Semantic Inference on Clinical Documents: Combining Machine Learning Algorithms With an Inference Engine for Effective Clinical Diagnosis and Treatment

• Published in: IEEE access Vol. 5; pp. 3529 - 3546
• Main Authors: Yang, Shuo, Wei, Ran, Guo, Jingzhi, Xu, Lida
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Big data; case-based reasoning; Clinical diagnosis; Cognition; Data exchange; Data mining; data stream mining; Data transmission; Decision support systems; decision tree; Decision trees; Diagnosis; disease detection; Diseases; electronic health record; Electronic health records; Health services; Human error; Incompatibility; Inference; Information systems; Machine learning; Medical diagnosis; Medical diagnostic imaging; medical record; semantic integration
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2017.2672975

Clinical practice calls for reliable diagnosis and optimized treatment. However, human errors in health care remain a severe issue even in industrialized countries. The application of clinical decision support systems (CDSS) casts light on this problem. However, given the great improvement in CDSS over the past several years, challenges to their wide-scale application are still present, including: 1) decision making of CDSS is complicated by the complexity of the data regarding human physiology and pathology, which could render the whole process more time-consuming by loading big data related to patients; and 2) information incompatibility among different health information systems (HIS) makes CDSS an information island, i.e., additional input work on patient information might be required, which would further increase the burden on clinicians. One popular strategy is the integration of CDSS in HIS to directly read electronic health records (EHRs) for analysis. However, gathering data from EHRs could constitute another problem, because EHR document standards are not unified. In addition, HIS could use different default clinical terminologies to define input data, which could cause additional misinterpretation. Several proposals have been published thus far to allow CDSS access to EHRs via the redefinition of data terminologies according to the standards used by the recipients of the data flow, but they mostly aim at specific versions of CDSS guidelines. This paper views these problems in a different way. Compared with conventional approaches, we suggest more fundamental changes; specifically, uniform and updatable clinical terminology and document syntax should be used by EHRs, HIS, and their integrated CDSS. Facilitated data exchange will increase the overall data loading efficacy, enabling CDSS to read more information for analysis at a given time. Furthermore, a proposed CDSS should be based on self-learning, which dynamically updates a knowledge model according to the data-stream-based upcoming data set. The experiment results show that our system increases the accuracy of the diagnosis and treatment strategy designs.