Unsupervised Learning for Computational Phenotyping
This work addresses the scalability and accuracy limitations of supervised learning in computational phenotyping for healthcare researchers, but it is incremental as it builds on existing methods.
The paper tackles the problem of computational phenotyping from healthcare data by implementing an unsupervised learning method in Apache Spark and Python, applied to laboratory time-series data in MIMIC-III, and releases it as an open-source tool for exploration, analysis, and visualization.
With large volumes of health care data comes the research area of computational phenotyping, making use of techniques such as machine learning to describe illnesses and other clinical concepts from the data itself. The "traditional" approach of using supervised learning relies on a domain expert, and has two main limitations: requiring skilled humans to supply correct labels limits its scalability and accuracy, and relying on existing clinical descriptions limits the sorts of patterns that can be found. For instance, it may fail to acknowledge that a disease treated as a single condition may really have several subtypes with different phenotypes, as seems to be the case with asthma and heart disease. Some recent papers cite successes instead using unsupervised learning. This shows great potential for finding patterns in Electronic Health Records that would otherwise be hidden and that can lead to greater understanding of conditions and treatments. This work implements a method derived strongly from Lasko et al., but implements it in Apache Spark and Python and generalizes it to laboratory time-series data in MIMIC-III. It is released as an open-source tool for exploration, analysis, and visualization, available at https://github.com/Hodapp87/mimic3_phenotyping