Predicting Effectiveness of Antihypertensive Medications for Heart Failure based on Longitudinal Patient Records and Deep Learning

Drug treatment for heart failure (HF) condition includes different medications. As patients could respond variably to a particular medication, being able to predict drug effectiveness is crucial for personalized treatment. Laboratory tests in EHR summarize different aspects of the patient’s physiological process related to a diagnosis, where blood pressure (BP) is deemed a critical hemodynamic parameter for HF prognosis. This work first proposes a novel method based on combinations of different clinical end points to generate the positive and negative samples corresponding to HF patients on whom the drug is effective and not effective respectively. We then formulate drug effectiveness prediction as a time series classification problem and experiment with several deep learning models, leveraging the temporal BP laboratory measurements from EHR as the features. Over thorough comparative evaluations among 3 categories of HF medications and two types of lab features, we achieved the best F1 performance of ∼0.97. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement National Institute of Health (NIH) NIGMS (R00GM135488) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of Mayo Clinic gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data used in the present study are PHI so cannot be make publicly available.