Key result
LSTM-based deep learning model using electronic health records shows increasing capability to predict heart failure.
Why the study?
Existing methods like SVM predicted heart failure rates without relating objective factors, prompting the development of an LSTM predictive model using clinical variables from EHR data.
Observational (n=299)
Yes
An LSTM-based deep learning model utilizing electronic health records can be applied to predict heart failure trends.
LSTM EHR models may aid HF prediction; hypothesis-generating and requires prospective validation before any clinical role.
Combining big data and deep learning is a world-shattering technology that can significantly impact any objective if appropriately used. With a large volume of available healthcare datasets and progressions in deep learning techniques, systems are now well-equipped to predict the future trend of any health problems. From the literature survey, we found that the SVM was used to predict the heart failure rate without relating objective factors. Utilizing the intensity of important historical information in electronic health records (EHR), we have built a smart and predictive model utilizing long short-term memory (LSTM) and incorporating correlation between clinical variables. Hence, the fundamental commitment of this work is to predict the failure of the heart using an LSTM based on the patient’s electronic medicinal information. We have analyzed a dataset containing the medical records of 299 heart failure patients collected at the Faisalabad Institute of Cardiology and the Allied Hospital in Faisalabad (Punjab, Pakistan). We have found an increasing trend in our analysis which will contribute to advancing the knowledge in the field of heart stroke prediction.
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Haque et al. (2023) conducted an observational in Heart failure (n=299). Long short-term memory (LSTM) deep learning model was evaluated on Prediction of heart failure. An LSTM-based deep learning model utilizing electronic health records from 299 patients was developed to predict heart failure, demonstrating an increasing trend in predictive capability.
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