ÖZLEM AKTAŞ, ALI RIZA ŞIŞMAN, MOHAMMED ABEBE YIMER, SÜLEYMAN SEVINÇ, OKTAY YILDIRIM, EMINULLAH YAŞAR

Monitoring Medical Interventions for Multidimensional Evaluation of Changes in Patient Test Results with Principal Component Analysis

Medical doctors of today are challenged with increasingly large volumes of high-dimensional, heterogeneous, and unstructured data from various sources that pose significant challenges for manual analysis. However, this unstructured data is mainly vital for decision making but there exists a shortage of intelligent tools to extract the hidden knowledge. Given these facts, the application of machine learning methods in healthcare is a growing phenomenon. This paper explores machine learning approaches for interpreting large quantities of continuously acquired, multivariate patient-based medical laboratory data, in intensive care unit (ICU) settings. The research hypothesizes that principal component analysis (PCA) can be able to capture the changes in the outcomes after a medical intervention. We adopted PCA as the main method, to observe and capture the daily changes for intensive care unit patients. The approach will be able to inform the physicians, which laboratory tests are exhibiting variances after an intervention, and their associated epiphenomenon. This can be used as a clue to make decisions on which treatment or diagnosis to apply further. Experimental analysis results indicate that PCA was able to capture patient progression in terms of variances. Permutation tests for the validity and stability of the model exhibit an acceptable significance level with a p-value of 0.001. Results showed that the approach provides promising results for interpreting large quantities of patient data for establishing a cause-effect relationship from medical interventions and be used as an early warning system. The study retrospectively demonstrated the capability of PCA to monitor and provide an alert to the clinicians about the patient's changing conditions, thereby providing opportunities for timely interventions. If coupled with other machine learning models, the approach can also be able to support clinical decision making and enable effective patienttailored care for better health outcomes.

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