Discovering hidden knowledge through auditing clinical diagnostic knowledge bases.
other · Level V
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- Record sourced from PubMed, PMID 29940263.
- Also identified by DOI 10.1016/j.jbi.2018.06.014.
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Abstract
Evaluate potential for data mining auditing techniques to identify hidden concepts in diagnostic knowledge bases (KB). Improving completeness enhances KB applications such as differential diagnosis and patient case simulation. Authors used unsupervised (Pearson's correlation - PC, Kendall's correlation - KC, and a heuristic algorithm - HA) methods to identify existing and discover new finding-finding interrelationships ("properties") in the INTERNIST-1/QMR KB. Authors estimated KB maintenance efficiency gains (effort reduction) of the approaches. The methods discovered new properties at 95% CI rates of [0.1%, 5.4%] (PC), [2.8%, 12.5%] (KC), and [5.6%, 18.8%] (HA). Estimated manual effort reduction for HA-assisted determination of new properties was approximately 50-fold. Data mining can provide an efficient supplement to ensuring the completeness of finding-finding interdependencies in diagnostic knowledge bases. Authors' findings should be applicable to other diagnostic systems that record finding frequencies within diseases (e.g., DXplain, ISABEL).
Medical subject headings
- Data Mining
- Diagnosis, Computer-Assisted
- Knowledge Bases
- Medical Informatics