Detalhe da pesquisa
1.
Autism spectrum disorders as a risk factor for adolescent self-harm: a retrospective cohort study of 113,286 young people in the UK.
BMC Med
; 20(1): 137, 2022 04 29.
Artigo
Inglês
| MEDLINE | ID: mdl-35484575
2.
Temporal and diurnal variation in social media posts to a suicide support forum.
BMC Psychiatry
; 21(1): 259, 2021 05 19.
Artigo
Inglês
| MEDLINE | ID: mdl-34011346
3.
Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances.
J Biomed Inform
; 88: 11-19, 2018 12.
Artigo
Inglês
| MEDLINE | ID: mdl-30368002
4.
Identifying Mentions of Pain in Mental Health Records Text: A Natural Language Processing Approach.
Stud Health Technol Inform
; 310: 695-699, 2024 Jan 25.
Artigo
Inglês
| MEDLINE | ID: mdl-38269898
5.
Development of a Knowledge Graph Embeddings Model for Pain.
AMIA Annu Symp Proc
; 2023: 299-308, 2023.
Artigo
Inglês
| MEDLINE | ID: mdl-38222382
6.
Development of a Corpus Annotated With Mentions of Pain in Mental Health Records: Natural Language Processing Approach.
JMIR Form Res
; 7: e45849, 2023 Jun 26.
Artigo
Inglês
| MEDLINE | ID: mdl-37358897
7.
Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records.
Front Psychiatry
; 14: 1217649, 2023.
Artigo
Inglês
| MEDLINE | ID: mdl-38152362
8.
Evaluating physical urban features in several mental illnesses using electronic health record data.
Front Digit Health
; 4: 874237, 2022.
Artigo
Inglês
| MEDLINE | ID: mdl-36158997
9.
Portability of natural language processing methods to detect suicidality from clinical text in US and UK electronic health records.
J Affect Disord Rep
; 102022 Dec.
Artigo
Inglês
| MEDLINE | ID: mdl-36644339
10.
Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data.
BMJ Open
; 12(12): e058058, 2022 12 05.
Artigo
Inglês
| MEDLINE | ID: mdl-36576182
11.
Can natural language processing models extract and classify instances of interpersonal violence in mental healthcare electronic records: an applied evaluative study.
BMJ Open
; 12(2): e052911, 2022 02 16.
Artigo
Inglês
| MEDLINE | ID: mdl-35172999
12.
Factuality levels of diagnoses in Swedish clinical text.
Stud Health Technol Inform
; 169: 559-63, 2011.
Artigo
Inglês
| MEDLINE | ID: mdl-21893811
13.
A Review of Recent Work in Transfer Learning and Domain Adaptation for Natural Language Processing of Electronic Health Records.
Yearb Med Inform
; 30(1): 239-244, 2021 Aug.
Artigo
Inglês
| MEDLINE | ID: mdl-34479396
14.
Using General-purpose Sentiment Lexicons for Suicide Risk Assessment in Electronic Health Records: Corpus-Based Analysis.
JMIR Med Inform
; 9(4): e22397, 2021 Apr 13.
Artigo
Inglês
| MEDLINE | ID: mdl-33847595
15.
Development of a Lexicon for Pain.
Front Digit Health
; 3: 778305, 2021.
Artigo
Inglês
| MEDLINE | ID: mdl-34966903
16.
A natural language processing approach for identifying temporal disease onset information from mental healthcare text.
Sci Rep
; 11(1): 757, 2021 01 12.
Artigo
Inglês
| MEDLINE | ID: mdl-33436814
17.
Using natural language processing to extract self-harm and suicidality data from a clinical sample of patients with eating disorders: a retrospective cohort study.
BMJ Open
; 11(12): e053808, 2021 12 31.
Artigo
Inglês
| MEDLINE | ID: mdl-34972768
18.
Louhi 2014: Special issue on health text mining and information analysis.
BMC Med Inform Decis Mak
; 15 Suppl 2: S1, 2015.
Artigo
Inglês
| MEDLINE | ID: mdl-26099575
19.
Enhancing predictions of patient conveyance using emergency call handler free text notes for unconscious and fainting incidents reported to the London Ambulance Service.
Int J Med Inform
; 141: 104179, 2020 09.
Artigo
Inglês
| MEDLINE | ID: mdl-32663739
20.
Reviewing a Decade of Research Into Suicide and Related Behaviour Using the South London and Maudsley NHS Foundation Trust Clinical Record Interactive Search (CRIS) System.
Front Psychiatry
; 11: 553463, 2020.
Artigo
Inglês
| MEDLINE | ID: mdl-33329090