Introduction

Hepatocellular carcinoma (HCC), the fourth leading cause of cancer-related death worldwide, typically occurs in patients with chronic liver disease and is an aggressive disease with dismal prognosis.1 Over the past decades, improved surveillance programs and imaging techniques have led to early HCC (EHCC) diagnosis in 4050% of patients, at a stage amenable to potentially curative therapiesresection, transplantation or ablation.2,3 Generally, EHCC is expected to have an excellent outcome after radical therapies. Since total hepatectomy eliminates both the diseased liver and the tumor, liver transplantation (LT) offers the highest chance of cure, with a survival up to 70% at 10 years in selected cases, and remains the best treatment for EHCC.4 Unfortunately, the critical shortage of donor organs represents the main limitation of LT and results in long waiting times.

According to clinical practice guidelines, liver resection (LR) is the recommended first-line option for patients with EHCC and preserved liver function, although ablation is an alternative treatment modality.3,5,6 The prognosis following LR may vary even among patients with EHCC and two competing causes of death (tumor recurrence and liver dysfunction) both influence survival.7 Several HCC staging systems have been proposed to pair prognostic prediction with treatment allocation; however, these proposalssuch as Barcelona Clinic Liver Cancer (BCLC) staging, China Liver Cancer (CNLC) staging, Hong Kong Liver Cancer (HKLC) staging and Cancer of the Liver Italian Program (CLIP) scoreare not derived from surgically managed patients, except for the American Joint Committee on Cancer (AJCC) system and Japan Integrated Staging (JIS) score, and therefore exhibit modest prognostic accuracy for resected cases.69 A few prognostic models have been developed based on readily available patient and tumor characteristics; however, they are by nature outmoded and rigid tools because all determinants were examined by conventional statistical methods (ie, Cox proportional hazard regression) and assigned fixed weights.8,10 Hence, new strategies to improve outcome prediction and treatment selection are warranted for EHCC patients.

Machine learning (ML), a subfield of artificial intelligence, leverages algorithmic methods that enable computers to learn from on large-scale, heterogeneous datasets and execute a specific task without predefined rules.11 ML solutions such as gradient boosting machine (GBM) have outperformed regression modelling in a variety of clinical situations (eg, diagnosis and prognosis).1113 Nevertheless, the benefit of ML in predicting prognosis of patients with resected EHCC has yet to be fully explored. Accordingly, we assembled a large, international cohort of EHCC patients to design and evaluate a ML-based model for survival prediction, and compare its performance with existing prognostic systems.

Patients with EHCC, defined as tumor 5 cm and without evidence of extrahepatic disease or major vascular invasion,14 were retrospectively screened from two sources: (1) Medicare patients treated with surgical therapy (LR+LT) in the Surveillance, Epidemiology, and End Results (SEER) Program, a population-based database in the United States, between 2004 and 2015; (2) consecutive patients treated with LR at two high-volume hepatobiliary centers in China (First Affiliated Hospital of Nanjing Medical University and Wuxi Peoples Hospital) between 2006 and 2016. The inclusion criteria were (1) adult patients aged 20 years; (2) histology-confirmed HCC (International Classification of Diseases for Oncology, Third Edition, histology codes 8170 to 8175 for HCC and site code C22.0 for liver);15 (3) complete survival data and a survival of 1 month. The exclusion criteria were (a) missing information on the type of surgical procedure; (b) another malignant primary tumor prior to HCC diagnosis; (c) unknown cause of death. Patient selection process is summarized in the flow chart of Figure 1. This study protocol was approved by the Institution Review Board of First Affiliated Hospital of Nanjing Medical University and Wuxi Peoples Hospital. Written informed consent was waived because retrospective anonymous data were analyzed. Non-identified information was used in order to protect patient data confidentiality. This study was conducted in accordance with the Declaration of Helsinki.

Figure 1 Analytical framework for survival prediction. (A) Flow diagram of the study cohort details. (B) A machine learning pipeline to train, validate and test the model.

The endpoint selected to develop ML-based model was disease-specific survival (DSS), defined as the time from the date of surgery to the date of death from disease (tumor relapse or liver dysfunction). All deaths from any other cause were counted as non-disease-specific and censored at the date of the last follow-up. Follow-up protocol for Chinese cohort included physical examination, laboratory evaluation and dynamic CT or MRI of the chest and abdomen every 3 months during the first 2 years and every 6 months thereafter. The follow-up was terminated on August 15, 2020.

Electronic and paper medical records were reviewed in detail; all pertinent demographic and clinicopathologic data were abstracted on a standardized template. The following characteristics of interest were ascertained at the time of enrollment: age, gender, race, year of diagnosis, alpha-fetoprotein level, use of neoadjuvant therapy, tumor size, tumor number, vascular invasion, histological grade, liver fibrosis score, and type of surgery.

