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1.
DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing.
J Biomed Inform
; 138: 104286, 2023 02.
Artículo
en Inglés
| MEDLINE | ID: mdl-36706848
2.
Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients.
BMC Med Inform Decis Mak
; 20(1): 79, 2020 04 29.
Artículo
en Inglés
| MEDLINE | ID: mdl-32349766
3.
Performance of International Classification of Disease-10 codes in detecting emergency department patients with opioid misuse.
Addiction
; 119(4): 766-771, 2024 Apr.
Artículo
en Inglés
| MEDLINE | ID: mdl-38011858
4.
Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning.
Proc Conf Assoc Comput Linguist Meet
; 2023(ClinicalNLP): 78-85, 2023 Jul.
Artículo
en Inglés
| MEDLINE | ID: mdl-37492270
5.
Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults.
JMIR Med Inform
; 11: e44977, 2023 Apr 20.
Artículo
en Inglés
| MEDLINE | ID: mdl-37079367
6.
The Identification of Subphenotypes and Associations with Health Outcomes in Patients with Opioid-Related Emergency Department Encounters Using Latent Class Analysis.
Int J Environ Res Public Health
; 19(14)2022 07 21.
Artículo
en Inglés
| MEDLINE | ID: mdl-35886733
7.
External validation of a machine learning classifier to identify unhealthy alcohol use in hospitalized patients.
Addiction
; 117(4): 925-933, 2022 Apr.
Artículo
en Inglés
| MEDLINE | ID: mdl-34729829
8.
Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning study.
Lancet Digit Health
; 4(6): e426-e435, 2022 06.
Artículo
en Inglés
| MEDLINE | ID: mdl-35623797
9.
Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study.
JMIR Public Health Surveill
; 8(12): e38158, 2022 12 08.
Artículo
en Inglés
| MEDLINE | ID: mdl-36265163
10.
The Evaluation of a Clinical Decision Support Tool Using Natural Language Processing to Screen Hospitalized Adults for Unhealthy Substance Use: Protocol for a Quasi-Experimental Design.
JMIR Res Protoc
; 11(12): e42971, 2022 Dec 19.
Artículo
en Inglés
| MEDLINE | ID: mdl-36534461
11.
The Addition of United States Census-Tract Data Does Not Improve the Prediction of Substance Misuse.
AMIA Annu Symp Proc
; 2021: 1149-1158, 2021.
Artículo
en Inglés
| MEDLINE | ID: mdl-35308901
12.
Investigating Unhealthy Alcohol Use As an Independent Risk Factor for Increased COVID-19 Disease Severity: Observational Cross-sectional Study.
JMIR Public Health Surveill
; 7(11): e33022, 2021 11 05.
Artículo
en Inglés
| MEDLINE | ID: mdl-34665758
13.
Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups.
J Am Med Inform Assoc
; 28(11): 2393-2403, 2021 10 12.
Artículo
en Inglés
| MEDLINE | ID: mdl-34383925
14.
External validation of an opioid misuse machine learning classifier in hospitalized adult patients.
Addict Sci Clin Pract
; 16(1): 19, 2021 03 17.
Artículo
en Inglés
| MEDLINE | ID: mdl-33731210
15.
Validation of an alcohol misuse classifier in hospitalized patients.
Alcohol
; 84: 49-55, 2020 05.
Artículo
en Inglés
| MEDLINE | ID: mdl-31574300
16.
Differences in length of stay and discharge destination among patients with substance use disorders: The effect of Substance Use Intervention Team (SUIT) consultation service.
PLoS One
; 15(10): e0239761, 2020.
Artículo
en Inglés
| MEDLINE | ID: mdl-33035229
17.
Subtypes in patients with opioid misuse: A prognostic enrichment strategy using electronic health record data in hospitalized patients.
PLoS One
; 14(7): e0219717, 2019.
Artículo
en Inglés
| MEDLINE | ID: mdl-31310611
18.
Development and application of a high throughput natural language processing architecture to convert all clinical documents in a clinical data warehouse into standardized medical vocabularies.
J Am Med Inform Assoc
; 26(11): 1364-1369, 2019 11 01.
Artículo
en Inglés
| MEDLINE | ID: mdl-31145455
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