Anusha Ihalapathirana, Konstantina Chalkou, Pekka Siirtola, Satu Tamminen, Gunjan Chandra, Pascal Benkert, Jens Kuhle, Georgia Salanti, Juha Röning, Explainable Artificial Intelligence to predict clinical outcomes in type 1 diabetes and relapsing-remitting multiple sclerosis adult patients, Informatics in Medicine Unlocked, Volume 42, 2023, 101349, ISSN 2352-9148, https://doi.org/10.1016/j.imu.2023.101349
Explainable Artificial Intelligence to predict clinical outcomes in type 1 diabetes and relapsing-remitting multiple sclerosis adult patients
|Author:||Ihalapathirana, Anusha1; Chalkou, Konstantina2; Siirtola, Pekka1;|
1Biomimetics and Intelligent Systems Group, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, FI-90014, Finland
2Institute of Social and Preventive Medicine, University of Bern, Bern, CH-3012, Switzerland
3Clinical Trial Unit, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, 4001, Switzerland
4Multiple Sclerosis Centre, Neurologic Clinic and Policlinic, Departments of Head, Spine and Neuromedicine, Biomedicine and Clinical Research, University Hospital Basel and University of Basel, Basel, 4001, Switzerland
5Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB), University Hospital and University of Basel, Basel, 4001, Switzerland
|Online Access:||PDF Full Text (PDF, 3.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20231017140462
|Publish Date:|| 2023-10-17
Artificial intelligence (AI) is increasingly being used to improve patient care and management. In this paper, we propose explainable AI (XAI) models for predicting severe hypoglycemia (SH) and diabetic ketoacidosis (DKA) episodes in adults with type 1 diabetes (T1D) and relapses in adults with relapsing-remitting multiple sclerosis (RRMS). We follow a three-step process in this study: (1) develop baseline machine learning (ML) models, (2) improve the models using ReliefF feature selection technique, and develop sex-stratified models, (3) explain the models and their results using SHapley Additive exPlanations (SHAP). We built six ML models (XGBoost, LightGBM, CatBoost, AdaBoost, random forest, and linear regression) for all scenarios. Applying the ReliefF feature selection led to improved model performance in predicting all outcomes compared to the baseline models. Additionally, sex-stratified models further improved the prediction of SH episodes and relapses. The F1 scores for predicting SH episodes in male and female patients were 84.07% and 84.95%, respectively, and the DKA prediction model achieved an F1 score of 78.67%. The proposed relapse prediction models outperformed existing models with F1 scores of 84.55% (males) and 76.11% (females), and ROCs of 70.26% (males) and 69.05% (females). Our results highlight the importance of considering sex differences, socioeconomic factors, and physical and mental health in medical outcome prediction. Boosting ML algorithms were found to be effective in detecting SH and DKA in T1D patients and relapses in RRMS patients compared to conventional tree-based ML and statistical models.
Informatics in medicine unlocked
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This research is funded by the HTx project. The HTx project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825162.
|EU Grant Number:||
(825162) HTx - Next Generation Health Technology Assessment to support patient-centred, societally oriented, real-time decision-making on access and reimbursement for health technologies throughout Europe
© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).