We deployed GBM, a decision tree-based ML algorithm that has gained popularity because of its performance and interpretability, to aggregate baseline risk factors and predict the likelihood of survival using the R package gbm. GBM algorithm16 assembles multiple base learners, in a step-wise fashion, with each successive learner fitting the residuals left over from previous learners to improve model performance: (1) , where is a base learner, typically a decision tree; (2) , where is optimized parameters in each base learner and is the weight of each base learner in the model. Each base learner may have different variables; variables with higher relative importance are utilized in more decision trees and earlier in the boosting algorithm. The model was trained using stratified 33-fold nested cross-validation (3 outer iterations and 3 inner iterations) on the training/validation cohort; a grid search of optimal hyper-parameter settings was run using the R package mlr. Figure 1 shows the ML workflow schematically.

Model discrimination was quantified using Harrells C-statistic and 95% confidence intervals [CIs] were assessed by bootstrapping. Calibration plots were used to assess the model fit. Decision curve analysis was used to determine the clinical net benefit associated with the adoption of the model.17

Differences between groups were tested using 2 test for categorical variables and MannWhitney U-test for continuous variables. Survival probabilities were assessed using the KaplanMeier method and compared by the Log rank test. The optimal cutoffs of GBM predictions were determined to stratify patients at low, intermediate, or high risk for disease-specific death by using X-tile software version 3.6.1 (Yale University School of Medicine, New Haven, CT).18 Propensity score matching (PSM) was used to balance the LR versus LT for EHCC in SEER cohort using 1:1 nearest neighbor matching with a fixed caliper width of 0.02. Cases (LR) and controls (LT) were matched on all baseline characteristics other than type of surgery using the R package MatchIt. All analyses were conducted using R software version 3.4.4 (www.r-project.org). Statistical significance was set at P<0.05; all tests were two-sided.

A total of 2778 EHCC patients (2082 males and 696 females; median age, 60 years; interquartile range [IQR], 5467 years) treated with LR were identified and divided into 1899 for the training/validation (SEER) cohort and 879 for the test (Chinese) cohort. Patient characteristics of the training/validation and test cohorts are summarized in Table 1. There were 625 disease-related deaths recorded (censored, 67.1%) during a median (IQR) follow-up time of 44.0 (26.074.0) months in the SEER cohort, and 258 deaths were recorded (censored, 70.6%) during a median (IQR) follow-up of 52.5 (35.876.0) months in the Chinese cohort. Baseline characteristics and post-resection survival differed between the cohorts.

Table 1 Baseline Characteristics in the Training/Validation and Test Cohorts

We investigated 12 potential model covariates using GBM algorithm. According to the results of nested cross-validation, we utilized 2000 decision trees sequentially, with at least 5 observations in the terminal nodes of the trees; the decision tree depth was optimized at 3, corresponding to 3-way interactions, and the learning rate was optimized at 0.01. Covariates with a relative influence greater than 5 (age, race, alpha-fetoprotein level, tumor size, multifocality, vascular invasion, histological grade and fibrosis score) were integrated into the final model developed to predict DSS (Figure 2A and B).

Figure 2 Overview of the machine-learning-based model. (A) Relative importance of the variables included in the model. (B) Illustrative example of the gradient boosting machine (GBM). GBM builds the model by combining predictions from stumps of massive decision-tree-base-learners in a step-wise fashion. GBM output is calculated by adding up the predictions attached to the terminal nodes of all 2000 decision trees where the patient traverses. (C) Performance of GBM model as compared with that of American Joint Committee on Cancer (AJCC) staging in the internal validation group. (D) Online model deployment based on GBM output.

The final GBM model demonstrated good discriminatory ability in predicting post-resection survival specific for EHCC, with a C-statistic of 0.738 (95% CI 0.7170.758), and outperformed the 7th and 8th edition of AJCC staging systems (P<0.001) in the training/validation cohort (Table 2). The internal validation group was the 33-fold nested cross-validation of the final model of the training cohort with 211 patients in each fold. For the composite outcome, the GBM model yielded a median C-statistic of 0.727 (95% CI 0.7060.761) and performed better than AJCC staging systems (P<0.05) in the internal validation group (Figure 2C). In the test cohort, the GBM model provided a C-statistic of 0.721 (95% CI, 0.6890.752) in predicting DSS after resection of EHCC and was clearly superior to AJCC, BCLC, CNLC, HKLC, CLIP and JIS systems (P<0.05). Note that prediction scores differed between training/validation and test sets (P<0.001) (Figure S1). The discriminatory performance of ML-based model exceeded those of AJCC staging systems even in sub-cohorts stratified by covariate integrity (complete/missing) (Table S1). Furthermore, the GBM model exhibited greater ability to discriminate survival probabilities than simple prognostic strategies, such as multifocal EHCC with vascular invasion indicating a dismal prognosis following LR, in sub-cohorts with complete strategy-related information (P<0.001) (Table S2).

Table 2 Performance of GBM Model and Staging Systems

Calibration plots presented excellent agreement between model predicted and actual observed survival in both the training/validation and test cohorts (Figure S2A and B). Decision curve analysis demonstrated that the GBM model provided better clinical utility for EHCC in designing clinical trials than the treat all or treat none strategy across the majority of the range of reasonable threshold probabilities (Figure S2C and D). The model is publicly accessible for use on Github (https://github.com/radgrady/EHCC_GBM), with an app (https://mlehcc.shinyapps.io/EHCC_App/) that allows survival estimates at individual scale (Figure 2D).

