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Objective: Using natural language processing (NLP) to find sentences thatstate treatment plans in a clinical note, would automate plan extraction andwould further enable their use in tools that help providers and care managers.However, as in the most NLP tasks on clinical text, creating gold standard totrain and test NLP models is tedious and expensive. Fortuitously, sometimes butnot always clinical notes contain sections with a heading that identifies thesection as a plan. Leveraging contents of such labeled sections as a noisytraining data, we assessed accuracy of NLP models trained with the data.  Methods: We used common variations of plan headings and rule-based heuristicsto find plan sections with headings in clinical notes, and we extractedsentences from them and formed a noisy training data of plan sentences. Wetrained Support Vector Machine (SVM) and Convolutional Neural Network (CNN)models with the data. We measured accuracy of the trained models on the noisydataset using ten-fold cross validation and separately on a set-aside manuallyannotated dataset.  Results: About 13% of 117,730 clinical notes contained treatment planssections with recognizable headings in the 1001 longitudinal patient recordsthat were obtained from Cleveland Clinic under an IRB approval. We were able toextract and create a noisy training data of 13,492 plan sentences from theclinical notes. CNN achieved best F measures, 0.91 and 0.97 in thecross-validation and set-aside evaluation experiments respectively. SVMslightly underperformed with F measures of 0.89 and 0.96 in the sameexperiments.  Conclusion: Our study showed that the training supervised learning modelsusing noisy plan sentences was effective in identifying them in all clinicalnotes. More broadly, sections with informal headings in clinical notes can be agood source for generating effective training data.

Training Models to Extract Treatment Plans from Clinical Notes Using Contents of Sections with Headings