Indrajani Sutedja

Background: The risk of rehospitalization in patients with heart failure (HF) has initiated various efforts to prevent and simultaneously improve quality of life. Self-monitoring at home is one option, and technology is increasingly being used for this purpose. Objective: This pilot study aimed to evaluate the feasibility and preliminary effects of a digital home monitoring intervention on patient-reported outcomes and 30-day readmissions among patients with HF in Indonesia. Methods: A mixed methods pilot study was conducted, combining qualitative system development and quantitative evaluation. Patients were assigned to an intervention group (digital monitoring) or control group (standard care). Readmission rates were compared using chi-square tests and odds ratios. Changes in Kansas City Cardiomyopathy Questionnaire scores were analyzed using linear mixed-effects models. Results: A total of 60 patients were included (n=30, 50% in the intervention group; n=30, 50% in the control group). Readmission occurred in 20% (6/30) of patients in the intervention group and 43.3% (13/30) of patients in the control group (odds ratio 0.33, 95% CI 0.10‐1.09; P =.10). Linear mixed-effects analysis showed greater improvement in Kansas City Cardiomyopathy Questionnaire overall summary score in the intervention group ( P =.02). Improvements were observed in the physical limitation, symptom frequency, symptom burden, quality of life, and social limitation domains. During follow-up, 3.3% (1/30) of the patients in the intervention group died of non–HF-related causes, and 10% (3/30) of the patients in the control group died due to HF. Conclusions: This pilot study suggests that digital home monitoring is feasible and associated with improvements in patient-reported outcomes, with a potential signal toward reduced readmission. Larger studies are needed to confirm effectiveness.

BACKGROUND The risk of rehospitalization in heart failure (HF) patients has initiated various preventive efforts and to simultaneously improve the quality of life (QoL) of the patients. Self-monitoring at home is one option, and technology is increasingly being used for this purpose. OBJECTIVE To reduce the risk of hospital readmission and maintain the QoL of HF patients through self-monitoring with the assistance of a digital application. METHODS Starting with determining the need for HF services in National Cardiovascular Center Jakarta in 2024, an application called FineHeart was built and tested on HF patients in a quasi-experimental pilot study. After the User Acceptance Test was completed, HF patients were recruited as intervention group and control group participants. Quality of Life was measured with the Kansas City Cardiomyopathy Questionnaire (KCCQ) before and after the test. The readmission rate and 4 of the KCCQ parameters were analysed between the 2 groups. RESULTS Of the 72 patients who met the inclusion criteria, thirty participated in the intervention group and thirty in the control group who had signed the consent form. In the intervention group, after 30 days, 22 people complied using the FineHeart application prototype (73.3%), six were readmitted (20%), and one died not because of HF (3.3%). In the control group, 10 were readmitted (33.3%), and three died because of HF (10%). There was a significant relationship between adherence to application usage and an increase in Quality-of-Life scores (p<0.029) and Overall Summary Score (p<0.001)), but not in Self-efficacy and Social Limitation Scores. CONCLUSIONS The findings of this study provide a potential benefit for HF patients to perform self-monitoring at home in collaboration with the hospital team by using FineHeart app. Patient adherence may reduce the risk of readmission and improve QoL. Multicenter studies on larger groups of patients are needed to confirm these preliminary results.

Throughout the world, the disease most people suffer from is heart disease. Likewise with Indonesia, many Indonesian people have heart disease. The cause of the high death rate from heart disease is still a lack of experts who can treat heart disease well. Apart from that, there is also a low level of public awareness to carry out regular checks on the development of their heart health. Because heart disease is still one of the main causes of death throughout the world and Indonesia, this is a topic to increase public awareness and explore the role of artificial intelligence (AI) in the prevention and early detection of heart disease. We explore what AI can do to prevent and cure heart disease, AI works by performing deep machine learning, and neural network learning and how it can be applied to analyze large data sets to identify risk factors, predict outcomes, and support decisions. clinical decisions. The focus is to leverage AI and improve early predictions for the public, personally providing patient care. In this article, readers can learn about the benefits and limitations of AI in the context of heart disease and emphasize the need for high-quality data in order to obtain appropriate analysis results. Finally, this paper suggests that other researchers carry out further research, such as improving the interpretability of AI models, expanding and searching for multiple data sources, and encouraging collaboration between government, society and medical personnel. The potential of using AI can reduce people's risk of heart disease and AI offers a path to better public health outcomes and more efficient health services to treat it and detect it early.

