Using machine learning to develop a clinical prediction model for SSRI-associated bleeding: a feasibility study.
BMC Med Inform Decis Mak
; 23(1): 105, 2023 06 11.
Article
em En
| MEDLINE
| ID: mdl-37301967
ABSTRACT
INTRODUCTION:
Adverse drug events (ADEs) are associated with poor outcomes and increased costs but may be prevented with prediction tools. With the National Institute of Health All of Us (AoU) database, we employed machine learning (ML) to predict selective serotonin reuptake inhibitor (SSRI)-associated bleeding.METHODS:
The AoU program, beginning in 05/2018, continues to recruit ≥ 18 years old individuals across the United States. Participants completed surveys and consented to contribute electronic health record (EHR) for research. Using the EHR, we determined participants who were exposed to SSRIs (citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, vortioxetine). Features (n = 88) were selected with clinicians' input and comprised sociodemographic, lifestyle, comorbidities, and medication use information. We identified bleeding events with validated EHR algorithms and applied logistic regression, decision tree, random forest, and extreme gradient boost to predict bleeding during SSRI exposure. We assessed model performance with area under the receiver operating characteristic curve statistic (AUC) and defined clinically significant features as resulting in > 0.01 decline in AUC after removal from the model, in three of four ML models.RESULTS:
There were 10,362 participants exposed to SSRIs, with 9.6% experiencing a bleeding event during SSRI exposure. For each SSRI, performance across all four ML models was relatively consistent. AUCs from the best models ranged 0.632-0.698. Clinically significant features included health literacy for escitalopram, and bleeding history and socioeconomic status for all SSRIs.CONCLUSIONS:
We demonstrated feasibility of predicting ADEs using ML. Incorporating genomic features and drug interactions with deep learning models may improve ADE prediction.Palavras-chave
Texto completo:
1
Eixos temáticos:
Pesquisa_clinica
Base de dados:
MEDLINE
Assunto principal:
Inibidores Seletivos de Recaptação de Serotonina
/
Saúde da População
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Adolescent
/
Humans
País como assunto:
America do norte
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article