The Journal of Machine Learning Research
Concept-based electronic health records: opportunities and challenges
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Toward personalized care management of patients at risk: the diabetes case study
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Rao-blackwellised particle filtering for dynamic Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Towards heterogeneous temporal clinical event pattern discovery: a convolutional approach
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Supervised patient similarity measure of heterogeneous patient records
ACM SIGKDD Explorations Newsletter
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The fast development of hospital information systems (HIS) produces a large volume of electronic medical records, which provides a comprehensive source for exploratory analysis and statistics to support clinical decision-making. In this paper, we investigate how to utilize the heterogeneous medical records to aid the clinical treatments of diabetes mellitus. Diabetes mellitus, simply diabetes, is a group of metabolic diseases, which is often accompanied with many complications. We propose a Symptom-Diagnosis-Treatment model to mine the diabetes complication patterns and to unveil the latent association mechanism between treatments and symptoms from large volume of electronic medical records. Furthermore, we study the demographic statistics of patient population w.r.t. complication patterns in real data and observe several interesting phenomena. The discovered complication and treatment patterns can help physicians better understand their specialty and learn previous experiences. Our experiments on a collection of one-year diabetes clinical records from a famous geriatric hospital demonstrate the effectiveness of our approaches.