We utilized X-tile analysis to generate two optimal cut-off values (6.35 and 5.32 in GBM predictions, Figure S3) that separated EHCC patients into 3 strata with a highly different probability of post-resection survival in the training/validation cohort: low risk (760 [40.0%]; 10-year DSS, 75.6%), intermediate risk (948 [49.9%]; 10-year DSS, 41.8%), and high risk (191 [10.1%]; 10-year DSS, 5.7%) (P<0.001). In the test cohort, the aforementioned 3 prognostic strata by using the GBM model were confirmed: low risk (634 [72.1%]; 10-year DSS, 69.0%), intermediate risk (194 [22.1%]; 10-year DSS, 37.9%), and high risk (51 [5.8%]; 10-year DSS, 4.7%) (P<0.001) (Table 3). Visual inspection of the survival curves again revealed that, compared with the 8th edition AJCC criteria, the GBM model provided better prognostic stratification in both the training/validation and test cohorts (Figure 3). Differences in the baseline patient characteristics according to risk groups defined by the GBM model are summarized in Table S3.

Table 3 Disease-Specific Survival According to Risk Stratification

Figure 3 Kaplan-Meier survival plots demonstrating disparities between groups. Disease-specific survival stratified by the 8th edition of the American Joint Committee on Cancer T stage and the machine-learning model in the training/validation (A and C) and the test (B and D) cohort.

We also gathered data of 2124 EHCC patients (1671 males and 453 females; median age, 58 years; IQR, 5362 years) treated with LT from the SEER-Medicare database. SEER data demonstrated that considerable differences existed between LR (n=1899) and LT (n=2124) cohorts in terms of all listed clinical variables except for alpha-fetoprotein level (Table S4). Upon initial analysis, we found a remarkable survival benefit of LT over LR for patients with EHCC (hazard ratio [HR] 0.342, 95% CI 0.3000.389, P<0.001), which was further confirmed in a well-matched cohort of 1892 patients produced by PSM (HR 0.342, 95% CI 0.2850.410, P<0.001). Although a trend for higher survival probability was observed after 5 years in the LT cohort, no statistically significant difference in DSS was observed when compared with low-risk LR cohort (HR 0.850, 95% CI 0.6791.064, P=0.138). After PSM, 420 patients in the LT cohort were matched to 420 patients in the low-risk LR cohort; the trend for improved survival remained after 5 years in the matched LT cohort while the matched comparison also yielded no significant survival difference (HR 0.802, 95% CI 0.5611.145, P=0.226) (Figure 4). By contrast, when compared with intermediate-and high-risk patients treated with LR, remarkable survival benefits were observed in patients treated with LT both before and after PSM (P<0.001) (Table S5).

Figure 4 Comparison of survival after resection versus transplantation before and after propensity score matching in SEER-Medicare database. (A) KaplanMeier curves for different risk groups stratified by the model in the SEER resection cohort (n=1899) and patients in the SEER transplantation cohort (n=2124). (B) KaplanMeier curves for low-risk patients treated with resection and patients treated with transplantation in propensity score-matched cohort (n=840).

In this study involving over 2700 EHCC patients treated with resection, a gradient-boosting ML model was trained, validated and tested to predict post-resection survival. Our results demonstrated that this ML model utilized readily available clinical information, such as age, race, alpha-fetoprotein level, tumor size and number, vascular invasion, histological grade and fibrosis score, and provided real-time, accurate prognosis prediction (C-statistic >0.72) that outperform traditional staging systems. Among the model covariates, tumor-related characteristics, such as size, multifocality and vascular invasion, as well as liver cirrhosis are known risk factors for poor survival following resection of HCC.710 Besides, multiple population-based studies have shown the racial and age differences in survival of HCC.19,20 Therefore, our ML model is a valid and reliable tool to estimate prognosis of EHCC patients. This study represents, to our knowledge, the first application of a state-of-the-art ML survival prediction algorithm in EHCC based on large-scale, heterogeneous datasets.

In SEER cohort, the 10-year survival rate of EHCC after LR was around 50%, which seemed acceptable but was remarkably lower than that after LT (around 80%). No adjuvant therapies are able to prevent tumor relapse and cirrhosis progression; however, patients with dismal prognosis should be considered candidates for clinical trials of adjuvant therapy.7 Salvage LT has also been a highly applicable strategy to alleviate both graft shortage and waitlist dropout with excellent outcomes that are comparable to upfront LT.1,5 Priority policy, defined as enlistment of patients at high mortality risk before disease progression, was then implemented to improve the transplantability rate.21 Promisingly, our ML tool may help clinicians better identify EHCC patients who are at high risk of disease-related death, engage in clinical trials, and meet priority enlistment policy. Specifically, the GBM model identified 10% of EHCC patients who suffered from extremely dismal prognosis following LR in this study. Given its small proportion and survival benefit, we advocate the pre-emptive enlistment of high-risk subset for salvage LT after LR to avoid the later emergence of advanced disease (ie, tumor recurrence and liver decompensation) ultimately leading to death. Moreover, 40% of EHCC patients were at intermediate risk of disease-related death; adjuvant treatments that target HCC and cirrhosis are desirable. In turn, nearly half of EHCC patients were categorized as low risk by using the GBM model. The low-risk subset permits satisfactory long-term survival after LR and may receive no adjuvant therapy. We note that DSS curves are separated after 5 years for low-risk patients treated with LR as compared with patients treated with upfront LT, and thus long-lasting surveillance should be maintained.

