ArticleJournal of biomedical informatics2017
Predictive modeling for classification of positive valence system symptom severity from initial psychiatric evaluation records.
Article in Journal of biomedical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning and Natural Language Processing in Mental Health: Systematic Review.Journal of medical Internet research · 2021Pooled it
- The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review.International journal of mental health nursing · 2025Review
- Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records.BMC medical informatics and decision making · 2024Article
- Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing.Communications medicine · 2024Article
- Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts.JAMIA open · 2021Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- A natural language processing challenge for clinical records: Research Domains Criteria (RDoC) for psychiatry.Journal of biomedical informatics · 2017Article
- Natural Language Processing for EHR-Based Computational Phenotyping.IEEE/ACM transactions on computational biology and bioinformaticsReview
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
In response to the challenges set forth by the CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing, we describe a framework to automatically classify initial psychiatric evaluation records to one of four positive valence system severities: absent, mild, moderate, or severe. We used a dataset provided by the event organizers to develop a framework comprised of natural language processing (NLP) modules and 3 predictive models (two decision tree models and one Bayesian network model) used in the competition. We also developed two additional predictive models for comparison purpose. To evaluate our framework, we employed a blind test dataset provided by the 2016 CEGS N-GRID. The predictive scores, measured by the macro averaged-inverse normalized mean absolute error score, from the two decision trees and Naïve Bayes models were 82.56%, 82.18%, and 80.56%, respectively. The proposed framework in this paper can potentially be applied to other predictive tasks for processing initial psychiatric evaluation records, such as predicting 30-day psychiatric readmissions.
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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.