Online adaptive method for disease prediction based on big data of clinical laboratory test

To better utilize the medical data in electronic medical records (EMR), this study aims to present an online adaptive method for disease prediction based on the medical data of clinical laboratory test (CLT) items stored in EMR. We firstly extract the diagnosis and CLT items information from the system, and then divide the CLT items into three categories to establish the patterns of CLT items, which are subsequently used for the selection of candidate diseases. A binary relevance approach based on logistic sparse group lasso method is finally used for disease prediction. Four groups of 21,288 patients with diagnosis of chronic hepatitis, hyperuricemia, hyperlipidemia and random diseases are used to test the performance of our method. Results show that the accuracy and recall for these four groups are all above 70%. As a primary attempt to practice intelligent healthcare, this model may have the potential values of computer-aided diagnosis. Further studies are suggested to combine CLT with other types of EMR data to further improve the prediction performance.