Feature engineering with clinical expert knowledge: A case study assessment of machine learning model complexity and performance.

Autor: Roe KD; Johns Hopkins Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States of America.; The Institute of Clinical and Translational Research, Johns Hopkins University, Baltimore, MD, United States of America., Jawa V; Johns Hopkins Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States of America.; Department of Computer Science, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States of America., Zhang X; Division of Health Sciences Informatics, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America., Chute CG; Johns Hopkins Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States of America.; The Institute of Clinical and Translational Research, Johns Hopkins University, Baltimore, MD, United States of America.; Division of Health Sciences Informatics, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America.; Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America., Epstein JA; Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America., Matelsky J; Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America., Shpitser I; Johns Hopkins Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States of America.; Department of Computer Science, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, United States of America., Taylor CO; Johns Hopkins Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States of America.; The Institute of Clinical and Translational Research, Johns Hopkins University, Baltimore, MD, United States of America.; Division of Health Sciences Informatics, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America.; Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America.; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2020 Apr 23; Vol. 15 (4), pp. e0231300. Date of Electronic Publication: 2020 Apr 23 (Print Publication: 2020).
DOI: 10.1371/journal.pone.0231300
Abstrakt: Incorporating expert knowledge at the time machine learning models are trained holds promise for producing models that are easier to interpret. The main objectives of this study were to use a feature engineering approach to incorporate clinical expert knowledge prior to applying machine learning techniques, and to assess the impact of the approach on model complexity and performance. Four machine learning models were trained to predict mortality with a severe asthma case study. Experiments to select fewer input features based on a discriminative score showed low to moderate precision for discovering clinically meaningful triplets, indicating that discriminative score alone cannot replace clinical input. When compared to baseline machine learning models, we found a decrease in model complexity with use of fewer features informed by discriminative score and filtering of laboratory features with clinical input. We also found a small difference in performance for the mortality prediction task when comparing baseline ML models to models that used filtered features. Encoding demographic and triplet information in ML models with filtered features appeared to show performance improvements from the baseline. These findings indicated that the use of filtered features may reduce model complexity, and with little impact on performance.
Competing Interests: The authors have declared that no competing interest exist.
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje