Knowledge discovery and data mining

Knowledge discovery and data mining

Knowledge Discovery and Data Mining is the process of automating computational and statistical analysis of biomedical datasets (data mining) to derive useful insight in diagnosis, therapy, and healthcare costs (knowledge discovery). It requires robust methods in such analytical stages as database management, data pre-processing, feature selection, inference and prior knowledge, model selection, visualization, and results analysis. Mining and discovery techniques can be applied in medicine to improve patient's health and hospital operation, such as case-based retrieval for patient treatment, anomaly detection of doctor performance, and adverse drug effects.

This interdisciplinary field involves collaboration between clinical, biomedical, and computer science team members and addresses diverse areas including machine learning, pattern recognition, database science, statistics and analytics, artificial intelligence, knowledge, data modeling and visualization, and high performance computing. The overall goal of knowledge discovery and data mining is the extraction of patterns and rules, turning low-level information into high-level knowledge that impacts healthcare.

Past Projects: