ArticleHealth information science and systems2016
Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction.
Article in Health information science and systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis.Bioengineering (Basel, Switzerland) · 2024Article
- Effect of Artificial Intelligence-based Health Education Accurately Linking System (AI-HEALS) for Type 2 diabetes self-management: protocol for a mixed-methods study.BMC public health · 2023Article
- An Assessment of How Clinicians and Staff Members Use a Diabetes Artificial Intelligence Prediction Tool: Mixed Methods Study.JMIR AI · 2023Article
- The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives.Drug design, development and therapy · 2023Article
- Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study.JMIR medical informatics · 2022Article
- Exploration of Black Boxes of Supervised Machine Learning Models: A Demonstration on Development of Predictive Heart Risk Score.Computational intelligence and neuroscience · 2022Article
- Machine learning for diabetes clinical decision support: a review.Advances in computational intelligence · 2022Review
- Ranking Rule-Based Automatic Explanations for Machine Learning Predictions on Asthma Hospital Encounters in Patients With Asthma: Retrospective Cohort Study.JMIR medical informatics · 2021Article
- A Roadmap for Automating Lineage Tracing to Aid Automatically Explaining Machine Learning Predictions for Clinical Decision Support.JMIR medical informatics · 2021Article
- Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis.JMIR research protocols · 2021Article
- Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study.Journal of medical Internet research · 2021Article
- Generalizability of an Automatic Explanation Method for Machine Learning Prediction Results on Asthma-Related Hospital Visits in Patients With Asthma: Quantitative Analysis.Journal of medical Internet research · 2021Article
- Understanding Heart Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models.IEEE open journal of engineering in medicine and biology · 2021Article
- Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients With Asthma: Secondary Analysis.JMIR medical informatics · 2020Article
- Accuracy of individual and combined risk-scale items in the prediction of repetition of self-harm: multicentre prospective cohort study.BJPsych open · 2020Article
- Constructing a knowledge-based heterogeneous information graph for medical health status classification.Health information science and systems · 2020Article
- Classification and prediction of diabetes disease using machine learning paradigm.Health information science and systems · 2020Article
- Developing a Predictive Model for Asthma-Related Hospital Encounters in Patients With Asthma in a Large, Integrated Health Care System: Secondary Analysis.JMIR medical informatics · 2020Article
- Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review.Journal of the American Medical Informatics Association : JAMIA · 2020Article
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Authors and funding
1 author.
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Abstract
backgroundPredictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings.
methodsThis paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy. We did a computer coding implementation of the method. Using the electronic medical record data set from the Practice Fusion diabetes classification competition containing patient records from all 50 states in the United States, we demonstrated the method on predicting type 2 diabetes diagnosis within the next year.
resultsFor the champion machine learning model of the competition, our method explained prediction results for 87.4 % of patients who were correctly predicted by the model to have type 2 diabetes diagnosis within the next year.
conclusionsOur demonstration showed the feasibility of automatically explaining results for any machine learning predictive model without degrading accuracy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.