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

Explainable Suicide Phenotyping from Initial Psychiatric Evaluation Notes Using Reasoning Large Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium - 1 Jan 2024

Li Zehan, Wang Wanjing, Shahani Lokesh, Vieira Rodrigo M, Selek Salih, Soares Jair, Liu Hongfang, Huang Ming

Abstract excerpt

Clinical phenotyping is the process of extracting patient's observable symptoms and traits to better understand their disease condition. Suicide phenotyping focuses more on behavioral and cognitive characteristics, such as suicide ideation, attempt, and self-injury, to identify suicide risks and improve interventions. In this study, we leveraged the latest reasoning models, namely 4o, o1, and o3-mini, to perform...

Read the complete abstract on PubMed

Topics

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

Share to GPT gene
Share to Natural Language Processing

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Phenotype

Share to Suicidal Ideation

Share to Suicide

Source

Open in PubMed Open full text

Identifiers

PMID: 41726447
PMCID: PMC12919457

Genes

GPT

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 9 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Explainable Suicide Phenotyping from Initial Psychiatric Evaluation Notes Using Reasoning Large Language Models.PMID 41726447Read ↗

Explainable Suicide Phenotyping from Initial Psychiatric Evaluation Notes Using Reasoning Large Language ModelsSemantic proximity Read ↗

INSIGHTFUL: Insight Generation through Clinical Annotation, Analysis, and Modeling of Suicide-Related Factors towards Understanding and LifesavingSemantic proximity Read ↗

Automated Insomnia Phenotyping from Electronic Health Records: Leveraging Large Language Models to Decode Clinical NarrativesSemantic proximity Read ↗

clickBrick Prompt Engineering: Optimizing Large Language Model Performance in Clinical PsychiatrySemantic proximity Read ↗

Large-Scale Psychiatric Concept Extraction from Electronic Health Records: A Comparative Study of Encoder-Based Language ModelsSemantic proximity Read ↗

Scalable Incident Detection via Natural Language Processing and Probabilistic Language ModelsSemantic proximity Read ↗

mpathic Psychologist-led AI Clinical Tests Suicide Benchmark (mPACT-S-v1.0)Semantic proximity Read ↗

Improving ascertainment of suicidal ideation and suicide attempt with natural language processingSemantic proximity Read ↗

Evaluating Diagnostic Accuracy and Clinical Reasoning of Multiple Large Language Models in PsychiatrySemantic proximity Read ↗

REFINE: Closing the Loop Between Large Language Models and Symbolic Rules in Clinical NLPSemantic proximity Read ↗

Characterizing Dementia Phenotypes from Unstructured EHR Notes with Generative AI and Interpretable Machine LearningSemantic proximity Read ↗

Reasoning Models for Text Mining in Oncology – a Comparison Between o1 Preview and GPT-4oSemantic proximity Read ↗

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