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Background Automated clinical decision support for risk assessment is a powerful tool in combating cardiovascular disease (CVD), enabling targeted early intervention that could avoid issues of overtreatment or undertreatment. However, current CVD risk prediction models use observations at baseline without explicitly representing patient history as...
Citations
... Hsu et al. [11] proposed a methodology for cardiovascular disease-related event detection using recurrent neural networks. The research work was carried out using a 2-year observation and 5-year prediction window. ...
Cardiovascular disease (CVDs) is a rapidly rising global concern due to unhealthy diets, lack of physical activity, and other factors. According to the World Health Organization (WHO), primary risk factors include elevated blood pressure, glucose, blood lipids, and obesity. Recent research has focused on accurate and timely disease prediction to reduce risk and fatalities, often relying on predictive models trained on large datasets, which require intensive training. An intelligent system for CVDs patients could greatly assist in making informed decisions by effectively analyzing health parameters. CEP has emerged as a valuable method for solving real-time challenges by aggregating patterns of interest and their causes and effects on end users. In this work, a fuzzy rule-based system is proposed for monitoring clinical data to provide real-time decision support. A fuzzy rule based on clinical and WHO standards ensures accurate predictions. The integrated approach uses Apache Kafka and Spark for data streaming, and the Siddhi CEP Engine for event processing. Additionally, numerous cardiovascular disease-related parameters are passed through CEP Engine to ensure fast and reliable prediction decisions. To validate the effectiveness of the approach, simulation is done with real-time, unseen data to predict cardiovascular disease. Using synthetic data (1000 samples), and categorized it into "Very Low Risk, Low Risk, Medium Risk, High Risk, and Very High Risk." Validation results showed that 20% of samples were categorized as very low risk, 15–45% as low risk, 35–65% as medium risk, 55–85% as high risk, and 75% as very high risk.
... This problem of dealing with long-range dependencies was overcome with the development of RNNs including a long short-term memory (LSTM) hidden unit that remembers the activation patterns of hidden layers. This allows significant events from the distant past to be recalled and unimportant events to be forgotten when making current predictions [62]. Within the context of healthcare, LSTM networks retain the sequential information from patient histories making them especially suitable for longterm forecasting using EHR data. ...
The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.
