Similarity-based prediction of Ejection Fraction in Heart Failure Patients

by   Jamie Wallis, et al.

Biomedical research is increasingly employing real world evidence (RWE) to foster discoveries of novel clinical phenotypes and to better characterize long term effect of medical treatments. However, due to limitations inherent in the collection process, RWE often lacks key features of patients, particularly when these features cannot be directly encoded using data standards such as ICD-10. Here we propose a novel data-driven statistical machine learning approach, named Feature Imputation via Local Likelihood (FILL), designed to infer missing features by exploiting feature similarity between patients. We test our method using a particularly challenging problem: differentiating heart failure patients with reduced versus preserved ejection fraction (HFrEF and HFpEF respectively). The complexity of the task stems from three aspects: the two share many common characteristics and treatments, only part of the relevant diagnoses may have been recorded, and the information on ejection fraction is often missing from RWE datasets. Despite these difficulties, our method is shown to be capable of inferring heart failure patients with HFpEF with a precision above 80 containing 11,950 and 10,051 heart failure patients. This is an improvement when compared to classical approaches such as logistic regression and random forest which were only able to achieve a precision < 73 approach allows us to analyse which features are commonly associated with HFpEF patients. For example, we found that specific diagnostic codes for atrial fibrillation and personal history of long-term use of anticoagulants are often key in identifying HFpEF patients.


page 4

page 10

page 14


Long-term adherence to polytherapy in heart failure patients: a novel approach emphasising the importance of secondary prevention

Heart failure (HF) is a severe and costly clinical syndrome associated w...

Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure

Heart failure is a syndrome which occurs when the heart is not able to p...

Analysis and prediction of heart stroke from ejection fraction and serum creatinine using LSTM deep learning approach

The combination of big data and deep learning is a world-shattering tech...

Computer Assisted Localization of a Heart Arrhythmia

We consider the problem of locating a point-source heart arrhythmia usin...

A point-wise linear model reveals reasons for 30-day readmission of heart failure patients

Heart failures in the United States cost an estimated 30.7 billion dolla...

Automated Identification of Drug-Drug Interactions in Pediatric Congestive Heart Failure Patients

Congestive Heart Failure, or CHF, is a serious medical condition that ca...

Please sign up or login with your details

Forgot password? Click here to reset