Extraction of Treatment Information From Electronic Health Records and Evaluation of Testosterone Recovery in Patients With Prostate Cancer.
Sunny GuinTomi JunVaibhav G PatelKristin L AyersMatthew DeitzYuqin CaiXiang ZhouChe-Kai TsaoWilliam K OhRong ChenBobby C LiawPublished in: JCO clinical cancer informatics (2022)
We augmented structured electronic medical record data with data extracted from notes and improved the accuracy of medication information for patients. ADT exposure and T-recovery in patients with LPC produced results consistent with the literature and clinical experience and illustrates the power of applying machine learning methods to enhance the quality of real-world evidence in answering clinically relevant questions.
Keyphrases
- electronic health record
- prostate cancer
- machine learning
- adverse drug
- clinical decision support
- end stage renal disease
- big data
- newly diagnosed
- ejection fraction
- chronic kidney disease
- systematic review
- healthcare
- prognostic factors
- radical prostatectomy
- replacement therapy
- emergency department
- patient reported outcomes
- data analysis
- smoking cessation