Testing the Generalizability of an Automated Method for Explaining Machine Learning Predictions on Asthma Patients' Asthma Hospital Visits to an Academic Healthcare System
| Autor: | Gang Luo, Amanda I. Messinger, Yao Tong |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Psychological intervention Machine learning computer.software_genre Preventive care Article 03 medical and health sciences 0302 clinical medicine Health care medicine General Materials Science Generalizability theory 030212 general & internal medicine Interpretability Asthma business.industry 030503 health policy & services General Engineering medicine.disease Artificial intelligence 0305 other medical science business Psychology computer Automated method Healthcare system |
| Zdroj: | IEEE Access |
| ISSN: | 2169-3536 |
| Popis: | Asthma puts a tremendous overhead on healthcare. To enable effective preventive care to improve outcomes in managing asthma, we recently created two machine learning models, one using University of Washington Medicine data and the other using Intermountain Healthcare data, to predict asthma hospital visits in the next 12 months in asthma patients. As is common in machine learning, neither model supplies explanations for its predictions. To tackle this interpretability issue of black-box models, we developed an automated method to produce rule-style explanations for any machine learning model’s predictions made on imbalanced tabular data and to recommend customized interventions without lowering the prediction accuracy. Our method exhibited good performance in explaining our Intermountain Healthcare model’s predictions. Yet, it stays unknown how well our method generalizes to academic healthcare systems, whose patient composition differs from that of Intermountain Healthcare. This study evaluates our automated explaining method’s generalizability to the academic healthcare system University of Washington Medicine on predicting asthma hospital visits. We did a secondary analysis on 82,888 University of Washington Medicine data instances of asthmatic adults between 2011 and 2018, using our method to explain our University of Washington Medicine model’s predictions and to recommend customized interventions. Our results showed that for predicting asthma hospital visits, our automated explaining method had satisfactory generalizability to University of Washington Medicine. In particular, our method explained the predictions for 87.6% of the asthma patients whom our University of Washington Medicine model accurately predicted to experience asthma hospital visits in the next 12 months. |
| Databáze: | OpenAIRE |
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