1
|
Mishra AK, Chong B, Arunachalam SP, Oberg AL, Majumder S. Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment. Am J Gastroenterol 2024; 119:1466-1482. [PMID: 38752654 PMCID: PMC11296923 DOI: 10.14309/ajg.0000000000002870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 05/06/2024] [Indexed: 06/20/2024]
Abstract
INTRODUCTION Accurate risk prediction can facilitate screening and early detection of pancreatic cancer (PC). We conducted a systematic review to critically evaluate effectiveness of machine learning (ML) and artificial intelligence (AI) techniques applied to electronic health records (EHR) for PC risk prediction. METHODS Ovid MEDLINE(R), Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science were searched for articles that utilized ML/AI techniques to predict PC, published between January 1, 2012, and February 1, 2024. Study selection and data extraction were conducted by 2 independent reviewers. Critical appraisal and data extraction were performed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Risk of bias and applicability were examined using prediction model risk of bias assessment tool. RESULTS Thirty studies including 169,149 PC cases were identified. Logistic regression was the most frequent modeling method. Twenty studies utilized a curated set of known PC risk predictors or those identified by clinical experts. ML model discrimination performance (C-index) ranged from 0.57 to 1.0. Missing data were underreported, and most studies did not implement explainable-AI techniques or report exclusion time intervals. DISCUSSION AI/ML models for PC risk prediction using known risk factors perform reasonably well and may have near-term applications in identifying cohorts for targeted PC screening if validated in real-world data sets. The combined use of structured and unstructured EHR data using emerging AI models while incorporating explainable-AI techniques has the potential to identify novel PC risk factors, and this approach merits further study.
Collapse
Affiliation(s)
- Anup Kumar Mishra
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA
| | - Bradford Chong
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA
| | | | - Ann L. Oberg
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Shounak Majumder
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA
| |
Collapse
|
2
|
Hien NTK, Tsai FJ, Chang YH, Burton W, Phuc PT, Nguyen PA, Harnod D, Lam CSK, Lu TC, Chen CI, Hsu MH, Lu CY, Huang CW, Yang HC, Hsu JC. Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases. Front Med (Lausanne) 2024; 10:1289968. [PMID: 38249981 PMCID: PMC10797111 DOI: 10.3389/fmed.2023.1289968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024] Open
Abstract
Background Previous studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods. Objective This study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19. Methods Data were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity. Results The study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902. Conclusion This study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.
Collapse
Affiliation(s)
- Nguyen Thi Kim Hien
- Master Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Feng-Jen Tsai
- Master Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan
- Ph.D. Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Yu-Hui Chang
- PharmD Program, Division of Clinical Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | - Whitney Burton
- International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Phan Thanh Phuc
- International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Phung-Anh Nguyen
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- Research Center of Health Care Industry Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Dorji Harnod
- Department of Emergency, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Emergency and Critical Care Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Carlos Shu-Kei Lam
- Department of Emergency, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Division of Emergency, Department of Emergency and Critical Care Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Tsung-Chien Lu
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chang-I Chen
- Department of Healthcare Administration, School of Management, Taipei Medical University, Taipei, Taiwan
| | - Min-Huei Hsu
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Christine Y. Lu
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, United States
- School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- Kolling Institute, Faculty of Medicine and Health, The University of Sydney and the Northern Sydney Local Health District, Sydney, NSW, Australia
| | - Chih-Wei Huang
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan
| | - Hsuan-Chia Yang
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Research Center of Big Data and Meta-analysis, Wanfang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Jason C. Hsu
- International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- Research Center of Health Care Industry Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
| |
Collapse
|