Leveraging Comprehensive Health Records for Breast Cancer Risk Prediction: A Binational Assessment.
AMIA Annu Symp Proc
; 2022: 385-394, 2022.
Article
em En
| MEDLINE
| ID: mdl-37128397
ABSTRACT
Breast cancer (BC) risk models based on electronic health records (EHR) can assist physicians in estimating the probability of an individual with certain risk factors to develop BC in the future. In this retrospective study, we used clinical data combined with machine learning tools to assess the utility of a personalized BC risk model on 13,786 Israeli and 1,695 American women who underwent screening mammography in the years 2012-2018 and 2008-2018, respectively. Clinical features were extracted from EHR, personal questionnaires, and past radiologists' reports. Using a set of 1,547 features, the predictive ability for BC within 12 months was measured in both datasets and in sub-cohorts of interest. Our results highlight the improved performance of our model over previous established BC risk models, their ultimate potential for risk-based screening policies on first time patients and novel clinically relevant risk factors that can compensate for the absence of imaging history information.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
Tipo de estudo:
Diagnostic_studies
/
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Limite:
Female
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Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article