors on Terra and Aqua, and the NDVI data of VIRR (NDVIv) sensors on Fengyun satellite31. (I) Compare the NDVIv with the NDVIa, and the NDVIa and NDVIm. (II) Find out the functional relationship between NDVIa and NDVIm, and the functional relationship between NDVIv and NDVIa through comparison. (III) use NDVIa to correct NDVIv data to a level equivalent to NDVIm.

The data used in this study include (see Table 1): NDVIa from 1982 to 2015, NDVIm from 2000 to 2019, and NDVIv from 2015 to 2020, all of which have a resolution of 0.05. Because in 2005, there are both NDVIa data and NDVIm data. Therefore, we use the data of this year to compare NDVIa and NDVIm, and explore the correlation between the two. Because in 2015, there are both NDVIv data and NDVIa data. Therefore, we used the data of this year to compare NDVIv and NDVIa and explore the correlation between the two. Finally, we compared the corrected NDVIv of 2019 with the NDVIm of 2019 to verify the success of the model we constructed.

Figure1 shows the spectral response function curves of different satellite sensors in the visible and near-infrared spectrum32. By comparison, it can be found that in the visible light band, the spectral response function of MODIS is narrower than AVHRR, and the spectral response function of AVHRR is narrower than VIRR. In the near-infrared band, MODIS still has the narrowest spectral response function, followed by VIRR, and AVHRR has the widest spectral response function. The channel, wavelength range, corresponding spectrum and sub-satellite resolution information of MODIS, AVHRR, and VIRR sensors are shown in Table 2.

Spectral response function curves of different satellite sensors in the visible and near-infrared spectrum29.

Linear model is a form of machine learning model. The form of linear model is relatively simple and easy to model. The linear model contains some important basic ideas in machine learning. Many more powerful nonlinear models can be obtained by introducing hierarchical structure or high-dimensional mapping on the basis of linear models. There are many forms of linear models, and linear regression is a common one. Linear regression tries to learn a linear model to predict the real-valued output markers as accurately as possible. By establishing a linear model on the data set, a loss function is established, and finally the model parameters are determined with the goal of optimizing the cost function, so as to obtain the model for subsequent prediction. The general linear regression algorithm process is as presented in Fig.2.

Schematic diagram of the linear regression algorithm flow.

The detailed procedure is as follows33:

The data is standardized and preprocessed. The preprocessing includes data cleaning, screening, organization, etc., so that the data can be input into the machine learning model as feature variables.

Different machine learning algorithms are selected to train a separate data set, and find the best machine learning model, establish a machine learning model based on the normalized vegetation index product retrieved by Fengyun satellite.

Verify and output the long-term series normalized vegetation index of the Fengyun satellite.

For 20012005, there are both AVHRR NDVI data and MODIS NDVI data. Therefore, we used the data of these 5years to compare NDVIa and NDVIm and explore the correlation between the two. Because 2015 has both VIRR's NDVI data and AVHRR's NDVI data. Therefore, we used the data of this year to compare NDVIv and NDVIa and explore the correlation between the two. Finally, we compared the corrected NDVIv of 2019 with the NDVIm of 2019 to verify the success of the model we constructed.

The linear machine learning model is used to construct the optimal functional relationship between the NDVIa and the NDVIm. The formula is as presented in formula (1):

$${text{Y}}_{{{text{NDVIm}}}} = left{ {{text{k2}}00{1},{text{k2}}00{2},{text{k2}}00{3},{text{k2}}00{4},{text{k2}}00{5},{text{kmin}},{text{kmax}},{text{kave}}} right} times {text{X}}_{{{text{NDVIa}}}} + left{ {{text{m2}}00{1},{text{m2}}00{2},{text{m2}}00{3},{text{m2}}00{4},{text{m2}}00{5},{text{mmin}},{text{mmax}},{text{mmean}}} right}$$

(1)

In the formula, XNDVIa is the NDVI value of AVHRR, YNDVIm is the NDVI value of MODIS, k is the coefficient value of the linear function relationship between NDVIa and NDVIm, k2001, k2002, k2003, k2004, k2005, kmin, kmax, kave are the coefficients of 2001, 2002, 2003, 2004, 2005, the 5-year minimum, 5-year maximum, and the 5-year coefficient average respectively. m is the intercept of the linear function relationship between the NDVIa and the NDVIm, m2001, m2002, m2003, m2004, m2005, mmin, mmax, mmean are the intercept of 2001, 2002, 2003, 2004, 2005 Year, 5-year minimum, 55-year maximum, and 5-year average respectively.

Through multiple cross-comparison analysis, the optimal coefficient k and the optimal coefficient m are selected, and then the optimal functional relationship between NDVIa and NDVIm is determined.

Based on the above analysis, we continue to construct the functional relationship between NDVIa and NDVIv, according to formula (2).

$${text{X}}_{{{text{NDVIa}}}} = {text{aZ}}_{{{text{NDVIv}}}} + {text{b}}{.}$$

(2)

In the formula (2), ZNDVIv is the NDVI value of VIRR, XNDVIa is the NDVI value of AVHRR, a is the coefficient value of the linear function relationship between the NDVIv and the NDVIa fitting, and b is the intercept of the linear function relationship between NDVIv and NDVIa fitting.

