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Semantic Inference on Clinical Documents: Combining Machine Learning Algorithms with an Inference Engine for Effective Clinical Diagnosis and Treatment
Shuo Yang1; Ran Wei2; Jingzhi Guo1; Lida Xu3
2017
Source PublicationIEEE Access
ISSN21693536
Volume5Pages:3529-3546
Abstract

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.

KeywordBig Data Case-based Reasoning Clinical Diagnosis Data Stream Mining Decision Tree Disease Detection Electronic Health Record Medical Record Semantic Integration
DOIhttp://doi.org/10.1109/ACCESS.2017.2672975
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000397809900071
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Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorShuo Yang
Affiliation1.Faculty of Science and Technology, University of Macau, Taipa 999078, China
2.Department of Microbiology, Rutgers University, Newark, NJ 07103 USA
3.Department of Information Technology and Decision Sciences, Old Dominion University, Norfolk, VA 23529 USA
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Shuo Yang,Ran Wei,Jingzhi Guo,et al. Semantic Inference on Clinical Documents: Combining Machine Learning Algorithms with an Inference Engine for Effective Clinical Diagnosis and Treatment[J]. IEEE Access,2017,5:3529-3546.
APA Shuo Yang,Ran Wei,Jingzhi Guo,&Lida Xu.(2017).Semantic Inference on Clinical Documents: Combining Machine Learning Algorithms with an Inference Engine for Effective Clinical Diagnosis and Treatment.IEEE Access,5,3529-3546.
MLA Shuo Yang,et al."Semantic Inference on Clinical Documents: Combining Machine Learning Algorithms with an Inference Engine for Effective Clinical Diagnosis and Treatment".IEEE Access 5(2017):3529-3546.
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