Caitlin Dreisbach|Theresa A. Koleck|Philip E. Bourne|Suzanne Bakken
A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data
University of Virginia|Columbia University|University of Virginia|Columbia University
systematic review aim synthesize literature use natural language processing nlp text mining apply symptom extraction processing electronic patientauthored text epat comprehensive literature search 1964 articles pubmed embase narrowed 21 eligible articles data related purpose text source number users andor posts evaluation metrics quality indicators recorded pain n 18 fatigue sleep disturbance n 18 frequently evaluated symptom clinical content categories studies accessed epat sources twitter online community forums patient portals focused diseases including diabetes cancer depression fifteen studies used nlp primary methodology studies reported evaluation metrics including precision recall fmeasure symptomspecific research questions nlp text mining used extract analyze patientauthored symptom data wide variety online communities though computational challenges accessing epat depth information provided directly patients offers new horizons precision medicine characterization subclinical symptoms creation personal health libraries outlined national library medicine future research consider needs patients expressed epat relevance symptom science understanding role epat plays health communication realtime assessment symptoms use nlp text mining critical patientcentered health system