Replacing the functional relationship between NDVIa and NDVIv into the optimal NDVIa and NDVIm functional relationships filtered out to obtain the refitted NDVIv, which is Yvir_ndvi in the formula (3). The functional relationship formula of the simulated NDVIv is as follows (3):

$${text{C}}_{{{text{NDVIcv}}}} = {text{k}}_{{{text{NDVIa}}}} + {text{m}} = {text{k}}left( {{text{aZ}}_{{{text{NDVIv}}}} + {text{b}}} right) + {text{m}} = {text{kaZ}}_{{{text{NDVIv}}}} + {text{kb}} + {text{m}}{.}$$

(3)

In the formula, CNDVIcv is the corrected NDVIv(NDVIcv), k is the optimal coefficient of the correlation between NDVIa and NDVIm, and m is the optimal intercept of the correlation between NDVIa and NDVIm.

The data of 2005 were selected to compare NDVIm and NDVIa in some parts of China and surrounding areas. The data of 2015 were selected to compare NDVIv and NDVIa in some parts of China and surrounding areas. Through analysis, the correlation among NDVIv, NDVIa and NDVIm is found.

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Research on the establishment of NDVI long-term data set based on ... - Nature.com

Abstract

Objective To develop a clinically useful model that estimates the 10 year risk of breast cancer related mortality in women (self-reported female sex) with breast cancer of any stage, comparing results from regression and machine learning approaches.

Design Population based cohort study.

Setting QResearch primary care database in England, with individual level linkage to the national cancer registry, Hospital Episodes Statistics, and national mortality registers.

Participants 141765 women aged 20 years and older with a diagnosis of invasive breast cancer between 1 January 2000 and 31 December 2020.

Main outcome measures Four model building strategies comprising two regression (Cox proportional hazards and competing risks regression) and two machine learning (XGBoost and an artificial neural network) approaches. Internal-external cross validation was used for model evaluation. Random effects meta-analysis that pooled estimates of discrimination and calibration metrics, calibration plots, and decision curve analysis were used to assess model performance, transportability, and clinical utility.

Results During a median 4.16 years (interquartile range 1.76-8.26) of follow-up, 21688 breast cancer related deaths and 11454 deaths from other causes occurred. Restricting to 10 years maximum follow-up from breast cancer diagnosis, 20367 breast cancer related deaths occurred during a total of 688564.81 person years. The crude breast cancer mortality rate was 295.79 per 10000 person years (95% confidence interval 291.75 to 299.88). Predictors varied for each regression model, but both Cox and competing risks models included age at diagnosis, body mass index, smoking status, route to diagnosis, hormone receptor status, cancer stage, and grade of breast cancer. The Cox models random effects meta-analysis pooled estimate for Harrells C index was the highest of any model at 0.858 (95% confidence interval 0.853 to 0.864, and 95% prediction interval 0.843 to 0.873). It appeared acceptably calibrated on calibration plots. The competing risks regression model had good discrimination: pooled Harrells C index 0.849 (0.839 to 0.859, and 0.821 to 0.876, and evidence of systematic miscalibration on summary metrics was lacking. The machine learning models had acceptable discrimination overall (Harrells C index: XGBoost 0.821 (0.813 to 0.828, and 0.805 to 0.837); neural network 0.847 (0.835 to 0.858, and 0.816 to 0.878)), but had more complex patterns of miscalibration and more variable regional and stage specific performance. Decision curve analysis suggested that the Cox and competing risks regression models tested may have higher clinical utility than the two machine learning approaches.

Conclusion In women with breast cancer of any stage, using the predictors available in this dataset, regression based methods had better and more consistent performance compared with machine learning approaches and may be worthy of further evaluation for potential clinical use, such as for stratified follow-up.

Clinical prediction models already support medical decision making in breast cancer by providing individualised estimations of risk. Tools such as PREDICT Breast1 or the Nottingham Prognostic Index23 are used in patients with early stage, surgically treated breast cancer for prognostication and selection of post-surgical treatment. Such tools are, however, inherently limited to treatment specific subgroups of patients. Accurate estimation of mortality risk after diagnosis across all patients with breast cancer of any stage may be clinically useful for stratifying follow-up, counselling patients about their expected prognosis, or identifying high risk individuals suitable for clinical trials.4

The scope for machine learning approaches in clinical prediction modelling has attracted considerable interest.56789 Some have posited that these flexible approaches might be more suitable for capturing non-linear associations, or for handling higher order interactions without explicit programming.10 Others have raised concerns about model transparency,1112 interpretability,13 risk of algorithmic bias exacerbating extant health inequalities,14 quality of evaluation and reporting,15 ability to handle rare events16 or censoring,17 and appropriateness of comparisons11 to regression based methods.18 Indeed, systematic reviews have shown no inherent benefit of machine learning approaches over appropriate statistical models in low dimensional clinical settings.18 As no a priori method exists to predict which modelling approach may yield the most useful clinical prediction model for a given scenario, frameworks that appropriately compare different models can be used.

