Using machine learning to reduce unnecessary rehospitalization of cardiovascular patients in Saudi Arabia

Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism

Deep learning has shown great promise in predicting Atrial Fibrillation using ECG signals and other vital signs. However, a major hurdle lies in the privacy concerns surrounding these datasets, which often contain sensitive patient information. Balancing accurate AFib prediction with robust user privacy remains a critical challenge to address. We suggest Federated Learning , a privacy-preserving machine learning technique, to address this privacy barrier. Our approach makes use of FL by presenting Fed-CL, a advanced method that combines Long Short-Term Memory networks and Convolutional Neural Networks to accurately predict AFib. In addition, the article explores the importance of analysing mean heart rate variability to differentiate between healthy and abnormal heart rhythms. This combined approach within the proposed system aims to equip healthcare professionals with timely alerts and valuable insights. Ultimately, the goal is to facilitate early detection of AFib risk and enable preventive care for susceptible individuals.

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

Background

Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care.

Methods

We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data.

Results

Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health.

Conclusions

Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.

The Association Between Medication Non-Adherence and Early and Late Readmission Rates for Patients with Acute Coronary Syndrome

Purpose

Unplanned hospital readmission forms costly, but preventable burdens on healthcare system. This study was designed to evaluate cardiovascular-related readmission rate after discharge of acute coronary syndrome (ACS) patients and its relationship with medication adherence at a university hospital, Saudi Arabia.

Methods

A total of 370 consecutive patients presenting with ACS were involved. The inclusion criteria were clinical and coronary angiography diagnostic data of ACS. Exclusion criteria included heart valve disease, myocarditis, hepatic disease, and history of acute infection during the previous two weeks. Patients were divided into index admission group (n = 291) and unplanned readmission group (n = 79). Readmission and medication adherence rates were evaluated during 1–30, 31–180, 181–365, and 366–548 days post-ACS discharge. Medication adherence was estimated with a (yes/no) questionnaire.

Results

The overall readmission rate was 21.4%; individual rates were 30.4%, 38.0%, 27.8%, and 3.8% and the overall medication adherence rate was 62.03%, while individual rates were 54.2%, 70.0%, 63.6%, and 33.3% during the four periods, respectively. There were strong correlations between medication non-adherence and readmission rates. Heart failure, ST-elevated myocardial infarction, unstable angina, cerebrovascular accident, and arrhythmia represented the top causes. Body mass index was higher in readmission group. There were significant correlations between smoking, hypertension, cerebrovascular accident, ischemic heart disease, previous stent, instent restenosis, and LDL-cholesterol as predictor factors and readmission rate.

Conclusion

The cardiovascular-related unplanned readmission rate post-ACS discharge was 21.4%, and medication non-adherence rate was 37.97%. There were strong correlations between them in the time frames from 1-month to 1.5-year post-discharge. The individual rates decreased by time, but the first month showed lower rates than the following 5 months and this indicated the role of factors other than medication non-adherence in readmission. The rates are generally consistent with the international levels but utilizing technology can further improve medication adherence and reduce readmission rates.

A hybrid approach for risk stratification and predictive modelling of 30-days unplanned readmission of comorbid patients with diabetes

OBJECTIVES

When comorbid patients with diabetes have 30-days Unplanned Readmission (URA), they attract more burdens to the healthcare system due to increased cost of treatment, insurance penalties to hospitals, and unavailable bed spaces for new patients. This paper, therefore, aims to develop a risk stratification and a predictive model for identifying patients at various risk severities of 30-days URA.

METHODS

Patients records of comorbid patients with diabetes treated with different medications were collected from different hospitals and analysed with Principal Component Analysis (PCA) and Multivariate Logistic Regression (MLR) to determine the probability of 30-days URA, which is classified into very low, low, moderate, high, and very high. The risk classes are later modelled using ANOVA feature selection to identify the optimal predictors and the best random forest (RF) hyperparameters for 30-days URA risk stratification. Synthetic Minority Oversampling Technique (SMOTE) was used to balance the risk classes while employing a10-fold cross-validation.

RESULTS

After analysing 17,933 episodes of comorbid diabetes patients' treatment, 10.71% are identified to have 30-days URA with 61.95% of patients at moderate risk, 35.5% at low risk, 2.25% at very low risk, 0.37% at high risk, and 0.08% at very high risk. The predictive accuracy of RF is: - recall: 0.947 ± 0.035, precision: 0.951 ± 0.033, F1-score: 0.947 ± 0.035, AUC: 0.994 ± 0.007 and Average Precision (AP) of 0.99. The predictive accuracies of the risk classes measured with F1-score are: - very low: 0.985 ± 0.019, low risk: 0.871 ± 0.079, moderate: 0.881 ± 0.093, high: 0.999 ± 0.003, and very high: 1.000 ± 0.00.

CONCLUSION

This study identified the risk severity of comorbid patients with diabetes treated with different medications, making it easier to identify those that will be prioritized on hospitalization to minimize 30-days URA. By relying on the technique developed, vulnerable patients to 30-days URA can be given better post-discharge monitoring to build critical self-management skills that will minimize the cost of diabetes care and improve the quality of life.