Abstract
The complex and insidious presentation of certain health conditions, such as pituitary disorders, makes it challenging for primary care providers (PCP) to render a timely diagnosis—often delaying appropriate treatment for years. In contemporary clinical laboratories, laboratory interventions can appropriately add-on extra tests to help confirm or rule out complex disorders. For these protocols to be clinically valid and economically efficient, they require combining knowledge on abnormal test result patterns and patient health data to automatically “reflex” add-on tests and issue comments subsequent to their results. In this paper, we present a Semantic Web based framework for the computerization of reflex testing protocols. To avoid casting too wide a net in terms of add-on tests, a reflex (testing) protocol may include an arbitrary number of stages, where test result patterns in stagen can trigger add-on tests in stagen+1. Our evaluation applies a computerized reflex protocol for pituitary dysfunction on 1-year retrospective data, and compares its accuracy and financial cost with a combined reflex/reflective approach that included manual laboratory clinician intervention.
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Notes
- 1.
Patient samples remained available for five days, as per our local laboratory policy.
- 2.
We refer to [5] for the full criteria and reflective tests (note these have been refined since).
- 3.
For simplicity, the shown rules assume that the system reasons over one patient at a time.
- 4.
SWRL (and OWL2) lack negation-as-failure, so it is left to the system to cope with exclusions.
- 5.
This could be generalized using universal quantification, but this is not supported by SWRL.
- 6.
- 7.
I.e., extra cost of add-on tests on a patient sample (avg. CAD 1.15 per test).
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Van Woensel, W., Elnenaei, M., Imran, S.A., Abidi, S.S.R. (2021). Semantic Web Framework to Computerize Staged Reflex Testing Protocols to Mitigate Underutilization of Pathology Tests for Diagnosing Pituitary Disorders. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_13
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