ArticleJournal of biomedical informatics2017
Counting trees in Random Forests: Predicting symptom severity in psychiatric intake reports.
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 11 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 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
- Modeling the Determinants of Subjective Well-Being in Schizophrenia.Schizophrenia bulletin · 2025Trial
- 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
- Descriptive forest: experiments on a novel tree-structure-generalization method for describing cardiovascular diseases.BMC medical informatics and decision making · 2023Article
- Informatics Research on Mental Health Functioning: Decision Support for the Social Security Administration Disability Program.Psychiatric services (Washington, D.C.) · 2023Article
- Entropy-based discrimination between translated Chinese and original Chinese using data mining techniques.PloS one · 2022Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients.Clinical kidney journal · 2019Article
- Article
- Symptom severity prediction from neuropsychiatric clinical records: Overview of 2016 CEGS N-GRID shared tasks Track 2.Journal of biomedical informatics · 2017Review
- A natural language processing challenge for clinical records: Research Domains Criteria (RDoC) for psychiatry.Journal of biomedical informatics · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The CEGS N-GRID 2016 Shared Task (Filannino et al., 2017) in Clinical Natural Language Processing introduces the assignment of a severity score to a psychiatric symptom, based on a psychiatric intake report. We present a method that employs the inherent interview-like structure of the report to extract relevant information from the report and generate a representation. The representation consists of a restricted set of psychiatric concepts (and the context they occur in), identified using medical concepts defined in UMLS that are directly related to the psychiatric diagnoses present in the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) ontology. Random Forests provides a generalization of the extracted, case-specific features in our representation. The best variant presented here scored an inverse mean absolute error (MAE) of 80.64%. A concise concept-based representation, paired with identification of concept certainty and scope (family, patient), shows a robust performance on the task.
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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.