Intelligent Clinical Decision Systems Using Machine Learning

Paolo Soda

Intelligent Clinical Decision Systems Using Machine Learning

Author(s)	Prof. Paolo Soda
Country	Italy
Abstract	Intelligent clinical decision systems using machine learning have emerged as important tools for supporting healthcare professionals in evidence-based diagnosis, risk assessment, treatment planning, and patient management. Machine-learning models can process large volumes of clinical information, identify patterns, estimate risks, and provide decision-support outputs that may assist professionals in complex healthcare environments. These systems can incorporate electronic health records, laboratory findings, medical imaging, physiological measurements, medication information, and patient histories. This study examines intelligent clinical decision systems using machine learning, focusing on machine-learning methods, clinical data processing, predictive decision support, healthcare applications, and implementation requirements. A qualitative descriptive and conceptual research design was adopted using secondary academic literature. The analysis indicates that machine-learning-based decision systems can support disease prediction, clinical risk assessment, patient stratification, diagnosis, treatment planning, and resource management. However, effective implementation depends on data quality, model accuracy, interpretability, interoperability, cybersecurity, privacy, clinical validation, and professional oversight. Challenges include biased datasets, incomplete clinical information, model generalizability, technical complexity, workflow integration, and potential overreliance on automated recommendations. The study concludes that machine-learning-based clinical decision systems can strengthen healthcare decision-making when appropriately validated and integrated with professional expertise. Future research should emphasize explainable artificial intelligence, multimodal clinical data, real-time decision support, federated learning, personalized prediction, continuous model monitoring, and clinically meaningful evaluation.
Keywords	Machine Learning, Clinical Decision Systems, Clinical Decision Support, Artificial Intelligence, Healthcare Analytics, Predictive Modeling, Medical Diagnosis, Risk Assessment, Health Informatics, Intelligent Healthcare.
Field	Engineering
Published In	Volume 5, Issue 5, September-October 2023
Published On	2023-09-26

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American International Journal of Advanced Research and Development

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Intelligent Clinical Decision Systems Using Machine Learning

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