ArticleJournal of biomedical informatics2016
A study of the effectiveness of machine learning methods for classification of clinical interview fragments into a large number of categories.
Article in Journal of biomedical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography.Eye (London, England) · 2024Observational
- Neural Conversational Agent for Weight Loss Counseling: Protocol for an Implementation and Feasibility Study.JMIR research protocols · 2024Article
- Opening the Black Box of Family-Based Treatments: An Artificial Intelligence Framework to Examine Therapeutic Alliance and Therapist Empathy.Clinical child and family psychology review · 2023Article
- Language Use in Mother-Adolescent Dyadic Interaction: Preliminary Results.International Conference on Affective Computing and Intelligent Interaction and workshops : [proceedings]. ACII (Conference) · 2022Article
- Asthma and Technology in Emerging African American Adults (The ATHENA Project): Protocol for a Trial Using the Multiphase Optimization Strategy Framework.JMIR research protocols · 2022Article
- Classification of Watermelon Seeds Using Morphological Patterns of X-ray Imaging: A Comparison of Conventional Machine Learning and Deep Learning.Sensors (Basel, Switzerland) · 2020Article
- The application of machine learning to balance a total knee arthroplasty.Bone & joint open · 2020Article
- Automated Analysis of Public Health Laboratory Test Results.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Detecting conversation topics in primary care office visits from transcripts of patient-provider interactions.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Computer-Based Readability Testing of Information Booklets for German Cancer Patients.Journal of cancer education : the official journal of the American Association for Cancer Education · 2019Article
- Design feasibility of an automated, machine-learning based feedback system for motivational interviewing.Psychotherapy (Chicago, Ill.) · 2019Article
- IMPROVING THE PREDICTION OF THERAPIST BEHAVIORS IN ADDICTION COUNSELING BY EXPLOITING CLASS CONFUSIONS.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2019Article
- Identifying Effective Motivational Interviewing Communication Sequences Using Automated Pattern Analysis.Journal of healthcare informatics research · 2019Article
- Deep Neural Architectures for Discourse Segmentation in E-Mail Based Behavioral Interventions.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Predicting the Outcome of Patient-Provider Communication Sequences using Recurrent Neural Networks and Probabilistic Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
This study examines the effectiveness of state-of-the-art supervised machine learning methods in conjunction with different feature types for the task of automatic annotation of fragments of clinical text based on codebooks with a large number of categories. We used a collection of motivational interview transcripts consisting of 11,353 utterances, which were manually annotated by two human coders as the gold standard, and experimented with state-of-art classifiers, including Naïve Bayes, J48 Decision Tree, Support Vector Machine (SVM), Random Forest (RF), AdaBoost, DiscLDA, Conditional Random Fields (CRF) and Convolutional Neural Network (CNN) in conjunction with lexical, contextual (label of the previous utterance) and semantic (distribution of words in the utterance across the Linguistic Inquiry and Word Count dictionaries) features. We found out that, when the number of classes is large, the performance of CNN and CRF is inferior to SVM. When only lexical features were used, interview transcripts were automatically annotated by SVM with the highest classification accuracy among all classifiers of 70.8%, 61% and 53.7% based on the codebooks consisting of 17, 20 and 41 codes, respectively. Using contextual and semantic features, as well as their combination, in addition to lexical ones, improved the accuracy of SVM for annotation of utterances in motivational interview transcripts with a codebook consisting of 17 classes to 71.5%, 74.2%, and 75.1%, respectively. Our results demonstrate the potential of using machine learning methods in conjunction with lexical, semantic and contextual features for automatic annotation of clinical interview transcripts with near-human accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.