Knowledge-Based System for Orthopedic Pediatric Disorders

摘要

Knowledge-based system has become a useful tool to help clinicians in their diagnosis and decision-making during their routine practices. The objective of this present study was to develop a knowledge-based system applied to orthopedic pediatric disorders (rotational abnormalities, club-foot deformities and children with cerebral palsy). Based on patient data, two tree-based data mining approaches were studied to develop mathematical predictive models. The first one related to a deterministic standard decision tree model derived from one-dimensional morphological patient data. The second one deals with a belief decision tree model derived from high multi-dimensional motion capture data (kinetics (reaction forces) and muscle activities (EMG signals)). As results, deterministic predictive diagnostic models of rotational abnormalities and clubfoot deformities were presented and analyzed. On the other hand, a belief classification model of children with cerebral palsy was presented. We found that reaction forces and muscle activities of rectus femoris are main discriminators for recognizing diplegia and hemiplegia cases. Moreover, the belief level of each classification result was also calculated and presented to take uncertain and imprecise characters of input data into account. Rule-based sets extracted from decision trees to develop diagnosis/classification algorithms were also presented. Validation of developed predictive models was performed on real patient cases. Web-based interfaces including step-by-step diagnosis, treatment and monitoring processes were also presented. To conclude, tree-based data mining approaches demonstrated their flexibilities and performances in developing deterministic and belief predictive models. Furthermore, they showed strong generalization ability to interpret the classification results. In fact, clinicians could use our knowledge-based system to take appropriate medical decisions in an efficient and convivial manner.