Predictive Modelling of Clinical Deterioration Using Sequential Laboratory Trends with Machine Learning Techniques

Abstract

Objective: To summarize the role of sequential laboratory trends and machine learning techniques in predicting clinical deterioration among hospitalized patients.

Place and Duration of Study: This study was conducted as a narrative review article from June 2024 to April 2025.

Methods: Relevant literature was searched through PubMed, Google Scholar, Scopus, Web of Science, and IEEE Xplore which was published till April 2025. Evidence regarding the predictive value of serial laboratory
measurements and the performance of various machine learning algorithms was synthesized.

Results: Sequential changes in laboratory parameters, including serum creatinine, lactate, white blood cell count, platelet count, electrolytes, inflammatory markers, and arterial blood gases, were identified as important early indicators of worsening organ dysfunction and impending clinical deterioration. Machine learning approaches, such as logistic regression, random forest, gradient boosting, support vector machines, artificial neural networks, and recurrent neural networks, demonstrated the ability to identify complex temporal patterns and improve prediction
accuracy compared with conventional risk assessment methods.

Conclusion: Predictive modelling based on sequential laboratory trends and machine learning techniques represents a promising strategy for the early detection of clinical deterioration in hospitalized patients. Further research focusing on robust validation, interpretability, and seamless clinical integration is required to optimize patient outcomes and enhance healthcare safety.