Owing to the risks of harm from suboptimal medical decision making, clinical prediction models should be comprehensively evaluated for performance and utility,19 and, if widespread clinical use is intended, heterogeneity in model performance across relevant patient groups should be explored.20 Given developments in treatment for breast cancer over time, with associated temporal falls in mortality, another key consideration is the transportability of risk modelsnot just across regions and subpopulations but also across time periods.21 Although such dataset shift22 is a common issue with any algorithm sought to be deployed prospectively, this is not routinely explored. Robust evaluation is necessary but is non-uniform in the modelling of breast cancer prognostication.23 A systematic review identified 58 papers that assessed prognostic models for breast cancer,24 but only one study assessed clinical effectiveness by means of a simplistic approach measuring the accuracy of classifying patients into high or low risk groups. A more recent systematic review25 appraised 922 breast cancer prediction models using PROBAST (prediction model risk of bias assessment tool)26 and found that most of the clinical prediction models are poorly reported, show methodological flaws, or are at high risk of bias. Of the 27 models deemed to be at low risk of bias, only one was intended to estimate the risks of breast cancer related mortality in women with disease of any stage.27 However, this small study of 287 women using data from a single health department in Spain had methodological limitations, including possibly insufficient data to fit a model (see supplementary table 1) and uncertain transportability to other settings. Therefore, no reliable prediction model exists to provide accurate risk assessment of mortality in women with breast cancer of any stage. Although we refer to women throughout, this is based on self-reported female sex, which may include some individuals who do not identify as female.

We aimed to develop a clinically useful prediction model to reliably estimate the risks of breast cancer specific mortality in any woman with a diagnosis of breast cancer, in line with modern best practice. Utilising data from 141675 women with invasive breast cancer diagnosed between 2000 and 2020 in England from a population representative, national linked electronic healthcare record database, this study comparatively developed and evaluated clinical prediction models using a combination of analysis methods within an internal-external validation strategy.2829 We sought to identify and compare the best performing methods for model discrimination, calibration, and clinical utility across all stages of breast cancer.

We evaluated four model building approaches: two regression methods (Cox proportional hazards and competing risks regression) and two machine learning methods (XGBoost and neural networks). The prediction horizon was 10 year risk of breast cancer related death from date of diagnosis. The study was conducted in accordance with our protocol30 and is reported consistent with the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) guidelines.31

Assuming 100 candidate predictor parameters, an annual mortality rate of 0.024 after diagnosis,32 and a conservative 15% of the maximal Cox-Snell R2, we estimated that the minimum sample size for fitting the regression models was 10080, with 1452 events, and 14.52 events for each predictor parameter.3334 No standard method exists to estimate minimum sample size for our machine learning models of interestsome evidence, albeit on binary outcome data, suggests that some machine learning methods may require much more data.35

The QResearch database was used to identify an open cohort of women aged 20 years and older (no upper age limit) at time of diagnosis of any invasive breast cancer between 1 January 2000 and 31 December 2020 in England. QResearch has collected data from more than 1500 general practices in the United Kingdom since 1989 and comprises individual level linkage across general practice data, NHS Digitals Hospital Episode Statistics, the national cancer registry, and the Office for National Statistics death registry.

The outcome for this study was breast cancer related mortality within 10 years from the date of a diagnosis of invasive breast cancer. We defined the diagnosis of invasive breast cancer as the presence of breast cancer related Read/Systemised Nomenclature of Medicine Clinical Terms (SNOMED) codes in general practice records, breast cancer related ICD-10 (international classification of diseases, 10th revision) codes in Hospital Episode Statistics data, or as a patient with breast cancer in the cancer registry (stage >0; whichever occurred first). The outcome, breast cancer death, was defined as the presence of relevant ICD-10 codes as any cause of death (primary or contributory) on death certificates from the ONS register. We excluded women with recorded carcinoma in situ only diagnoses as these are non-obligate precursor lesions and present distinct clinical considerations.36 Clinical codes used to define predictors and outcomes are available in the QResearch code group library (https://www.qresearch.org/data/qcode-group-library/). Follow-up time was calculated from the first recorded date of breast cancer diagnosis (earliest recorded on any of the linked datasets) to the earliest of breast cancer related death, other cause of death, or censoring (reached end of study period, left the registered general practice, or the practice stopped contributing to QResearch). The status at last follow-up depended on the modelling framework (ie, Cox proportional hazards or competing risks framework). The maximum follow-up was truncated to 10 years, in line with the model prediction horizon. Supplementary table 2 shows ascertainment of breast cancer diagnoses across the linked datasets.