Prior efforts to improve prognostic prediction of EHCC have mostly been reliant on tissue-based or imaging-assisted quantification of research biomarkers.9,22 However, a more accurate, yet more complex, prognosis estimate does not necessarily present a better clinical tool. Parametric regression models are ubiquitous in clinical research because of their simplicity and interpretability; however, regression analysis should be performed in complete cases only.23 Moreover, regression modeling strategies assume that relationships among input variables are linear and homogeneous but complicated interactions exist between predictors.24,25 Decision tree-based methods represent a large family of ML algorithms and can reveal complex non-linear relationships between covariates. GBM algorithm has been widely applied in big data analysis and consistently utilized by the top performers of ML predictive modelling competitions.14,26 GBM algorithm utilizes the boosting procedure to combine stumps of massive decision-tree-base-learners, which is similar to the clinical decision-making process for a patient by aggregating consultations from multiple specialists, each which would that look at the case in a slightly different way. Thus, our GBM model directly integrates interpretability in order to mitigate this issue. Compared with other tree-based ensemble methods such as random forest, GBM algorithm also has a built-in functionality to handle missing values that permits utilizing data from, and assigning classification to, all observations in the cohort without the need to impute data. We applied nested cross-validation scheme for hyperparameter tuning in GBM as it prevents information leaking between observations used for training and validating the model, and estimates the external test error of the given algorithm on unseen datasets more accurately by averaging its performance metrics across folds.27 Comparable discriminatory ability in the training/validation cohort, the test cohort as well as sub-cohorts from different clinical scenarios suggested good reproducibility and reliability of the proposed GBM model.

Our study has several limitations that warrant attention. First, all the presented analyses are retrospective; prospective validations of the ML model in different populations are warranted prior to routine use in clinical practice. Second, the study cohort included population-based cancer registries with limited information regarding patient and tumor characteristics; unavailable confounders, such as biochemical parameters, surgical margin status and recurrence treatment modality could not be adjusted for modeling. Third, SEER-Medicare database contains a considerable amount of missing data in several important clinical variables, such as fibrosis score. Indeed, missing data represent an unavoidable feature of all clinical and population-based databases; however, improper management of data resource, such as simply excluding cases with missing data, can introduce considerable bias, as previously noted across numerous cancer types.28 We therefore contend that integrating missingness into our GBM model indicates good transferability in future clinical practice.

In conclusion, ML approach is both feasible and accurate, and a novel way to consider analysis of survival outcomes in clinical scenarios. Our results suggest that a GBM model trained on readily-available clinical data provides good performance that is better than staging systems in predicting prognosis. Although several issues must be addressed, such as prospective validations and ethical challenges, prior to its widespread use, such an automated tool may complement existing prognostic sources and lead to better personalized treatments for patients with resected EHCC.

EHCC, early hepatocellular carcinoma; LT, liver transplantation; LR, liver resection; BCLC, Barcelona Clinic Liver Cancer; China Liver Cancer, CNLC; HKLC, Hong Kong Liver Cancer; CLIP, Cancer of the Liver Italian Program; AJCC, American Joint Committee on Cancer; ML, machine learning; GBM, gradient boosting machine; SEER, Surveillance, Epidemiology, and End Results; DSS, disease-specific survival; PSM, propensity score matching; IQR, interquartile range.

Data for model training and validation as well as R codes are available at Github (https://github.com/radgrady/EHCC_GBM). Test data are available from the corresponding author (Xue-Hao Wang) on reasonable request.

This study protocol was approved by the Institution Review Board of First Affiliated Hospital of Nanjing Medical University and Wuxi Peoples Hospital. Written informed consent was waived because retrospective anonymous data were analyzed. Non-identified information was used in order to protect patient data confidentiality.

This study was supported by the Key Program of the National Natural Science Foundation of China (31930020) and the National Natural Science Foundation of China (81530048, 81470901, 81670570).

The authors declare no potential conflicts of interest.

