Machine Learning for Clinical Pattern Discovery

Machine learning has emerged as an influential tool supporting various aspects of clinical decision making. By analyzing large cohorts of patients, machine-learning models can detect subtle patterns that may escape human perception. Such patterns can have particular value for diagnosis, prognosis, and treatment outcome prediction, as they delineate groups of patients requiring the same procedure, sharing similar risks, or benefiting or worsening from a specified treatment. Modeling diagnostic or therapeutic pathways expands this potential: discovering groups of patients following similar sequences leading to different outcomes can guide clinicians toward more effective pathways.