An Auto-Approval Approach for Laboratory Test Assessment

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MUTLU BİLGE B., Kazezoglu C., Soylu S., Uzunal E., Akyurek A. O., SEZER E.

IEEE ACCESS, vol.9, pp.138323-138344, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9
  • Publication Date: 2021
  • Doi Number: 10.1109/access.2021.3116680
  • Journal Name: IEEE ACCESS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.138323-138344
  • Keywords: Frequency measurement, Biochemistry, Task analysis, Real-time systems, Random forests, Decision making, Support vector machines, Automatic approval system, clinical biochemistry laboratory test, laboratory information system, machine learning, AUTOVERIFICATION, VALIDATION, SYSTEM, RULES
  • Hacettepe University Affiliated: Yes


Background: Auto-approval (also known as autoverification) is the task of automatically evaluating the consistency of a test result throughout the laboratory information system rather than its manual evaluation by the biochemists. Most of the existing auto-approval systems rely on a rule-based solution obtained from expert knowledge. However, it is a challenging issue to produce a complete and general rule-base for every single test type. To that end, the studies have relied only on a small subset of laboratory tests. Methods: The rule-based auto-approval process was re-investigated in this study, and the rules predetermined by human experts were utilized as a pre-filtering step for grouping the laboratory test result via some common criteria. Subsequently, a machine learning-based approval method, smart-approval, was proposed to approve the tests more precisely. At this point, the expert knowledge in the rule-based pre-filtering was extended by the tendency to imitate the experts' behavior in the smart-approval step. Two novel datasets (entitled with plot and real-time datasets) containing human experts' responses to previously studied tests have been used to train the machine learning models. Results: Experiments have been handled on several machine learning models on plot dataset to obtain the trained models based on cross-validation. Here, the random forest classifier provided the best approval performance while also outperforming the approval success of existing studies in the literature. To observe the real-time performance of these trained models, they were also evaluated on real-time unseen data for 4 months. Here, random forest reaffirmed that it was the best approval model. Conclusions: The proposed auto-approval system relying on random forest can provide convincing classification performance on both of the obtained datasets. With the correct approval rate of 98.51%, it surpasses many powerful approval methods in the literature.