1. Yang JD, Hainaut P, Gores GJ, Amadou A, Plymoth A, Roberts LR. A global view of hepatocellular carcinoma: trends, risk, prevention and management. Nat Rev Gastroenterol Hepatol. 2019;16(10):589604. doi:10.1038/s41575-019-0186-y

2. Llovet JM, Montal R, Sia D, Finn RS. Molecular therapies and precision medicine for hepatocellular carcinoma. Nat Rev Clin Oncol. 2018;15(10):599616. doi:10.1038/s41571-018-0073-4

3. European Association for the Study of the Liver. EASL clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol. 2018;69(1):182236. doi:10.1016/j.jhep.2018.03.019

4. Pinna AD, Yang T, Mazzaferro V, et al. Liver transplantation and hepatic resection can achieve cure for hepatocellular carcinoma. Ann Surg. 2018;268(5):868875. doi:10.1097/SLA.0000000000002889

5. Marrero JA, Kulik LM, Sirlin CB, et al. Diagnosis, staging, and management of hepatocellular carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology. 2018;68(2):723750. doi:10.1002/hep.29913

6. Zhou J, Sun H, Wang Z, et al. Guidelines for the diagnosis and treatment of hepatocellular carcinoma (2019 Edition). Liver Cancer. 2020;9(6):682720. doi:10.1159/000509424

7. Villanueva A. Hepatocellular carcinoma. N Engl J Med. 2019;380(15):14501462. doi:10.1056/NEJMra1713263

8. Chan AWH, Zhong J, Berhane S, et al. Development of pre and post-operative models to predict early recurrence of hepatocellular carcinoma after surgical resection. J Hepatol. 2018;69(6):12841293. doi:10.1016/j.jhep.2018.08.027

9. Ji GW, Zhu FP, Xu Q, et al. Radiomic features at contrast-enhanced CT predict recurrence in early stage hepatocellular carcinoma: a Multi-Institutional Study. Radiology. 2020;294(3):568579. doi:10.1148/radiol.2020191470

10. Shim JH, Jun MJ, Han S, et al. Prognostic nomograms for prediction of recurrence and survival after curative liver resection for hepatocellular carcinoma. Ann Surg. 2015;261(5):939946. doi:10.1097/SLA.0000000000000747

11. Deo RC. Machine learning in medicine. Circulation. 2015;132(20):19201930. doi:10.1161/CIRCULATIONAHA.115.001593

12. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):13471358. doi:10.1056/NEJMra1814259

13. Eaton JE, Vesterhus M, McCauley BM, et al. Primary sclerosing cholangitis risk estimate tool (PREsTo) predicts outcomes of the disease: a derivation and validation study using machine learning. Hepatology. 2020;71(1):214224. doi:10.1002/hep.30085

14. Nathan H, Hyder O, Mayo SC, et al. Surgical therapy for early hepatocellular carcinoma in the modern era: a 10-year SEER-medicare analysis. Ann Surg. 2013;258(6):10221027. doi:10.1097/SLA.0b013e31827da749

15. Fritz AG. International Classification of Diseases for Oncology: ICD-O. 3. Geneva, Switzerland: World Health Organization; 2000.

16. Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29(5):11891232. doi:10.1214/aos/1013203451

17. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26(6):565574. doi:10.1177/0272989X06295361

18. Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004;10(21):72527259. doi:10.1158/1078-0432.CCR-04-0713

19. Altekruse SF, Henley SJ, Cucinelli JE, McGlynn KA. Changing hepatocellular carcinoma incidence and liver cancer mortality rates in the United States. Am J Gastroenterol. 2014;109(4):542553. doi:10.1038/ajg.2014.11

20. Dasari BV, Kamarajah SK, Hodson J, et al. Development and validation of a risk score to predict the overall survival following surgical resection of hepatocellular carcinoma in non-cirrhotic liver. HPB (Oxford). 2020;22(3):383390. doi:10.1016/j.hpb.2019.07.007

21. Ferrer-Fbrega J, Forner A, Liccioni A, et al. Prospective validation of ab initio liver transplantation in hepatocellular carcinoma upon detection of risk factors for recurrence after resection. Hepatology. 2016;63(3):839849. doi:10.1002/hep.28339

22. Qiu J, Peng B, Tang Y, et al. CpG methylation signature predicts recurrence in early-stage hepatocellular carcinoma: results from a Multicenter Study. J Clin Oncol. 2017;35(7):734742. doi:10.1200/JCO.2016.68.2153

23. Sterne JA, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393. doi:10.1136/bmj.b2393

24. Loftus TJ, Tighe PJ, Filiberto AC, et al. Artificial intelligence and surgical decision-making. JAMA Surg. 2020;155(2):148158. doi:10.1001/jamasurg.2019.4917

25. Shindoh J, Andreou A, Aloia TA, et al. Microvascular invasion does not predict long-term survival in hepatocellular carcinoma up to 2 cm: reappraisal of the staging system for solitary tumors. Ann Surg Oncol. 2013;20(4):12231229. doi:10.1245/s10434-012-2739-y

26. Bibault JE, Chang DT, Xing L. Development and validation of a model to predict survival in colorectal cancer using a gradient-boosted machine. Gut. 2021;70(5):884889. doi:10.1136/gutjnl-2020-321799

27. Maros ME, Capper D, Jones DTW, et al. Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. Nat Protoc. 2020;15(2):479512. doi:10.1038/s41596-019-0251-6

28. Jeong CW, Washington SL 3rd, Herlemann A, Gomez SL, Carroll PR, Cooperberg MR. The new surveillance, epidemiology, and end results prostate with watchful waiting database: opportunities and limitations. Eur Urol. 2020;78(3):335344. doi:10.1016/j.eururo.2020.01.009

See the rest here:
Machine learning to improve prognosis prediction of EHCC | JHC - Dove Medical Press

by Sanjeev Mervana

Northampton, MA --News Direct-- Cisco Systems Inc.

