COVID-19 is an acute respiratory disease that has become a pandemic worldwide. Many studies have been conducted to enhance our understanding of COVID-19. However, the abundance of information obtained from these studies has resulted in information overload. In this study, we purposed a simple COVID-19 Knowledge Graph in Bahasa Indonesia as a way to reconstruct knowledge to combat this information overload. We used Bahasa Indonesia in our study to explore its potential for constructing a Knowledge Graph (KG). The construction of our KG involved manual curation of medical literatures and annotation of entities and relationships by the domain experts. The KG was implemented using Neo4J version 5. We successfully demonstrated our COVID-19 KG, which consists of 240 nodes and 276 relationships with 15 and 14 node and relationship labels respectively. Accessing the information within the KG is made effortless through the use of Cypher queries in Neo4J. Further research is still needed to develop the KG into a larger and better one. However, our COVID-19 KG can serve as a basis for further development.

Critical illness is a condition that requires special attention, as it often involves life-threatening situations with a higher mortality rate. Attention is further focused on diabetic patients, who have recently become common comorbid cases among critically ill patients. Patients with these conditions are often prone to hyperglycemia and hypoglycemia due to the nature of critical illness and diabetes itself. Intensive Insulin Therapy (IIT) is known to be beneficial in maintaining blood glucose levels at the optimal target in such patients. However, the application of IIT is not straightforward. To date, there is no standardized IIT protocol, and its use is highly dependent on the expertise of each clinician. Moreover, each protocol does not always guarantee the maintenance of blood glucose levels because it depends on the patient's response to insulin, while the protocol is designed to be general. With the rapid advancement of Artificial Intelligence (AI), particularly Machine Learning (ML), a personalized IIT can be developed to address this issue. With the power of data, AI algorithms can analyze and assist in creating personalized IITs according to the patient's condition, with the goal of maintaining 2 their blood glucose levels. In this review, we present a future directive on the potential role of AI in creating personalized IITs for critically ill diabetic patients, as well as the challenges to be overcome in the development process.

Diabetes mellitus, particularly type-2 diabetes, remains a prevalent health issue, raising concerns due to its associated risk of complications. Among these, cardiovascular complications pose a significant threat, exhibiting high morbidity and mortality rates. Health screening plays a pivotal role in stratifying the risk levels of diabetes patients, facilitating proactive measures to prevent the progression of complications. As such, the primary objective of this study is to develop a predictive model system for assessing cardiovascular risk in diabetes patients. Our study used the Cardiovascular Disease dataset and conducts experiments with various supervised machine learning algorithms, such as Naive Bayes, decision tree, random forest, AdaBoost, and XGBoost. The results reveal that ensemble learning algorithms based on boosting, particularly AdaBoost and XGBoost, outperform other supervised machine learning methods. However, even with the best performance achieved using the dataset, the accuracy stands at 0.71, and the F-1 score is 0.69, which is still acceptable for screening purposes. Although these results provide valuable insights, indicating individuals at higher risk for cardiovascular complications in diabetes, further improvements are needed to enhance early prevention strategies.