DECISION SUPPORT FOR CLINICAL LABORATORY TEST REQUISITION: THE UTILITY OF ICD-10 CODING
Keywords:healthcare analytics, data mining, big data, decision support, smart laboratories
This study examined the possibility of a strong relationship between ICD-10 codes and the panel of clinical laboratory tests requested. Decision-tree learning principles were used to determine whether requisition event attributes had a useful relationship with laboratory tests. A recommender system was designed and tested using ICD-10 codes as a core predictor. The results showed an average requisition accuracy upwards of 74 per cent. If such a system were to be deployed, health professionals would be able to draw from a vast and accessible pool of knowledge when selecting clinical laboratory tests, improving the effectiveness of clinical laboratory operations.
How to Cite
LicenseAuthors who publish in the Journal agree to the following terms:
- Authors retain copyright and grant the Journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this Journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the Journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this Journal.