ReviewJournal of biomedical informatics2017
Symptom severity prediction from neuropsychiatric clinical records: Overview of 2016 CEGS N-GRID shared tasks Track 2.
Review in Journal of biomedical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 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
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- The interaction network and potential clinical effectiveness of dimensional psychopathology phenotyping based on EMR: a Bayesian network approach.BMC psychiatry · 2025Article
- 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
- A scoping review of publicly available language tasks in clinical natural language processing.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case.JMIR medical informatics · 2022Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Cohort selection for clinical trials using hierarchical neural network.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death.Depression and anxiety · 2019Article
- Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances.Journal of biomedical informatics · 2018Article
- Machine learning for psychiatric patient triaging: an investigation of cascading classifiers.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Advancing the State of the Art in Clinical Natural Language Processing through Shared Tasks.Yearbook of medical informatics · 2018Review
- Automatic mining of symptom severity from psychiatric evaluation notes.International journal of methods in psychiatric research · 2018Article
- Article
- Adapting Word Embeddings from Multiple Domains to Symptom Recognition from Psychiatric Notes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
- Automatic recognition of symptom severity from psychiatric evaluation records.Journal of biomedical informatics · 2017Article
- Predictive modeling for classification of positive valence system symptom severity from initial psychiatric evaluation records.Journal of biomedical informatics · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
The second track of the CEGS N-GRID 2016 natural language processing shared tasks focused on predicting symptom severity from neuropsychiatric clinical records. For the first time, initial psychiatric evaluation records have been collected, de-identified, annotated and shared with the scientific community. One-hundred-ten researchers organized in twenty-four teams participated in this track and submitted sixty-five system runs for evaluation. The top ten teams each achieved an inverse normalized macro-averaged mean absolute error score over 0.80. The top performing system employed an ensemble of six different machine learning-based classifiers to achieve a score 0.86. The task resulted to be generally easy with the exception of two specific classes of records: records with very few but crucial positive valence signals, and records describing patients predominantly affected by negative rather than positive valence. Those cases proved to be very challenging for most of the systems. Further research is required to consider the task solved. Overall, the results of this track demonstrate the effectiveness of data-driven approaches to the task of symptom severity classification.
Indexed as
Identifiers
What 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.