2017/07/24 by Norman Hiob, Hiob, Norman, Stefan Lessmann +1
Computer Science · Environmental Science · Health Professions · Mathematics · #Artificial Intelligence in Healthcare #Health, Environment, Cognitive Aging #Machine Learning in Healthcare #stat.ML
paper · pdf · doi:10.48550/arxiv.1707.07425
arxiv created 2017/07/24 · arxiv updated 2017/07/25
The paper presents a systematic review of state-of-the-art approaches to identify patient cohorts using electronic health records. It gives a comprehensive overview of the most commonly de-tected phenotypes and its underlying data sets. Special attention is given to preprocessing of in-put data and the different modeling approaches. The literature review confirms natural language processing to be a promising approach for electronic phenotyping. However, accessibility and lack of natural language process standards for medical texts remain a challenge. Future research should develop such standards and further investigate which machine learning approaches are best suited to which type of medical data.