Individual participant data were extracted on the candidate predictor parameters listed in Box 1, as well as geographical region, auxiliary variables (breast cancer treatments), and dates of events of interest. Candidate predictors were based on evidence from the clinical, epidemiological, or prediction model literature.12337383940 The most recently recorded values before or at the time of breast cancer diagnosis were used with no time restriction. Data were available from the cancer registry about cancer treatment within one year of diagnosis (eg, chemotherapy) but without any corresponding date. The intended model implementation (prediction time) would be at the breast cancer multidisciplinary team meeting or similar clinical setting, following initial diagnostic investigations and staging. To avoid information leakage, and since we did not seek model treatment selection within a causal framework,41 breast cancer treatment variables were not included as predictors.

Age at breast cancer diagnosiscontinuous or fractional polynomial

Townsend deprivation score at cohort entrycontinuous or fractional polynomial

Body mass index (most recently recorded before breast cancer diagnosis)continuous or fractional polynomial

Self-reported ethnicity

Tumour characteristics:

Cancer stage at diagnosis (ordinal: I, II, III, IV)

Differentiation (categorical: well differentiated, moderately differentiated, poorly or undifferentiated)

Oestrogen receptor status (binary: positive or negative)

Progesterone receptor status (binary: positive or negative)

Human epidermal growth factor receptor 2 (HER2) status (binary: positive or negative)

Route to diagnosis (categorical: emergency presentation, inpatient elective, other, screen detected, two week wait)

Comorbidities or medical history on general practice or Hospital Episodes Statistics data (recorded before or at entry to cohort; categorical unless stated otherwise):

Hypertension

Ischaemic heart disease

Type 1 diabetes mellitus

Type 2 diabetes mellitus

Chronic liver disease or cirrhosis

Systemic lupus erythematosus

Chronic kidney disease (ordinal: none or stage 2, stage 3, stage 4, stage 5)

Vasculitis

Family history of breast cancer (categorical: recorded in general practice or Hospital Episodes Statistics data, before or at entry to cohort)

Drug use (before breast cancer diagnosis):

Hormone replacement therapy

Antipsychotic

Tricyclic antidepressant

Selective serotonin reuptake inhibitor

Monoamine oxidase inhibitor

Oral contraceptive pill

Angiotensin converting enzyme inhibitor

blocker

Renin-angiotensin aldosterone antagonists

Age (fractional polynomial terms)family history of breast cancer

Ethnicityage (fractional polynomial terms)

Fractional polynomial42 terms for the continuous variables age at diagnosis, Townsend deprivation score, and body mass index (BMI) at diagnosis were identified in the complete data. This was done separately for the Cox and competing risks regression models, with a maximum of two powers permitted.

Multiple imputation with chained equations was used to impute missing data for BMI, ethnicity, Townsend deprivation score, smoking status, cancer stage at diagnosis, cancer grade at diagnosis, HER2 status, oestrogen receptor status, and progesterone receptor status under the missing at random assumption.4344 The imputation model contained all other candidate predictors, the endpoint indicator, breast cancer treatment variables, the Nelson-Aalen cumulative hazard estimate,45 and the period of cohort entry (period 1=1 January 2000-31 December 2009; period 2=1 January 2010-31 December 2020). The natural logarithm of BMI was used in imputation for normality, with imputed values exponentiated back to the regular scale for modelling. We generated 50 imputations and used these in all model fitting and evaluation steps. Although missing data were observed in the linked datasets used for model development, in the intended use setting (ie, risk estimation at breast cancer multidisciplinary team after a medical history has been taken), the predictors would be expected to be available for all patients.

Models were fit to the entire cohort and then evaluated using internal-external cross validation,28 which involved splitting the dataset by geographical region (n=10) and time period (see figure 1 for summary). For the internal-external cross validation, we recalculated follow-up so that those women who entered the study during the first study decade and survived into the second study period had their follow-up truncated (and status assigned accordingly) at 31 December 2009. This was to emulate two wholly temporally distinct datasets, both with maximum follow-up of 10 years, for the purposes of estimating temporal transportability of the models.

Summary of internal-external cross validation framework used to evaluate model performance for several metrics, and transportability

For the approach using Cox proportional hazards modelling, we treated other (non-breast cancer) deaths as censored. A full Cox model was fitted using all candidate predictor parameters. Model fitting was performed in each imputed dataset and the results combined using Rubins rules, and then this pooled model was used as the basis for predictor selection. We selected binary or multilevel categorical predictors associated with exponentiated coefficients >1.1 or <0.9 (at P<0.01) for inclusion, and interactions and continuous variables were selected if associated with P<0.01. Then these were used to refit the final Cox model. The predictor selection approach benefits from starting with a full, plausible, maximally complex model,46 and then considers both the clinical and the statistical magnitude of predictors to select a parsimonious model while making use of multiply imputed data.4748 This approach has been used in previous clinical prediction modelling studies using QResearch.495051 Clustered standard errors were used to account for clustering of participants within individual general practices in the database.

Deaths from other, non-breast cancer related causes represent a competing risk and in this framework were handled accordingly.30 We repeated the fractional polynomial term selection and predictor selection processes for the competing risks models owing to potential differential associations between predictors and risk or functional forms thereof. A full model was fit with all candidate predictors, with the same magnitude and significance rule used to select the final predictors.

