Article

Knowledge discovery for Deep Phenotyping serious mental illness from Electronic Mental Health records

2018-05-08

Abstract excerpt

<h4>Background: </h4> Deep Phenotyping is the precise and comprehensive analysis of phenotypic features in which the individual components of the phenotype are observed and described. In UK mental health clinical practice, most clinically relevant information is recorded as free text in the Electronic Health Record, and offers a granularity of information beyond what is expressed in most medical knowledge bases. T...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: fcd771e2-2d96-5d3e-8f80-5d060eaa45e4
DOI: 10.12688/f1000research.13830.2

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Knowledge discovery for Deep Phenotyping serious mental illness from Electronic Mental Health recordsDOI 10.12688/f1000research.13830.2Read

Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records.Semantic proximity Read

Phecoder: semantic retrieval for auditing and expanding ICD-based phenotypes in EHR biobanksSemantic proximity Read

Towards Similarity-based Differential Diagnostics For Common DiseasesSemantic proximity Read

Towards similarity-based differential diagnostics for common diseases.Semantic proximity Read

SemEHR: A General-purpose Semantic Search System to Surface Semantic Data from Clinical Notes for Tailored Care, Trial Recruitment and Clinical ResearchSemantic proximity Read

Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support toolsSemantic proximity Read

High Throughput Phenotyping for Dimensional Psychopathology in Electronic Health Records.Semantic proximity Read

Multi-faceted Semantic Clustering With Text-derived PhenotypesSemantic proximity Read

Multi-faceted semantic clustering with text-derived phenotypes.Semantic proximity Read

Extraction of Human Phenotype Ontology (HPO) Concepts from Clinical Notes Utilizing Large Language Models (LLM) with Model Context Protocol (MCP)Semantic proximity Read

Consistent Performance of GPT-4o in Rare Disease Diagnosis Across Nine Languages and 4967 CasesSemantic proximity Read

Towards automated phenotype definition extraction using large language modelsSemantic proximity Read