A Clinical Decision Support Model for Predicting Avoidable Re-hospitalization of Breast Cancer Patients in Kenyatta National Hospital |Chapter 7 | Mathematics and Computer Science: Contemporary Developments Vol. 2

 

This study develops a Clinical Decision Support Model (DSM) to aid in assessing and recommending the discharge of a breast cancer patient from the hospital ward since the discharge problem is often overwhelming for clinicians to process at the point of care or in urgent situations. The model incorporates breast cancer patient-specific data that is well-structured, having been obtained from pre-study administered questionnaires and current evidence-based guidelines. The obtained dataset of the pre-study questionnaires is processed using data mining techniques to generate an optimal clinical decision tree classifier model. This model assists physicians in enhancing their decision-making process when discharging a patient, based on basic cognitive processes in medical thinking. This resulted in new, better-formed, and superior discharge outcomes. The model improves the quality of patient discharge assessments through a predictive discharging model outcome designed from individual unique risk attributes at the point of discharge. This enables timely detection of possible deterioration in health quality upon said discharge, which is noted as a major contributor to ward congestion as a result of re-hospitalization from poor discharge assessment that currently wholly relies on overwhelmed clinicians at the point of care or in urgent situations. The outcome of the implemented model is that it bridges the gap caused by less informed clinical discharge, and reinforces discharge decisions that ensure better treatment outcomes, thus reducing unforeseeable deterioration in the quality of health for discharged patients and surges in the mortality rate blamed on mistrusted discharge decisions. This paper is organized to start with a discussion of the breast cancer scourge and clinical knowledge for discharging patients, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.

Author(s) Details

Christopher Oyuech Otieno
Department of Computer Science, University of Nairobi, Nairobi, Kenya.

Oboko Robert Obwocha
Department of Computer Science, University of Nairobi, Nairobi, Kenya.

Andrew Mwaura Kahonge
Department of Computer Science, University of Nairobi, Nairobi, Kenya.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v2/1022