The competing risks model was developed using jack-knife pseudovalues for the Aalen-Johansen cumulative incidence function at 10 years as the outcome variable52the pseudovalues were calculated for the overall cohort (for fitting the model) and then separately in the data from period 1 and from period 2 for the purposes of internal-external cross validation. These values are a marginal (pseudo) probability that can then be used in a regression model to predict individuals probabilities conditional on the observed predictor values. Pseudovalues for the cumulative incidence function at 10 years were regressed on the predictor parameters in a generalised linear model with a complementary log-log link function525354 and robust standard errors to account for the non-independence of pseudovalues. The resultant coefficients are statistically similar to those of the Fine-Gray model5254 but computationally less burdensome to obtain, and permit direct modelling of probabilities.

All fitting and evaluation of the Cox and competing risks regression models occurred in each separate imputed dataset, with Rubins rules used to pool coefficients and standard errors across all imputations.55

The XGBoost and neural network approaches were adapted to handle right censored data in the setting of competing risks by using the jack-knife pseudovalues for the cumulative incidence function at 10 years as a continuous outcome variable. The same predictor parameters as selected for the competing risks regression model were used for the purposes of benchmarking. The XGBoost model used untransformed values for continuous predictors, but these were minimum-maximum scaled (constrained between 0 and 1) for the neural network. We converted categorical variables with more than two levels to dummy variables for both machine learning approaches.

We fit the XGBoost and neural network models to the entire available cohort and used bayesian optimisation56 with fivefold cross validation to identify the optimal configuration of hyperparameters to minimise the root mean squared error between observed pseudovalues and model predictions. Fifty iterations of bayesian optimisation were used, with the expected improvement acquisition function.

For the XGBoost model, we used bayesian optimisation to tune the number of boosting rounds, learning rate (eta), tree depth, subsample fraction, regularisation parameters (alpha gamma, and lambda), and column sampling fractions (per tree, per level). We used the squared error regression option as the objective, and the root mean squared error as the evaluation metric.

To permit modelling of higher order interactions in this tabular dataset, we used a feed forward artificial neural network approach with fully connected dense layers: the model architecture comprised an input layer of 26 nodes (ie, number of predictor parameters), rectified linear unit activation functions in each hidden layer, and a single linear activation output node to generate predictions for the pseudovalues of the cumulative incidence function. The Adam optimiser was used,57 with the initial learning rate, number of hidden layers, number of nodes in each hidden layer, and number of training epochs tuned using bayesian optimisation. If the loss function had plateaued for three epochs, we halved the learning rate, with early stopping after five epochs if the loss function had not reduced by 0.0001. The loss function was the root mean squared error between observed and predicted pseudovalues due to the continuous nature of the target variable.58

After identification of the optimal hyperparameter configurations, we fit the models accordingly to the entirety of the cohort data. We then assessed the performance of these models using the internal-external cross validation strategythis resembled that for the regression models but with the addition of a hyperparameter tuning component (fig 1). During each iteration of internal-external cross validation, we used bayesian optimisation with fivefold cross validation to identify the optimal hyperparameters for the model fitted to the development data from period 1, which we then tested on the held-out period 2 data. This therefore constituted a form of nested cross validation.59

As the XGBoost and neural network models do not constitute a linear set of parameters and do not have standard errors (therefore not able to be pooled using Rubins rules), we used a stacked imputation strategy. The 50 imputed datasets were stacked to form a single, long dataset, which enabled us to use the same full data as for the regression models, avoiding suboptimal approaches such as complete case analysis or single imputation. For model evaluation after internal-external cross validation, we used approaches based on Rubins rules,55 with performance estimates calculated in each separate imputed dataset using the internal-external cross validation generated individual predictions, and then the estimates were pooled.

Predicted risks when using the Cox model can be derived by combining the linear predictor with the baseline hazard function using the equation: predicted event probability=1Stexp(X) where St is the baseline survival function calculated at 10 years, and X is the individuals linear predictor. For internal-external cross validation, we estimated baseline survival functions separately in each imputation in the period 1 data (continuous predictors centred at the mean, binary predictors set to zero), with results pooled across imputations in accordance with Rubins rules.55 We estimated the final models baseline function similarly but using the full cohort data.

Probabilistic predictions for the competing risks regression model were directly calculated using the following transformation of the linear predictors (X, which included a constant term): predicted event probability=1exp(exp(X)).

As the XGBoost and neural network approaches modelled the pseudovalues directly, we handled the generated predictions as probabilities (conditional on the predictor values). As pseudovalues are not restricted to lie between 0 and 1, we clipped the XGBoost and neural network model predictions to be between 0 and 1 to represent predicted probabilities for model evaluation.

Discrimination was assessed using Harrells C index,60 calculated at 10 years and taking censoring into accountthis used inverse probability of censoring weights for competing risks regression, XGBoost, and neural networks given their competing risks formulation.61 Calibration was summarised in terms of the calibration slope and calibration-in-the-large.6263 Region level results for these metrics were computed during internal-external cross validation and pooled using random effects meta-analysis20 with the Hartung-Knapp-Sidik-Jonkmann method64 to provide an estimate of each metric with a 95% confidence interval, and with a 95% prediction interval. The prediction interval estimates the range of model performance on application to a distinct dataset.20 We also computed these metrics by ethnicity, 10 year age groups, and cancer stage (I-IV) using the pooled, individual level predictions.

Using the individual level predictions from all models, we generated smoothed calibration plots to assess alignment of observed and predicted risks across the spectrum of predicted risks. We generated these using a running smoother through individual risk predictions, and observed individual pseudovalues65 for the Kaplan-Meier failure function (Cox model) or cumulative incidence function (all other models).

Meta-regression following Hartung-Knapp-Sidik-Jonkmann random effects models were used to calculate measures of I2 and R2 to assess the extent to which inter-regional heterogeneity in discrimination and calibration metrics could be attributable to regional variation in age, BMI (standard deviation thereof), mean deprivation score, and ethnic diversity (percentage of people of non-white ethnicity).20 These region level characteristics were estimated using the data from period 2.

We compared the models for clinical utility using decision curve analysis.66 This analysis assesses the trade-off between the benefits of true positives (breast cancer deaths) and the potential harms that may arise from false positives across a range of threshold probabilities. Each model was compared using the two default scenarios of treat all or treat none, with the mean model prediction used for each individual across all imputations. This approach implicitly takes into account both discrimination and calibration and also extends model evaluation to consider the ramifications on clinical decision making.67 The competing risk of other, non-breast-cancer death was taken into account. Decision curves were plotted overall, and by cancer stage to explore potential utility for all breast cancers.

Predictions generated from the Cox proportional hazards model and other, competing risks approaches have different interpretations, owing to their differential handling of competing events and their modelling of hazard functions with distinct statistical properties.

Data processing, multiple imputation, regression modelling, and evaluation of internal-external cross validation results utilised Stata (version 17). Machine learning modelling was performed in R 4.0.1 (xgboost, keras, and ParBayesianOptimization packages), with an NVIDIA Tesla V100 used for graphical processing unit support. Analysis code is available in repository https://github.com/AshDF91/Breast-cancer-prognosis.

Two people who survived breast cancer were involved in discussions about the scope of the project, candidate predictors, importance of research questions, and co-creation of lay summaries before submitting the project for approval. This project was also presented at an Oxfordshire based breast cancer support group to obtain qualitative feedback on the studys aims and face validity or plausibility of candidate predictors, and to discuss the acceptability of clinical risk models to guide stratified breast cancer care.

A total of 141765 women aged between 20 and 97 years at date of breast cancer diagnosis were included in the study. During the entirety of follow-up (median 4.16 (interquartile range 1.76-8.26) years), there were 21688 breast cancer related deaths and 11454 deaths from other causes. Restricting to 10 years maximum follow-up from breast cancer diagnosis, 20367 breast cancer related deaths occurred during a total of 688564.81 person years. The crude mortality rate was 295.79 per 10000 person years (95% confidence interval 291.75 to 299.88). Supplementary figure 1 presents ethnic group specific mortality curves. Table 1 shows the baseline characteristics of the cohort overall and separately by decade defined subcohort.

Summary characteristics of final study cohort overall and separated into temporally distinct subcohorts used in internal-external cross validation. Values are number (column percentage) unless stated otherwise

After the cohort was split by decade of cohort entry and follow-up was truncated for the purposes of internal-external cross validation, 7551 breast cancer related deaths occurred in period 1 during a total of 211006.95 person years of follow-up (crude mortality rate 357.96 per 10000 person years (95% confidence interval 349.87 to 366.02)). In the period 2 data, 8808 breast cancer related deaths occurred during a total of 297066.74 person years of follow-up, with a lower crude mortality rate of 296.50 per 10000 person years (290.37 to 302.76) observed.

We selected non-linear fractional polynomial terms for age and BMI (see supplementary figure 2). The final Cox model after predictor selection is presented as exponentiated coefficients in figure 2 for transparency, with the full model detailed in supplementary table 3. Model performance across all ethnic groups is summarised in supplementary table 4: discrimination ranged between a Harrells C index of 0.794 (95% confidence interval 0.691 to 0.896) in Bangladeshi women to 0.931 (0.839 to 1.000) in Chinese women, but the low numbers of event counts in smaller ethnic groups (eg, Chinese) meant that overall calibration indices were imprecisely estimated for some.

