scholarly journals On using electronic health records to improve optimal treatment rules in randomized trials

Biometrics ◽  
2020 ◽  
Vol 76 (4) ◽  
pp. 1075-1086
Author(s):  
Peng Wu ◽  
Donglin Zeng ◽  
Haoda Fu ◽  
Yuanjia Wang
Keyword(s):  
Electronic Health Records ◽  
Randomized Trials ◽  
Optimal Treatment ◽  
Health Records ◽  
Electronic Health ◽  
Treatment Rules

scholarly journals Predicting optimal treatment regimens for patients with HR+/HER2- breast cancer using machine learning based on electronic health records

2021 ◽  
Vol 10 (9) ◽  
pp. 777-795
Author(s):  
Zhanglin Lin Cui ◽  
Zbigniew Kadziola ◽  
Ilya Lipkovich ◽  
Douglas E Faries ◽  
Kristin M Sheffield ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Electronic Health Records ◽  
Metastatic Breast ◽  
Optimal Treatment ◽  
Health Records ◽  
Treatment Regimens ◽  
Her2 Breast Cancer ◽  
Electronic Health ◽  
Line Of Therapy

Aim: To predict optimal treatments maximizing overall survival (OS) and time to treatment discontinuation (TTD) for patients with metastatic breast cancer (MBC) using machine learning methods on electronic health records. Patients/methods: Adult females with HR+/HER2- MBC on first- or second-line systemic therapy were eligible. Random survival forest (RSF) models were used to predict optimal regimen classes for individual patients and each line of therapy based on baseline characteristics. Results: RSF models suggested greater use of CDK4 & 6 inhibitor-based therapies may maximize OS and TTD. RSF-predicted optimal treatments demonstrated longer OS and TTD compared with nonoptimal treatments across line of therapy (hazard ratios = 0.44∼0.79). Conclusion: RSF may help inform optimal treatment choices and improve outcomes for patients with HR+/HER2- MBC.

Electronic health records: Context matters!

2016 ◽  
Vol 34 (2) ◽  
pp. 163-165 ◽  
Author(s):  
William B. Ventres ◽  
Richard M. Frankel
Keyword(s):  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health
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