Knowledge discovery with medical databases: A case-based reasoning approach

Medical informatics projects are accumulating enormous numbers of clinical cases in hospital information systems. Efficient extraction of clinically useful knowledge patterns from these clinical databases to improve health care quality is a challenging research topic. Though the progress in Knowledge Discovery in Databases (KDD) provides a basis for medical data mining development, the characteristics of the medical practice requires an unique medical knowledge exploration process. In patient care, physicians utilize knowledge extracted from basic principles and cases they have experienced. In medical education and practice, the Case-Based, or problem-based learning and reasoning approaches are widely used. Integrating Case-Based Reasoning (CBR) principles in Knowledge Discovery with Medical Databases (KDMD) development is intuitive. The hypothesis of this research is that by combining the CBR paradigms, KDD principles, and clinicians' expertise, the knowledge patterns extracted from clinical databases can be utilized to improve health care quality.; A KDMD working model is proposed to test the hypothesis. Three basic phases: goal and data discovery, knowledge exploration, and knowledge refinement are introduced. In this working model, clinicians can express their concerns and preferences to guide knowledge exploration from the data. When applying the derived knowledge patterns in clinical work, clinicians can further justify the decision support information and then refine the scope of the knowledge with the help of CBR paradigms.; To achieve this objective, a KDMD support system called MIKE (Medical Interactive Knowledge Explorer) has been developed. The knowledge exploration examples in this research manifest how the system learned from both clinicians' expertise and evidence in the data. Tests using breast cancer data shows that the expert-guided decision tree construction strategy + combined with case similarity assessment outperformed pure inductive learning methods. An application of the working model on coronary artery disease verified the functional proficiency of MIKE. The plot of the learning curve after each training session demonstrates the incremental knowledge discovered. Using clinical data on difficult airway prediction, MIKE yields 58% sensitivity, compared to current rule-based airway risk alert algorithm (36% sensitivity) and the other airway evaluation methods, such as the Mallampati test and the Wilson Risk-Sum ({dollar}<{dollar}50% sensitivity). The improvement from this trial demonstrated that the working model is capable of increasing the predictability of difficult airways versus anesthesiologists rule based methods. Furthermore, the medical knowledge discovery working model should be applicable to many different data and experience rich fields.