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

However, physical therapy procedures are typically described in unstructured clinical notes, meaning that simple data extraction methods such as database queries cannot be applied to obtain sufficient information. Additionally, the language used to describe these procedures can differ between clinicians, cites, and times. A more advanced natural language processing (NLP) algorithm is required to extract this information from clinical notes, but such a method has not yet been developed for this application. In this paper we devise and compare several approaches to extracting information about therapeutic procedures for physical rehabilitation, both for the purpose of emulating a manual annotation process using named entity recognition (NER) and categorizing descriptions of therapeutic procedures using multi label sequence classification. Using a set of manually annotated notes as a gold standard reference, we evaluated the performance of a rule-based algorithm using the MedTagger software, and several machine learning approaches such as logistic regression (LR) and support vector machines (SVM). Methods Data Collection We identified a cohort of patients diagnosed with stroke between January 1st, 2016 and December 31st, 2016 at UPMC. For these patients, we extracted clinical encounter notes created between January 1st, 2016 and December 31st, 2018 from the institutional data warehouse. The study was approved by the University of Pittsburgh's Institutional Review Board (IRB #21040204).