ArticleJournal of biomedical informatics2019
Symptom-based patient stratification in mental illness using clinical notes.
Article in Journal of biomedical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled it
- Enhancing disease clustering through symptom-based analysis and large language model interpretations.Scientific reports · 2025Article
- Online harms: Problematic technology use is a public health concern and requires a multistakeholder approach.Addictive behaviors reports · 2025Review
- Predicting recurrent chat contact in a psychological intervention for the youth using natural language processing.NPJ digital medicine · 2024Article
- A Traumatic Brain Injury Prescreening Tool for Intimate Partner Violence Patients Using Initial Clinical Reports and Machine Learning.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Editorial: Community series in novel antipsychotics within and beyond clinical trials: symptom-based treatment of psychiatric disorders with D3-D2 partial agonists, volume II.Frontiers in psychiatry · 2023Article
- Examining Analytic Practices in Latent Dirichlet Allocation Within Psychological Science: Scoping Review.Journal of medical Internet research · 2022Article
- Multi-faceted semantic clustering with text-derived phenotypes.Computers in biology and medicine · 2021Article
- Cognitive Impairments in Schizophrenia: A Study in a Large Clinical Sample Using Natural Language Processing.Frontiers in digital health · 2021Article
- Deep phenotyping: Embracing complexity and temporality-Towards scalability, portability, and interoperability.Journal of biomedical informatics · 2020Article
- Factors associated with poor self-management documented in home health care narrative notes for patients with heart failure.Heart & lung : the journal of critical careArticle
- Stigmatization and Self-Perception regarding issues related to Mental Health: A qualitative survey from a lower and middle-income country.Pakistan journal of medical sciencesArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Mental illnesses are highly heterogeneous with diagnoses based on symptoms that are generally qualitative, subjective, and documented in free text clinical notes rather than as structured data. Moreover, there exists significant variation in symptoms within diagnostic categories as well as substantial overlap in symptoms between diagnostic categories. These factors pose extra challenges for phenotyping patients with mental illness, a task that has proven challenging even for seemingly well characterized diseases. The ability to identify more homogeneous patient groups could both increase our ability to apply a precision medicine approach to psychiatric disorders and enable elucidation of underlying biological mechanism of pathology. We describe a novel approach to deep phenotyping in mental illness in which contextual term extraction is used to identify constellations of symptoms in a cohort of patients diagnosed with schizophrenia and related disorders. We applied topic modeling and dimensionality reduction to identify similar groups of patients and evaluate the resulting clusters through visualization and interrogation of clinically interpretable weighted features. Our findings show that patients diagnosed with schizophrenia may be meaningfully stratified using symptom-based clustering.
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What Socratic 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 Socratic graph.