Now that the Cisco Global Problem Solver Challenge 2021 winners have been officially announced, we are excited for you to learn more about each winning team and the story behind each innovation. The Cisco Global Problem Solver Challenge is an annual competition that awards cash prizes to early-stage tech entrepreneurs solving the worlds toughest problems. Now in its fifth year, the competition awarded its largest prize pool ever, $1 million USD, to 20 winning teams from around the world.

Last December, we announced that the 2021 Challenge would include a special Ethical Artificial Intelligence (AI) Prize. The $50,000 USD Ethical AI Prize was awarded to a startup designing AI in an inherently ethical manner, so that the solution addresses social, environmental, or technological challenges. This award is being offered by Ciscos Emerging Technologies and Incubation (ET&I) Group in the spirit of our purpose to Power an Inclusive Future for All. The partnership between the Cisco Global Problem Solver Challenge and ET&I is symbolic of our commitment and investment in the corporate social responsibility space and our passion to be at the forefront of innovation.

This years Ethical AI winning solution, .lumen, is focused on the same goals as our ET&I team, a commitment to and investment in the artificial intelligence and corporate social responsibility spaces. .lumen builds technological glasses that replicate the main advantages of a guide dog, and then some, in a package that can easily be replicated, maintained, and used. The solutions AI and machine learning models will provide enhanced mobility and opportunities for people who are blind.

Our ET&I team sat down with .lumens Cornel Amariei, CEO, to discuss their innovative product, their inspiration, and how winning the prize will impact their business.

What problem is your technology solution trying to solve?

Story continues

Cornel: Right now, if you look at the world, there are 40 million blind people. This number will increase to 100 million by 2050. Despite all the technological advancements you only have solutions allowing a blind person to read text or use a smartphone or laptop. And, you had the exact same two solutions for mobility and orientation for thousands of years the white cane and the guide dog.

Now, the guide dog is very interesting. Its features are unanimously seen as helpful, but it has a few drawbacks. It costs between $30-$60K to train a single guide dog and thats not even the biggest problem. By far, the biggest problem is that a blind person cant easily take care of a guide dog. And because of this, there are only 28,000 guide dogs to 40 million blind people. So the most advanced mobility solution, and the most useful one, is not a scalable one.

We realized that today, we could mimic the main functionality of a guide dog without the drawbacks and create a system for the other 39,972,000 people.

Can you explain how the solution works?

Cornel: Ill start with an analogy. If you have a guide dog, you can ask it to take you to the door, to an empty seat, or back home if it knows the route. It will do so by pulling your hand, moving you away from obstacles as you go. You can ask the .lumen glasses the same thing, using a voice command. But rather than pulling your hand, they will pull your head.

We have a patented system, which we evaluated with over 200 blind individuals. It very intuitively uses haptic and auditory feedback to guide a person very precisely avoiding obstacles, avoiding dangerous situations to keep you on track. And its as easy as someone pulling your hand in the direction you need to travel.

On a more technical level, we basically took technology from autonomous driving and robotics, and we scaled it down to something you can wear on your head. Its an immense trick of engineering, because you have so much space and available weight for sensors, batteries, and computers in an autonomous driving car.

In our solution, we only have a few hundred grams or roughly 20 ounces. The system has five cameras. We use artificial intelligence to understand the situation, to understand how you can interact with the world, and where it is safe to travel and where it is not.

We, as visually capable individuals, take these things for granted without realizing how complex they are. Our headset operates in our custom hardware and software system and its an incredible success that were getting close to what a guide dog can do.

What inspired you to develop this solution?

Cornel: The company is just under one and a half years old so its a pandemic startup. I left my job as the Head of Innovation at a very large automotive corporation to start .lumen. My motivation and inspiration actually came from my family. I was born in a family where both of my parents have locomotive disabilities, and my sister has cerebral disabilities. So I grew up being the only member in the family that does not have a disability. This made me very sensitive to the experiences of persons with disabilities and really made me realize how assistive technology can help and how little is being done.

Then, being an engineer, scientist, manager, and someone who has built other companies, along with my family experience, allowed me to put it all together. And day-by-day, were working with over 200 blind individuals to develop and test these products. Were very fortunate that way.

How will winning a prize in the Cisco Global Problem Solver Challenge help you advance your business?

Cornel: First of all, what were trying to do at .lumen is incredibly hard. And we need all the support in the world. Any type of support, especially what were getting now from Cisco the prize, the mentorship, and everything were receiving definitely helps us focus on what we have to do and improve our testing efforts. With everything we do, we test, and most of the money goes there.

Do you know what you will use the prize money for specifically?

