Predicting Hormonal Therapy Medication Discontinuation for Breast Cancer Patients using Structured Data in Electronic Medical Records.
AMIA Jt Summits Transl Sci Proc
; 2022: 359-368, 2022.
Article
in En
| MEDLINE
| ID: mdl-35854721
ABSTRACT
Hormonal therapy (HT) reduces the risk of cancer recurrence and the mortality rate for patients with hormone-receptor-positive breast cancer. However, it is estimated that half of the patients fail to complete the standard 5-year adjuvant treatment protocol. We investigate the extent to which certain types of structured data in electronic medical records (EMRs), namely conditions, drugs, laboratory tests and procedures, as well as when such data is entered EMRs, can forecast HT discontinuation. Our experiments with EMR data from 2,251 patients showed that machine learning models based on these data types achieve fair performance (AUC of 0.65). More importantly, the performance was not statistically significantly different when fitting a model using all or only one feature type, suggesting that the model is robust to missing information in the EMR.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Guideline
/
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
AMIA Jt Summits Transl Sci Proc
Year:
2022
Document type:
Article