At a time when technological developments are increasingly rapid, many industrial sectors are using AI to support the effectiveness of human life. One example is that AI can be used to build qualified antimicrobial resistance prediction models based on genomic, epidemiological and biochemical data. AI will then work to identify patterns and factors related to antimicrobial resistance. In this way, researchers can predict the potential resistance of a pathogen to certain antibiotics. By utilizing AI, we can predict molecules that may be effective in disrupting antimicrobial resistance pathways or inhibiting pathogen growth. Apart from that, AI assistance will be useful in designing new drugs with desired properties, because AI can see strong to weak antimicrobial activity, specificity against certain pathogens, and good bioavailability. Other benefits may also help in assessing the risk of antimicrobial resistance to new drug candidates. By analyzing factors such as drug-microbe interactions, potential cross-resistance, and microbial population dynamics, AI can estimate the probability of resistance to a drug under development. The final benefit is that AI can help in personalizing antimicrobial treatment by considering factors such as the patient's genomic profile, treatment history, and local resistance patterns. By utilizing machine learning techniques. Doctors will find AI helpful because AI can provide advice on choosing the most effective therapy and minimizing the risk of resistance. In silico research is becoming increasingly important in efforts to understand, prevent and overcome antimicrobial resistance and develop effective treatments.

This systematic literature review explores the intersection of deep learning and content-based image retrieval (CBIR) in addressing copyright concerns related to AI-generated art. As artificial intelligence rapidly transforms various artistic domains, it raises critical questions regarding authorship, ownership, and the ethical implications of machine-generated creativity. The review examines the capabilities of CBIR systems in identifying AI-generated images by analyzing visual features such as color, texture, and shape. Additionally, it highlights the role of deep learning models in enhancing the accuracy of these systems through the detection of distinctive patterns characteristic of AI artworks. The findings underscore the importance of developing robust methodologies that leverage AI and CBIR technologies to protect intellectual property rights while fostering innovation in the creative industries. This research contributes to the broader discourse on the legal and ethical challenges posed by AI in art, providing insights for policymakers, artists, and technologists in navigating the evolving landscape of AI-generated content.

Most of Adverse Pregnancy Outcomes (APO) are preventable particularly if the health personnel can early detect the risk. This study aimed to review articles on how the machine learning model can predict APO for early detection to prevent neonatal mortality. We conducted a systematic literature review by analyzing seven articles which published between 1 January 2013 and 31 October 2022. The search strategy was the populations are pregnant women, intervention using machine learning for APO prediction, and the outcomes of APO are Low Birth Weight, preterm birth, and stillbirth. We found that the predictors of LBW were demographic, maternal, environmental, fetus characteristics, and obstetric factors. The predictors of preterm birth were demographics and lifestyle. Meanwhile, the predictors of stillbirth were demographic, lifestyle, maternal, obstetric, and fetus characteristics. It was indicated that Random Forest (Accuracy: 91.60; AUC-ROC: 96.80), Extreme Gradient Boosting (Accuracy: 90.80; AUC-ROC: 95.90), logistic regression (accuracy 90.24% and precision 87.6%) can be used to predict the risk of APO. By using a machine learning algorithm, the best APO prediction models that can be used are logistic regression, random forest, and extreme gradient boosting with sensitivity values and AUC of almost 100%. Demographic factors are the main risk factors for APO.

Adverse Pregnancy Outcome (APO) is the leading cause of infant mortality, particularly in low and middle-income countries. Despite no significant change in neonatal mortality rates in Indonesia over the past fifteen years, identifying the risk factors for APO can help healthcare professionals provide personalized interventions and support. This study aims to determine the determinants of APO risk in Indonesia. The cross-sectional study utilized the 2017 Indonesian Health Demographic Survey (IDHS), which included 9,752 women of reproductive age. APO, including low birth weight, early birth, and stillbirth, was the outcome of the study. The demographic characteristics and other risk factors were evaluated. To identify APO-risk-associated factors, we performed multiple logistic regression analysis. The results showed that low economic status (Adj. OR= 1.407; 1.153-1.716; p= 0.001)), living in rural areas (Adj. OR= 0.804; 0.663-0.976; p= 0.027), having a history of pregnancy or childbirth complications (Adj. OR= 4.563; 3.617-5.756; p= 0.0001), premature rupture of the membrane during pregnancy or after giving birth (Adj. OR= 1.351; 1.069-1.708; p= 0.012), antenatal visits <4 times (Adj. OR= 1.603; 1.123-2.288; p= 0.009), consumption of iron supplement tablets <90 days (Adj. OR= 1.275; 1.063-1.529; p= 0.009), and twin pregnancy (Adj. OR= 33.715; 18.587-61.153; p= 0.0001) significantly influenced the APO prevalence. The study concluded that APO risk is significantly impacted by poor obstetric history features, multiple births, low health service utilization, and sociodemographic factors. Early detection of these risk factors is essential for reducing neonatal death caused by APO.