Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Integrating questionnaire measures for transdiagnostic psychiatric phenotyping using word2vec.

PloS one - 1 Jan 2020

Sonabend W Aaron, Pellegrini Amelia M, Chan Stephanie, Brown Hannah E, Rosenquist James N, Vuijk Pieter J, Doyle Alysa E, Perlis Roy H, Cai Tianxi

Abstract excerpt

BACKGROUND: Recent initiatives in psychiatry emphasize the utility of characterizing psychiatric symptoms in a multidimensional manner. However, strategies for applying standard self-report scales for multiaxial assessment have not been well-studied, particularly where the aim is to support both categorical and dimensional phenotypes. METHODS: We propose a method for applying natural language processing to derive...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Adolescent
Share to Aged

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Mental Disorders

Share to Phenotype

Share to Stochastic Processes

Share to Surveys and Questionnaires

Share to Young Adult

Source

Open in PubMed Open full text

Identifiers

PMID: 32243452
DOI: 10.1371/journal.pone.0230663
PMCID: PMC7122719

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Integrating questionnaire measures for transdiagnostic psychiatric phenotyping using word2vec.PMID 32243452Read ↗

The empirical structure of psychopathology is represented in large language modelsSemantic proximity Read ↗

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

Language or Syndrome? Investigating the Origins of the General Psychopathology Factor through Large Language Model EmbeddingsSemantic proximity Read ↗

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

Mapping of Transdiagnostic Neuropsychiatric Phenotypes Across Patients in Two General Hospitals.Semantic proximity Read ↗

Automated disease cohort selection using word embeddings from Electronic Health Records.Semantic proximity Read ↗

Phe2vec: Automated Disease Phenotyping based on Unsupervised Embeddings from Electronic Health RecordsSemantic proximity Read ↗

An Intrinsic and Extrinsic Evaluation of Learned COVID-19 Concepts using Open-Source Word Embedding SourcesSemantic proximity Read ↗

Optimizing Word Embeddings for Patient Portal Message Datasets with a Small Number of SamplesSemantic proximity Read ↗

Combining Clinical Embeddings with Multi-Omic Features for Improved Patient Classification and Interpretability in Parkinson’s DiseaseSemantic proximity Read ↗

Language-model-based patient embedding using electronic health records facilitates phenotyping, disease forecasting, and progression analysisSemantic proximity Read ↗

Genome-wide Association Study of Dimensional Psychopathology Using Electronic Health Records.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.