Final Cox proportional hazards model predicting 10 year risk of breast cancer mortality, presented as its exponentiated coefficients (hazard ratios with 95% confidence intervals). Model contains fractional polynomial terms for age (0.5, 2) and body mass index (2, 2), but these are not plotted owing to reasons of scale. Model also includes a baseline survival term (not plottedthe full model as coefficients is presented in the supplementary file). ACE=angiotensin converting enzyme; CI=confidence interval; CKD=chronic kidney disease; ER=oestrogen receptor; GP=general practitioner; HER2= human epidermal growth factor receptor 2; HRT=hormone replacement therapy; PR=progesterone receptor; RAA=renin-angiotensin aldosterone; SSRI=selective serotonin reuptake inhibitor

Overall, the Cox models random effects meta-analysis pooled estimate for Harrells C index was the highest of any model, at 0.858 (95% confidence interval 0.853 to 0.864, 95% prediction interval 0.843 to 0.873). A small degree of miscalibration occurred on summary metrics, with a meta-analysis pooled estimate for the calibration slope of 1.108 (95% confidence interval 1.079 to 1.138, 95% prediction interval 1.034 to 1.182) (table 2). Figure 3, figure 4, and figure 5 show the meta-analysis pooling of performance metrics across regions. Smoothed calibration plots showed generally good alignment of observed and predicted risks across the entire spectrum of predicted risks, albeit with some minor over-prediction (fig 6).

Summary performance metrics for all four models, estimated using random effects meta-analysis after internal-external cross validation.

Results from internal-external cross validation of Cox proportional hazards model for Harrells C index. Plots display region level performance metric estimates and 95% confidence intervals (diamonds with lines), and an overall pooled estimate obtained using random effects meta-analysis and 95% confidence interval (lowest diamond) and 95% prediction interval (line through lowest diamond). CI=confidence interval

Results from internal-external cross validation of Cox proportional hazards model for calibration slope. Plots display region level performance metric estimates and 95% confidence intervals (diamonds with lines), and an overall pooled estimate obtained using random effects meta-analysis and 95% confidence interval (lowest diamond) and 95% prediction interval (line through lowest diamond). CI=confidence interval

Results from internal-external cross validation of Cox proportional hazards model for calibration-in-the-large. Plots display region level performance metric estimates and 95% confidence intervals (diamonds with lines), and an overall pooled estimate obtained using random effects meta-analysis and 95% confidence interval (lowest diamond) and 95% prediction interval (line through lowest diamond). CI=confidence interval

Calibration of the four models tested. Top row shows the alignment between predicted and observed risks for all models with smoothed calibration plots. Bottom row summarises the distribution of predicted risks from each model as histograms

Regional differences in the Harrells C index were relatively slight. None of the inter-region heterogeneity observed for discrimination (I2=53.14%) and calibration (I2=42.35%) appeared to be attributable to regional variation in any of the sociodemographic factors examined (table 3). The model discriminated well across cancer stages, but discriminative capability decreased with increasing stage; moderate variation was observed in calibration across cancer stage groups (supplementary table 9).

Random effects meta-regression of relative contributions of regional variation in age, body mass index, deprivation, and non-white ethnicity on inter-regional differences in performance metrics after internal-external cross validation

Similar fractional polynomial terms were selected for age and BMI in the competing risks regression model (see supplementary figure 2), and predictor selection yielded a model with fewer predictors than the Cox model. The competing risks regression model is presented as exponentiated coefficients in figure 7, with the full model (including constant term) detailed in supplementary table 5. Ethnic group specific discrimination and overall calibration metrics are detailed in supplementary table 4the model generally performed well across ethnic groups, with similar discrimination, but there was some overt miscalibration on summary metricsalthough some metrics were estimated imprecisely owing to small event counts in some ethnic groups.

Final competing risks regression model predicting 10 year risk of breast cancer mortality, presented as its exponentiated coefficients (subdistribution hazard ratios with 95% confidence intervals). Model contains fractional polynomial terms for age (1, 2) and body mass index (2, 2), but these are not plotted owing to reasons of scale. Model also includes an intercept term (not plottedsee supplementary file for full model as coefficients). CI=confidence interval; ER=oestrogen receptor; GP=general practitioner; HER2=human epidermal growth factor receptor 2; HRT=hormone replacement therapy; PR=progesterone receptor

The random effects meta-analysis pooled Harrells C index was 0.849 (95% confidence interval 0.839 to 0.859, 95% prediction interval 0.821 to 0.876). Some evidence suggested systematic miscalibration overallthat is, a pooled calibration slope of 1.160 (95% confidence interval 1.064 to 1.255, 95% prediction interval 0.872 to 1.447). Smoothed calibration plots showed underestimation of risk at the highest predicted values (eg, predicted risk >40%, fig 6). Supplementary figure 3 displays regional performance metrics.

An estimated 41.33% of the regional variation in the Harrells C index for the competing risks regression model was attributable to inter-regional case mix (table 3); ethnic diversity was the leading sociodemographic factor associated therewith (table 3). For calibration, the I2 from the full meta-regression model was 56.68%, with regional variation in age, deprivation, and ethnic diversity associated therewith. Similar to the Cox model, discrimination tended to decrease with increasing cancer stage (supplementary table 9).

