was declared a pandemic by the World Health Organization (WHO) in January 2020. Many studies found that some specific age groups of people have a higher risk of contracting the disease. The gold standard test for the disease is a condition-specific test based on Reverse-Transcriptase Polymerase Chain Reaction (RT-PCR). We have previously shown that the results of a standard suite of non-specific blood tests can be used to indicate the presence of a COVID-19 infection with a high likelihood. We continue our research in this area with a study of the connection between the patients' routine blood test results and their age. Predicting a person's age from blood chemistry is not new in health science. Most often, such results are used to detect the signs of diseases associated with aging and develop new medications. The experiment described here shows that the XGBoost algorithm can be used to predict the patients' age from their routine blood tests. The performance evaluation is very satisfactory, with R 2 > 0.80 and a normalized RMSE below 0.1.

has been declared by The World Health Organization (WHO) a global pandemic in January, 2020. Researchers have been working on formulating the best approach and solutions to cure the disease and help to prevent such pandemics in the future. A lot of efforts have been made to develop a fast and accurate early clinical assessment of the disease. Machine Learning (ML) has proven helpful for research and applications in the health domain as a way to understand real-world phenomena through data analysis. In our experiment, we collected the retrospective blood samples data set from 1,000 COVID-19 patients in Jakarta, Indonesia for the period of March to December 2020. We report our preliminary findings on the use of common blood test biomarkers in predicting COVID-19 patient mortality. This study took advantage of explainable machine learning to examine the data set. The contribution of this paper is to explain our findings on predicting COVID-19 mortality, including the role of the top 11 biomarkers found in our dataset. These findings can be generalized, especially in Indonesia, which is now at its highest peak of the epidemic. We show that tree-based AI models performed well on predicting COVID-19 mortality, while also making it easy to interpret the findings, as they lend themselves to human scrutiny and allow clinicians to interpret them and comment on their viability.

The number of COVID-19 cases is growing rapidly, while there is not enough healthcare workers which can help the patients. Even worse, the highly contagious nature of this disease, requires the medical staff to be more restrictive and wear the Personal Protective Equipment (PPE) all the time when handling the patients directly. In this situation, a remote system which can monitor patient progress from a distant is inevitable. The emerge of Internet of Thing (IoT) technology has been implemented in many domain. The availability of smart technology, where almost all devices around us has connectivity to the internet, allow people to automate process from distance. The implementation of IoT has also been shown very helpful in medical domain, especially during the pandemics. The IoT technology can be a suitable solution for monitoring patients with a highly contagious disease. The technology can also be very helpful for people who live far from healthcare facility. This can allow people to report immediately and even connect to the hospital system in real-time. In this paper, we propose the use of three different sensors, namely: heart-rate and pulse oximeter sensor (MAX30102), temperature sensor(DS18b20) and accelerometer sensor, which is integrated in a web-based early warning monitoring system for COVID-19 patients.

Background: The coronavirus (COVID-19) outbreak has caused public fear alongside social stigma and discrimination. As a result, people hide the illness to avoid discrimination. This study focuses on investigating doctor-patient communication, their challenges when diagnosing suspected COVID-19 patients, and how the physicians communicated with patients’ mental issues. Methods: A mixed-methods approach examined this phenomenon and an online survey was conducted among 221 Indonesian doctors. The following were quantitatively examined: theme of Covid stigma and patient openness, patient/physician interaction and communication, and information and stigmatization of Covid. Qualitatively, two Focus Group Discussions (FGD) were conducted with five physicians and four COVID-19 survivors or their family members. Thereafter, interviews were set up with the selected four persons. Results: 74.2% of respondents encountered patients with lack of honesty or openness, while 55% of physicians claimed that 1-2 patients out of every 10 patients covered up about their illness. 27% of physicians indicated that 3-5 of 10 patients did not tell the truth. Majority of respondents opined that the media/social media played a large role in the promotion of stigma for those who had COVID-19. Conclusion: Study results affirmed belief in a link between the stigma of COVID-19 and patients’ dishonesty. Results indicated that many individuals are reluctant to disclose their true positions for fear of stigmatization by the people around them. This is linked to the stigma of COVID-19 and patient reluctance to be honest about their health/illness per impact of COVID-19.  This research concluded that doctors need to find creative ways to communicate with their patients so as to increase patient honesty about illness.

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