ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2015
Interpretable Probabilistic Latent Variable Models for Automatic Annotation of Clinical Text.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Neural Conversational Agent for Weight Loss Counseling: Protocol for an Implementation and Feasibility Study.JMIR research protocols · 2024Article
- Detecting conversation topics in primary care office visits from transcripts of patient-provider interactions.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Developing Machine Learning Models for Behavioral Coding.Journal of pediatric psychology · 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
- Identifying Effective Motivational Interviewing Communication Sequences Using Automated Pattern Analysis.Journal of healthcare informatics research · 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
- A study of the effectiveness of machine learning methods for classification of clinical interview fragments into a large number of categories.Journal of biomedical informatics · 2016Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We propose Latent Class Allocation (LCA) and Discriminative Labeled Latent Dirichlet Allocation (DL-LDA), two novel interpretable probabilistic latent variable models for automatic annotation of clinical text. Both models separate the terms that are highly characteristic of textual fragments annotated with a given set of labels from other non-discriminative terms, but rely on generative processes with different structure of latent variables. LCA directly learns class-specific multinomials, while DL-LDA breaks them down into topics (clusters of semantically related words). Extensive experimental evaluation indicates that the proposed models outperform Naïve Bayes, a standard probabilistic classifier, and Labeled LDA, a state-of-the-art topic model for labeled corpora, on the task of automatic annotation of transcripts of motivational interviews, while the output of the proposed models can be easily interpreted by clinical practitioners.
Indexed as
Identifiers
26958214PMC4765604What OpenQuestion holds
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.