Table 4 summarises the selected hyperparameter configuration for the final XGBoost model. The discrimination of this model appeared acceptable overall,68 albeit lower than for both regression models (table 2; supplementary figure 4), with a meta-analysis pooled Harrells C index of 0.821 (95% confidence interval 0.813 to 0.828, 95% prediction interval 0.805 to 0.837). Pooled calibration metrics suggested some mild systemic miscalibrationfor example, the meta-analysis pooled calibration slope was 1.084 (95% confidence interval 1.003 to 1.165, 95% prediction interval 0.842 to 1.326). Calibration plots showed miscalibration across much of the predicted risk spectrum (fig 6), with overestimation in those with predicted risks <0.4 (most of the individuals) before mixed underestimation and overestimation in the patients at highest risk. Discrimination and calibration were poor for stage IV tumours (see supplementary table 9). Regarding regional variation in performance metrics as a result of differences between regions, most of the variation in calibration was attributable to ethnic diversity, followed by regional differences in age (table 3).

Description of machine learning model architectures and hyperparameters tuning performed

Table 4 summarises the selected hyperparameter configuration for the final neural network. This model performed better than XGBoost for overall discriminationthe meta-analysis pooled Harrells C index was 0.847 (95% confidence interval 0.835 to 0.858, 95% prediction interval 0.816 to 0.878, table 2 and supplementary figure 5). Post-internal-external cross validation pooled estimates of summary calibration metrics suggested no systemic miscalibration overall, such as a calibration slope of 1.037 (95% confidence interval 0.910 to 1.165), but heterogeneity was more noticeable across region, manifesting in the wide 95% prediction interval (slope: 0.624 to 1.451), and smoothed calibration plots showed a complex pattern of miscalibration (fig 6). Meta-regression estimated that the leading factor associated with inter-regional variation in discrimination and calibration metrics was regional differences in ethnic diversity (table 3).

Both the XGBoost and neural network approaches showed erratic calibration across cancer stage groups, especially major miscalibration in stage III and IV tumours, such as a slope for the neural network of 0.126 (95% confidence interval 0.005 to 0.247) in stage IV tumours (see supplementary table 9). Overall decision curves showed that when accounting for competing risks, net benefit was generally better for the regression models, and the neural network had lowest clinical utility; when not accounting for competing risks, the regression models had higher net benefit across the threshold probabilities examined (fig 8). Lastly, the clinical utility of the machine learning models was variable across tumour stages, such as null or negative net benefit compared with the scenarios of treat all for stage IV tumours (see supplementary figure 6).

Decision curves to assess clinical utility (net benefit) of using each model. Top plot accounts for the competing risk of other cause mortality. Bottom plot does not account for competing risks

Table 5 illustrates the predictions obtained using the Cox and competing risks regression models for different sample scenarios. When relevant, these are compared with predictions for the same clinical scenarios from PREDICT Breast and the Adjutorium model (obtained using their web calculators: https://breast.predict.nhs.uk/ and https://adjutorium-breastcancer.herokuapp.com).

Risk predictions from Cox and competing risks regression models developed in this study for illustrative clinical scenarios, compared where relevant with PREDICT and Adjutorium*

This study developed and evaluated four models to estimate 10 year risk of breast cancer death after diagnosis of invasive breast cancer of any stage. Although the regression approaches yielded models that discriminated well and were associated with favourable net benefit overall, the machine learning approaches yielded models that performed less uniformly. For example, the XGBoost and neural network models were associated with negative net benefit at some thresholds in stage I tumours, were miscalibrated in stage III and IV tumours, and exhibited complex miscalibration across the spectrum of predicted risks.

Study strengths include the use of linked primary and secondary healthcare datasets for case ascertainment, identification of clinical diagnoses using accurately coded data, and avoidance of selection and recall biases. Use of centralised national mortality registries was beneficial for ascertainment of the endpoint and competing events. Our methodology enabled the adaptation of machine learning models to handle time-to-event data with competing risks and inclusion of multiple imputation so that all models benefitted from maximal available information, and the internal-external cross validation framework28 permitted robust assessment of model performance and heterogeneity across time, place, and population groups.

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Development and internal-external validation of statistical and machine learning models for breast cancer ... - The BMJ

A new study using machine learning (ML) identified four distinct lupus disease profiles or autoantibody clusters that are predictive of long-term disease, treatment requirements, organ involvement and risk of death. Machine learning refers to the process by which a machine or computer can imitate human behavior to learn and optimize complicated tasks such as statistical analysis and predictive modeling using large datasets. Autoantibodies are antibodies produced by the immune system and directed against proteins in the body. Proteins are often a cause or marker for many autoimmune diseases, including lupus.

Researchers observed 805 people with lupus, looking at demographic, clinical, and laboratory data within 15-months of their diagnosis, then again at 3-years, and 5-years with the disease. After analyzing the data, the researchers used predictive ML which revealed four distinct clusters or lupus disease profiles associated with important lupus outcomes:

Further studies are needed to determine other lupus biomarkers and understand disease pathogenesis through ML approaches. The researchers suggest ML studies can also help to inform diagnosis and treatment strategies for people with lupus. Learn more about lupus research.

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Study Finds Four Predictive Lupus Disease Profiles Using Machine ... - Lupus Foundation of America