Cornel: We already allocated the budget for it even before we had the money to product manufacturing. We are beginning manufacturing soon, starting with 200 units, and have a global expansion plan next year. And in 2022, well make an additional 10,000 units.

How has the global pandemic impacted your work?

Cornel: Thankfully, there are many things you can do remotely. Quite many more things than I would have said would have been possible two years ago. Now, COVID has shown me how many things can be remote.

But there are things which you cannot. For example, you can develop hardware remotely, but its not easy. And you cant really prototype it that way. I expected that to be the biggest challenge, but we took note of that. The biggest challenge by far was testing, because we must get qualitative and quantitative feedback from over 200 people. They have to get our devices. We have to track how they interact with them, etc.

In order to test in-person during COVID, during lockdowns forgetting about the tremendous legal hassles, the risk that we took was incredible. Under very strictly controlled conditions, for every hour of testing, we had over 100 pages of procedures. We have hundreds if not thousands of pages of procedures as a result.

Some of our testers had COVID. They didnt know it, and we found out after the fact. And still, we never got it. We basically created a sterile hospital environment in our office. It was the only way we could test the system.

Why did you decide to start your own social enterprise versus going to work for a company?

Cornel: I was a university student back in 2014 when we first got the idea for .lumen. We actually built some prototypes back then to test them with blind individuals, and we did so. But it was quite clear to us that the technology was not where it needed to be. So knowing that we couldnt deliver it then, we took it upon ourselves to wait for the moment technology would catch up with our vision and our idea and thats the moment were going to start. That moment was 2020. We anticipated that in 2019 and said, Ok, if this is the shot. The technology is here lets start.

My cofounders and I put in our money and started the company. One year ago, we were nine people. Today, were a team of 40 passionate individuals. Next year, were projected to expand to 100.

What advice do you have for other social entrepreneurs?

Cornel: Theres one thing we learned from .lumen, which is: make meaning, not money. The problem in the world, not just with startups, is that people prioritize money, and then find a solution. Afterward, they find a problem for which the solution works. It should be the other way. You see a problem and really understand it, and then you come up with a solution that fits. Its only purpose should be to help. The moment you really make that work; the moment you really help thats when you make money.

Thats what really worked for us because we created .lumen with only one purpose: helping.

View additional multimedia and more ESG storytelling from Cisco Systems Inc. on 3blmedia.com

View source version on newsdirect.com: https://newsdirect.com/news/lumen-meet-the-team-empowering-millions-of-people-who-are-blind-through-ai-and-machine-learning-models-857172229

Link:
.lumen: Meet the Team Empowering Millions of People Who Are Blind Through AI and Machine Learning Models - Yahoo Finance

This article was originally published here

Sci Rep. 2021 Aug 13;11(1):16522. doi: 10.1038/s41598-021-95919-2.

ABSTRACT

Inflammatory bowel diseases (IBD), namely Crohns disease (CD) and ulcerative colitis (UC) are chronic inflammation within the gastrointestinal tract. IBD patient conditions and treatments, such as with immunosuppressants, may result in a higher risk of viral and bacterial infection and more severe outcomes of infections. The effect of the clinical and demographic factors on the prognosis of COVID-19 among IBD patients is still a significant area of investigation. The lack of available data on a large set of COVID-19 infected IBD patients has hindered progress. To circumvent this lack of large patient data, we present a random sampling approach to generate clinical COVID-19 outcomes (outpatient management, hospitalized and recovered, and hospitalized and deceased) on 20,000 IBD patients modeled on reported summary statistics obtained from the Surveillance Epidemiology of Coronavirus Under Research Exclusion (SECURE-IBD), an international database to monitor and report on outcomes of COVID-19 occurring in IBD patients. We apply machine learning approaches to perform a comprehensive analysis of the primary and secondary covariates to predict COVID-19 outcome in IBD patients. Our analysis reveals that age, medication usage and the number of comorbidities are the primary covariates, while IBD severity, smoking history, gender and IBD subtype (CD or UC) are key secondary features. In particular, elderly male patients with ulcerative colitis, several preexisting conditions, and who smoke comprise a highly vulnerable IBD population. Moreover, treatment with 5-ASAs (sulfasalazine/mesalamine) shows a high association with COVID-19/IBD mortality. Supervised machine learning that considers age, number of comorbidities and medication usage can predict COVID-19/IBD outcomes with approximately 70% accuracy. We explore the challenge of drawing demographic inferences from existing COVID-19/IBD data. Overall, there are fewer IBD case reports from US states with poor health ranking hindering these analyses. Generation of patient characteristics based on known summary statistics allows for increased power to detect IBD factors leading to variable COVID-19 outcomes. There is under-reporting of COVID-19 in IBD patients from US states with poor health ranking, underpinning the perils of using the repository to derive demographic information.

PMID:34389789 | DOI:10.1038/s41598-021-95919-2

Continue reading here:
A machine learning approach identifies 5-ASA and ulcerative colitis as being linked with higher COVID-19 mortality in patients with IBD - DocWire News

Harvard University researchers will take leading roles in a new National Science Foundation (NSF) artificial intelligence research institute housed at the University of Washington (UW). The UW-led AI Institute for Dynamic Systems is among 11 new AI research institutes announced today by the NSF.

