Abstract
ObjectiveThe reasonable classification of a large number of distinct diagnosis codes can clarify patient diagnostic information and help clinicians to improve their ability to assign and target treatment for primary diseases. Our objective is to identify and predict a unifying diagnosis (UD) from electronic medical records (EMRs).MethodsWe screened 4,418 sepsis patients from a public MIMIC-III database and extracted their diagnostic information for UD identification, their demographic information, laboratory examination information, chief complaint, and history of present illness information for UD prediction. We proposed a data-driven UD identification and prediction method (UDIPM) embedding the disease ontology structure. First, we designed a set similarity measure method embedding the disease ontology structure to generate a patient similarity matrix. Second, we applied affinity propagation clustering to divide patients into different clusters, and extracted a typical diagnosis code co-occurrence pattern from each cluster. Furthermore, we identified a UD by fusing visual analysis and a conditional co-occurrence matrix. Finally, we trained five classifiers in combination with feature fusion and feature selection method to unify the diagnosis prediction.ResultsThe experimental results on a public electronic medical record dataset showed that the UDIPM could extracted a typical diagnosis code co-occurrence pattern effectively, identified and predicted a UD based on patients' diagnostic and admission information, and outperformed other fusion methods overall.ConclusionsThe accurate identification and prediction of the UD from a large number of distinct diagnosis codes and multi-source heterogeneous patient admission information in EMRs can provide a data-driven approach to assist better coding integration of diagnosis.
Highlights
In medical practice, clinicians are encouraged to seek a unifying diagnosis (UD) that could explain all the patient’s signs and symptoms in preference to providing several explanations for the distress being presented [1]
Artificial intelligence and big data analytic technology have been successfully applied to clinical diagnostic procedures and treatment regimen recommendation, which has resulted in new opportunities for intelligent clinical decision support systems that use data-driven knowledge discovery methods [7,8,9,10]
We proved that defining the core zone of a cluster is an effective approach to extract stable clustering results [35]
Summary
Clinicians are encouraged to seek a unifying diagnosis (UD) that could explain all the patient’s signs and symptoms in preference to providing several explanations for the distress being presented [1]. To increase the accuracy of a UD, enhancing individual clinicians’ diagnostic reasoning skills and improving health care systems are regarded as two important approaches to support clinicians through the diagnostic process. The former requires professional knowledge training and lifelong learning, whereas the latter mainly involves the development of information technology [3]. Artificial intelligence and big data analytic technology have been successfully applied to clinical diagnostic procedures and treatment regimen recommendation, which has resulted in new opportunities for intelligent clinical decision support systems that use data-driven knowledge discovery methods [7,8,9,10]
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