title: Enhancing clinical problem lists through data mining and natural language processing.
abstract: The availability of timely, accurate, comprehensive, and coded clinical problem lists is essential for supporting a range of healthcare activities. Evidence and experience suggest, however, that problem lists are frequently out-of-date, sometimes omit clinically important problems, and contain uncoded entries. Here, we describe a study being performed at Partners HealthCare System to explore automated techniques for enhancing existing problem lists.
authors.affiliation: Clinical Informatics Research & Development, Partners HealthCare System, Wellesley, MA, USA.
authors.forename: Elizabeth S
authors.initials: ES
authors.lastname: Chen
authors.forename: Adam
authors.initials: A
authors.lastname: Wright
authors.forename: Francine L
authors.initials: FL
authors.lastname: Maloney
authors.forename: Cheryl
authors.initials: C
authors.lastname: Van Putten
authors.forename: Marilyn D
authors.initials: MD
authors.lastname: Paterno
authors.forename: Howard S
authors.initials: HS
authors.lastname: Goldberg
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016247:Information Storage and Retrieval / Q000379:methods; D008487:Medical History Taking / Q000379:methods*; D016347:Medical Records Systems, Computerized / Q000458:organization & administration*; D008501:Medical Records, Problem-Oriented; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D011785:Quality Assurance, Health Care / Q000379:methods*; D014481:United States