Digitisation & Health - Reflections from the Covid Pandemic

When COVID-19 reached Dr. Helmi Zakariah's home country of Malaysia in January 2020, he was consulting in Brazil as CEO of Artificial Intelligence in Medical Epidemiology (AIME) on Machine Learning application for dengue outbreak forecasting. A trained physician, public health professional, and digital health entrepreneur, Dr. Zakariah found himself in high demand as the Malaysian government began to mount it's COVID-19 response. He was asked to return home to his state of Selangor to lead the Digital Epidemiology portfolio for the Selangor State Task Force for COVID-19, and upon arrival immediately began to address the many challenges COVID-19 presented. This session will bring the audience along the sobering journey of health digitisation & adoption in the heat of the pandemic and beyond-not only what works, but more importantly-what doesn't-and to reflect on the case that the cost of underinvestment and inaction for digital innovation in health is simply too high in the face of another pandemic.Copyright © 2023

Predictive Model of Undergraduate Student Grading Using Machine Learning for Learning Analytics

Predicting the academic achievement has become very important for university students as well as lecturers, especially during the difficult times of pandemic Covid-19, online distance learning (ODL) with some students need to do part-time job due to financial problems. Furthermore, we are surrounded by a plethora of digital entertainment, such as social media platforms and mobile games, which may also serve as a distraction and undermine students' commitment to their studies. Therefore, this paper develops a model to predict student academic performance using Machine Learning approaches. The model is developed by training the dataset acquired that consists of demographic information, study preparation, academic performance and motivation from the students in a public higher institution in Malaysia. The model is also tested on the public dataset related to student academic performance. The findings showed that JRip classifier have obtained the best accuracy of 92% for the newly collected data and 100% accuracy by using Random Forest classifier on the public dataset. The developed model and data visualization are useful for the development of learning analytics system which students and lecturers can make an early intervention and determining whether students need to take necessary actions to improve their academic results in real-time, as well as gaining a better understanding of the factors that may affect their academic performance. © 2022 IEEE.

Probabilistic Risk Assessment of COVID-19 Patients at COVID-19 Assessment Centre

COVID-19 started impacting Malaysia in early 2020, and the cases have reached 4.4 million as of April 27, 2022, with 35507 deaths. Since then, federal and state governments have set up COVID-19 Assessment Centres (CACs) to monitor, manage and assess the risk of COVID-19 positive patients. However, a large number of patients within a day has caused the CACs to experience a shortage in medical officers and subsequently resort to overwhelming administrative work. A misassignment of a patient to either home quarantine or COVID-19 Quarantine and Treatment Center or immediate hospital admission (PKRC) could potentially increase the BroughtIn-Dead (BID) cases. Therefore, this study aimed to overcome the challenges by achieving the following two main objectives: (i) to identify the optimal feature sets for adult and child patients when they require hospital admission, (ii) to construct predictive models that perform preliminary assessment of a patient, which a medical officer later confirms. In this study, the predictive models developed were Naive Bayes, Random Forest, K-Nearest Neighbors, Logistic Regression and Decision Tree. The datasets were obtained from one of the CACs in Malaysia and were imbalanced in nature. The empirical findings showed that Logistic Regression outperformed the rest with a slight difference. The findings suggested that while there are shared symptoms among adult and child patients, such as runny nose and cough, the child patients exhibited extra symptoms such as vomiting, lung disease, and persistent fever.

Geospatial analytics for COVID-19 active case detection

Ever since the COVID-19 pandemic started in Wuhan, China, much research work has been focusing on the clinical aspect of SARS-CoV-2. Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus. Limited studies have, however, reported on COVID-19 transmission pattern analysis, and using geography features for prediction of potential outbreak sites. Predicting the next most probable outbreak site is crucial, particularly for optimizing the planning of medical personnel and supply resources. To tackle the challenge, this work proposed distance-based similarity measures to predict the next most probable outbreak site together with its magnitude, when would the outbreak likely to happen and the duration of the outbreak. The work began with preprocessing of 1365 patient records from six districts in the most populated state named Selangor in Malaysia. The dataset was then aggregated with population density information and human elicited geography features that might promote the transmission of COVID-19. Empirical findings indicated that the proposed unified decision-making approach outperformed individual distance metric in predicting the total cases, next outbreak location, and the time interval between start dates of two similar sites. Such findings provided valuable insights for policymakers to perform Active Case Detection. © 2021 Tech Science Press. All rights reserved.