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Physical rehabilitation plays a crucial role in the recovery process ofpost-stroke patients. By personalizing therapies for patients leveragingpredictive modeling and electronic health records (EHRs), healthcare providerscan make the rehabilitation process more efficient. Before predictive modelingcan provide decision support for the assignment of treatment plans, automatedmethods are necessary to extract physical rehabilitation exercise informationfrom unstructured EHRs. We introduce a rule-based natural language processingalgorithm to annotate therapeutic procedures for stroke patients and compare itto several small machine learning models. We find that our algorithmoutperforms these models in extracting half of the concepts where sufficientdata is available, and individual exercise descriptions can be assigned binarylabels with an f-score of no less than 0.75 per concept. More research needs tobe done before these algorithms can be deployed on unlabeled documents, butcurrent progress gives promise to the potential of precision rehabilitationresearch.

Extracting Physical Rehabilitation Exercise Information from Clinical  Notes: a Comparison of Rule-Based and Machine Learning Natural Language  Processing Techniques