Interpretability of machine learning based prediction models in healthcare

Image credit: Wiley

Abstract

There is a need of ensuring machine learning models that are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end-users. Further, interpretable machine learning models allow healthcare experts to make reasonable and data-driven decisions to provide personalized decisions that can ultimately lead to higher quality of service in healthcare. Generally, we can classify interpretability approaches in two groups where the first focuses on personalized interpretation (local interpretability) while the second summarizes prediction models on a population level (global interpretability). Alternatively, we can group interpretability methods into model-specific techniques, which are designed to interpret predictions generated by a specific model, such as a neural network, and model-agnostic approaches, which provide easy-to-understand explanations of predictions made by any machine learning model. Here, we give an overview of interpretability approaches and provide examples of practical interpretability of machine learning in different areas of healthcare, including prediction of health-related outcomes, optimizing treatments or improving the efficiency of screening for specific conditions. Further, we outline future directions for interpretable machine learning and highlight the importance of developing algorithmic solutions that can enable machine-learning driven decision making in high-stakes healthcare problems.

Publication
WIREs Data Mining and Knowledge Discovery, 10(5), p. e1379
Gregor Štiglic
Gregor Štiglic
Associate Professor and head of Research Institute

My research interests include predictive models in healthcare, interpretability of complex models.

Primož Kocbek
Primož Kocbek
PhD Student

My research interests include statistical models and machine learning techniques with applications in healthcare. My specific areas of interest include temporal data analysis, interpretability of prediction models, stability of algorithms, advanced machine learning methods on massive datasets, e.g. deep neural networks.

Nino Fijačko
Nino Fijačko
PhD Student

My research interests include the newest pedagogical technologies in different healthcare fields and their effect on individual persons. Specific areas of interest include how serious game in gamification affect the level of physiological and psychological aspects in critical situations, such as cardiopulmonary resuscitation.

Marinka Zitnik
Marinka Zitnik
Assistant Professor of Biomedical Informatics
Katrien Verbert
Katrien Verbert
Professor
Leona Cilar Budler
Leona Cilar Budler
PhD

My research interests include mental health, nursing research, and health informatics. Specific areas of interest include adolescent mental health, psychometric testing of questionnaires, questionnaire localization, and quantitative data analysis.