ArticleJournal of the American Medical Informatics Association : JAMIA2013
Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records.
Article in Journal of the American Medical Informatics Association : JAMIA, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses 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
34 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Data Mining Algorithms and Techniques in Mental Health: A Systematic Review.Journal of medical systems · 2018Pooled it
- Applications of network analysis to routinely collected health care data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
- Natural Language Processing Methods and Bipolar Disorder: Scoping Review.JMIR mental health · 2022Article
- Mitochondrial protein dysfunction in pathogenesis of neurological diseases.Frontiers in molecular neuroscience · 2022Review
- The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis.JMIR medical informatics · 2021Review
- Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder.BMC medical informatics and decision making · 2020Article
- Epidemiology of recurrent seizure disorders and epilepsy in cats under primary veterinary care in the United Kingdom.Journal of veterinary internal medicine · 2020Article
- Automated Phenotyping Tool for Identifying Developmental Language Disorder Cases in Health Systems Data (APT-DLD): A New Research Algorithm for Deployment in Large-Scale Electronic Health Record Systems.Journal of speech, language, and hearing research : JSLHR · 2020Article
- Clinical and Pharmacological Aspects of Hospital-Acquired Acute Kidney Injuries Outside the Intensive Care Unit: A Phenome-Wide Association Study.Kidney diseases (Basel, Switzerland) · 2019Article
- Symptom-based patient stratification in mental illness using clinical notes.Journal of biomedical informatics · 2019Article
- Feature Ranking in Predictive Models for Hospital-Acquired Acute Kidney Injury.Scientific reports · 2018Article
- The use of electronic health records for psychiatric phenotyping and genomics.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2018Review
- Comparison of MetaMap and cTAKES for entity extraction in clinical notes.BMC medical informatics and decision making · 2018Article
- 21st century toolkit for optimizing population health through precision nutrition.Critical reviews in food science and nutrition · 2018Review
- Clinical information extraction applications: A literature review.Journal of biomedical informatics · 2018Review
- Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record.Applied clinical informatics · 2017Article
- EHR-based phenotyping: Bulk learning and evaluation.Journal of biomedical informatics · 2017Article
- Big data for bipolar disorder.International journal of bipolar disorders · 2016Review
- 'Big data' in mental health research: current status and emerging possibilities.Social psychiatry and psychiatric epidemiology · 2016Review
- Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records.Scientific reports · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectiveMental illness is the leading cause of disability in the USA, but boundaries between different mental illnesses are notoriously difficult to define. Electronic medical records (EMRs) have recently emerged as a powerful new source of information for defining the phenotypic signatures of specific diseases. We investigated how EMR-based text mining and statistical analysis could elucidate the phenotypic boundaries of three important neuropsychiatric illnesses-autism, bipolar disorder, and schizophrenia.
methodsWe analyzed the medical records of over 7000 patients at two facilities using an automated text-processing pipeline to annotate the clinical notes with Unified Medical Language System codes and then searching for enriched codes, and associations among codes, that were representative of the three disorders. We used dimensionality-reduction techniques on individual patient records to understand individual-level phenotypic variation within each disorder, as well as the degree of overlap among disorders.
resultsWe demonstrate that automated EMR mining can be used to extract relevant drugs and phenotypes associated with neuropsychiatric disorders and characteristic patterns of associations among them. Patient-level analyses suggest a clear separation between autism and the other disorders, while revealing significant overlap between schizophrenia and bipolar disorder. They also enable localization of individual patients within the phenotypic 'landscape' of each disorder.
conclusionsBecause EMRs reflect the realities of patient care rather than idealized conceptualizations of disease states, we argue that automated EMR mining can help define the boundaries between different mental illnesses, facilitate cohort building for clinical and genomic studies, and reveal how clear expert-defined disease boundaries are in practice.
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.