METHODS: This study uses outpatient data from the HKL's Patient Management System (SPP) throughout 2019. The final data set has 246,943 appointment records with 13 attributes used for both descriptive and predictive analyses. The predictive analysis was carried out using seven machine learning algorithms, namely, logistic regression (LR), decision tree (DT), k-near neighbours (k-NN), Naïve Bayes (NB), random forest (RF), gradient boosting (GB) and multilayer perceptron (MLP).
RESULTS: The descriptive analysis showed that the no-show rate was 28%, and attributes such as the month of the appointment and the gender of the patient seem to influence the possibility of a patient not showing up. Evaluation of the predictive model found that the GB model had the highest accuracy of 78%, F1 score of 0.76 and area under the curve (AUC) value of 0.65.
CONCLUSION: The predictive model could be used to formulate intervention steps to reduce no-shows, improving patient care quality.
METHODOLOGY: We conducted a retrospective data retrieval from the medical records of 254 paediatric patients who had been diagnosed with confirmed cases of dengue fever. The clinical characteristics were compared between severe and non-severe dengue. Multiple logistic regression analysis was utilised to elucidate the variables that exhibited associations with severe dengue.
RESULTS: A total of 254 paediatric patients were included, among whom 15.4% (n = 39) were diagnosed with severe dengue. Multiple logistic regression analysis identified lethargy, systolic blood pressure (SBP) below 90 mmHg, capillary refilled time (CRT) longer than 2 seconds, ascites, and hepatomegaly were independently associated with severe dengue.
CONCLUSION: In paediatric patients, severe dengue is associated with specific clinical indicators, including lethargy, low systolic blood pressure, prolonged capillary refill time (CRT), and the presence of ascites and hepatomegaly. Identifying these clinical features early is crucial for primary care physicians, as it enables accurate diagnosis and timely intervention to manage severe dengue effectively.