Na Li, the Gordon McKay Professor of Electrical Engineering and Applied Mathematics at the Harvard John A. Paulson School of Engineering and Applied Science (SEAS), is a co-principal investigator at the institute and will lead one of the main research thrusts. Michael Brenner, the Michael F. Cronin Professor of Applied Mathematics and Applied Physics and Professor of Physics at SEAS, and Lucas Janson, Assistant Professor of Statistics and Affiliate in Computer Science will also be part of the institutes research team.

The AI Institute for Dynamic Systems will focus on fundamental AI and machine learning theory, algorithms and applications for real-time learning and control of complex dynamic systems, which describe chaotic situations where conditions are constantly shifting and hard to predict. In addition to research, the institute will be focused on training future researchers in this field throughout the education pipeline.

"The engineering sciences are undergoing a revolution that is aided by machine learning and AI algorithms," said institute director J. Nathan Kutz, a UW professor of applied mathematics. "This institute brings together a world-class team of engineers, scientists and mathematicians who aim to integrate fundamental developments in AI with applications in critical and emerging technological applications."

The overall goal of this institute is to integrate physics-based models with AI and machine learning approaches to develop data-enabled efficient and explainable solutions for challenges across science and engineering. The research will be divided into three main thrusts: modeling, control and optimization and sensors.

Li will lead the control research thrust. Li, along with Janson and the rest of the control research team, will leverage the successes of machine learning towards the control of modern complex dynamical systems. Specifically, they will be focused on several challenges pertaining to reinforcement learning (RL), a class of machine learning that addresses the problem of learning to control physical systems by explicitly considering their inherent dynamical structure and feedback loop.

The AI for control team will figure out how to develop scalable learning-based control methods for large-scale dynamical systems; maintain the performance of the learned policies even when there is a model class mismatch; and guarantee that the systems maintain stability and stay within a safety constraint while still learning efficiently.

To date, the successes of RL have been limited to very structured or simulated environments, said Li. Applying RL to real-world systems, like energy systems, advanced manufacturing, and robot autonomy, faces many critical challenges such as scalability, robustness, safety, to name a few. We will develop critically enabling mathematical, computational, and engineering architectures to overcome these challenges to bring the success of AI/ML to our real-world systems.

"One particular focus of ours will be to quantify the statistical uncertainty of what AI learns, enabling us to develop algorithms with rigorous safeguards that prevent them from harming anyone or anything while they explore and learn from their environment," said Janson.

Brenner, a leader in the field of physics-informed machine-learning methods for complex systems, will be part of the AI for modeling research team. That team will be focused on learning physically interpretable models of dynamical systems from off-line and/or on-line streaming data.

"Our research will explore how we can develop better machine-learning technologies by baking in and enforcing known physics, such as conservation laws, symmetries, etc.," said institute associate director Steve Brunton, a UW associate professor of mechanical engineering. "Similarly, in complex systems where we only have partially known or unknown physics such as neuroscience or epidemiology can we use machine learning to learn the 'physics' of these systems?"

Harvard is among several partner institutions including the University of Hawaii at Mnoa, Montana State University, the University of Nevada Reno, Boise State University, the University of Alaska Anchorage, Portland State University and Columbia University. The institute will also partner with high school programs that focus on AI-related projects and creating a post-baccalaureate program that will actively recruit and support recent college graduates from underrepresented groups, United States veterans and first-generation college students with the goal of helping them attend graduate school. The institute will receive about $20 million over five years.

Continue reading here:
Harvard researchers part of new NSF AI research institute - Harvard School of Engineering and Applied Sciences

This photo provided by LG CNS Co. on July 29, 2021, shows LG CNS workers showing Google Clouds Machine Learning Specialization distinction and Tensorflow Developer certificate.

SEOUL, July 29 (Korea Bizwire) LG CNS Co., a major IT service provider under LG Group, said Thursday it has earned a Machine Learning Specialization distinction from Google Cloud as the company strives to expand its presence in the artificial intelligence (AI) sector.

LG CNS said it became the first South Korean company to achieve machine learning in the Google Cloud Partnership Advantage program for its expertise in the sector.

Google Cloud has 17 types of specialization certification programs that are rewarded to its partner companies that prove their specialty in certain technology sectors.

To earn a Machine Learning Specialization distinction, a company has to meet Google Clouds requirements in 33 categories across six fields, in which a firm is assessed on areas ranging from machine learning models to investment plans.

Leveraging Google Clouds technology, LG CNS said it has established AI-powered services for LG Electronics Inc. and AEON Corp., one of Japans largest chains of language learning institutions.

Seven LG CNS professional machine learning engineers were certified by Google, and some 170 of its workers were also recognized with Tensorflow Developer Certificates from the company.

To beef up its AI capabilities, LG CNS said it has 35 working teams dedicated to its AI business.

(Yonhap)

Go here to see the original:
LG CNS Recognized by Google Cloud with Machine Learning Specialization - The Korea Bizwire