Predicting Stroke from Electronic Health Records

04/25/2019
by   Chidozie Shamrock Nwosu, et al.
0

Studies have identified various risk factors associated with the onset of stroke in an individual. Data mining techniques have been used to predict the occurrence of stroke based on these factors by using patients' medical records. However, there has been limited use of electronic health records to study the inter-dependency of different risk factors of stroke. In this paper, we perform an analysis of patients' electronic health records to identify the impact of risk factors on stroke prediction. We also provide benchmark performance of the state-of-art machine learning algorithms for predicting stroke using electronic health records.

READ FULL TEXT
research
03/01/2022

A predictive analytics approach for stroke prediction using machine learning and neural networks

The negative impact of stroke in society has led to concerted efforts to...
research
03/24/2022

Adverse Health Correlates of Intimate Partner Violence against Older Women: Mining Electronic Health Records

Intimate partner violence (IPV) is often studied as a problem that predo...
research
10/09/2020

Identifying Risk of Opioid Use Disorder for Patients Taking Opioid Medications with Deep Learning

The United States is experiencing an opioid epidemic, and there were mor...
research
11/19/2019

Examining the impact of data quality and completeness of electronic health records on predictions of patients risks of cardiovascular disease

The objective is to assess the extent of variation of data quality and c...
research
07/03/2019

High-Throughput Machine Learning from Electronic Health Records

The widespread digitization of patient data via electronic health record...

Please sign up or login with your details

Forgot password? Click here to reset