An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review

Abstract

Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We therefore aimed to present predictive models based on machine learning (ML) techniques in HF patients that were externally validated. We searched four databases and the reference lists of the included papers to identify studies in which HF patient data were used to create a predictive model. Literature screening was conducted in Academic Search Ultimate, ERIC, Health Source Nursing/Academic Edition and MEDLINE. The protocol of the current systematic review was registered in the PROSPERO database with the registration number CRD42022344855. We considered all types of outcomes: mortality, rehospitalization, response to treatment and medication adherence. The area under the receiver operating characteristic curve (AUC) was used as the comparator parameter. The literature search yielded 1649 studies, of which 9 were included in the final analysis. The AUCs for the machine learning models ranged from 0.6494 to 0.913 in independent datasets, whereas the AUCs for statistical predictive scores ranged from 0.622 to 0.806. Our study showed an increasing number of ML predictive models concerning HF populations, although external validation remains infrequent. However, our findings revealed that ML approaches can outperform conventional risk scores and may play important role in HF management.

Diagnostic Deep Learning Framework for Heart Failure

“…This approach minimized the risk of misdiagnosis and ensured a robust diagnosis of HF. (19)(20)(21)(22) Our study investigated the performance of the HFNet model in predicting imaging ndings of pulmonary edema, cardiomegaly, and pleural effusion. We found that as learning progressed, the model achieved high prediction performance for cardiomegaly, with an AUC of 1.00, and for pulmonary edema, with an AUC of 0.96, indicating high accuracy in distinguishing between positive and negative cases.…”

Machine Learning Approach to Understand Worsening Renal Function in Acute Heart Failure

“…The ML-based analysis is gaining popularity in cardiovascular research [ 19 ]. There were some magnificent attempts to implement ML in the HF population [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ].…”

Will Artificial Intelligence Provide Answers to Current Gaps and Needs in Chronic Heart Failure?

“…Wearable devices and remote monitoring will change the management of these patients, allowing a higher degree of personalisation, taking into account multiple individual characteristics such as genetics, lifestyle, and health history. Some of the ongoing projects are the AI-Powered Heart Failure Management System (AIHFMS) [16], and The Heart Failure Prediction with Deep Learning project [69], which aim at developing a deep learning algorithm to predict the risk of heart failure progression based on electronic health record data and wearable device data and based on imaging and lab results respectively [22]. Another example is the Cardihab project, a digital health platform developed by researchers at the University of Sydney that uses AI to personalise heart failure management.…”

“…Predictive modelling: to support decision-making and improve patient management, it is advisable to improve the prediction of HF progression and to define the underlying etiologies [22].…”