Machine learning methods for clinical forms analysis in mental health.
In preparation for a clinical information system implementation, the Centre for Addiction and Mental Health (CAMH) Clinical Information Transformation project completed multiple preparation steps. An automated process was desired to supplement the onerous task of manual analysis of clinical forms. We used natural language processing (NLP) and machine learning (ML) methods for a series of 266 separate clinical forms. For the investigation, documents were represented by feature vectors. We used four ML algorithms for our examination of the forms: cluster analysis, k-nearest neigh-bours (kNN), decision trees and support vector machines (SVM). Parameters for each algorithm were optimized. SVM had the best performance with a precision of 64.6%. Though we did not find any method sufficiently accurate for practical use, to our knowledge this approach to forms has not been used previously in mental health.
Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada.
John
J
Strauss
Arturo Martinez
AM
Peguero
Graeme
G
Hirst
D001185:Artificial Intelligence; D004282:Documentation / Q000145:classification*; D005568:Forms and Records Control / Q000379:methods*; D006801:Humans; D008603:Mental Health / Q000145:classification*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D011996:Records / Q000145:classification*; D018875:Vocabulary, Controlled