天美传媒

Abstract

Hidden Markov Models (HMMs) have emerged as a fundamental statistical tool in medical research, offering robust capabilities for modeling temporal processes characterized by unobservable hidden states. This review discusses the evolution and application of various HMM types in the medical field, including discrete, continuous, Hidden Semi-Markov, Hierarchical, and Coupled HMMs. These models have been used to address challenges across diverse medical contexts, from diagnosing disease states to tracking the progression of chronic conditions such as Alzheimer’s and cardiovascular diseases. We outline the structured methodological framework necessary to build HMMs for medical applications, emphasizing the importance of data pre-processing, feature extraction, parameter estimation, and model validation. The utility of HMMs in modeling continuous physiological signals, such as EEG, ECG and MRI data is highlighted, demonstrating their relevance in personalized medicine and disease progression modeling. Evaluation metrics, including accuracy, sensitivity and specificity are discussed in relation to the model’s clinical applicability and predictive power. The review concludes with a discussion of emerging trends in the use of HMMs, particularly their growing importance in genomics, pharmacometrics and infection transmission modeling. This comprehensive analysis underscores the versatility of HMMs in addressing complex temporal medical phenomena and their potential to enhance diagnostic, prognostic and therapeutic strategies in the future of healthcare.

Keywords: Hidden Markov Models; Medical; Time series; Artificial intelligence; Genetics