Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Narra J Journal of Health and Therapy
Keane, Arnold
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Health Professions Immunology & microbiology Medicine & Pharmacology Nursing Public Health Veterinary

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Predicting the risks of stroke, cardiovascular disease, and peripheral vascular disease among people with type 2 diabetes with artificial intelligence models: A systematic review and meta-analysis Nur, Aqsha; Tjandra, Sydney; Yumnanisha, Defin A.; Keane, Arnold; Bachtiar, Adang
Narra J Vol. 5 No. 1 (2025): April 2025
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v5i1.2116

Abstract

Macrovascular complications, including stroke, cardiovascular disease (CVD), and peripheral vascular disease (PVD), significantly contribute to morbidity and mortality in individuals with type 2 diabetes mellitus (T2DM). The aim of this study was to evaluate the performance of artificial intelligence (AI) models in predicting these complications, emphasizing applicability in diverse healthcare settings. Following PRISMA guidelines, a systematic search of six databases was conducted, yielding 46 eligible studies with 184 AI models. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUROC). Subgroup analyses examined model performance by outcome type, predictor data (lab-only, non-lab, mixed), and algorithm type. Heterogeneity was evaluated using I2 statistics, and sensitivity analyses addressed outliers and study biases. The pooled AUROC for all AI models was 0.753 (95%CI: 0.740–0.766; I2=99.99%). Models predicting PVD achieved the highest AUROC (0.794), followed by cerebrovascular diseases (0.770) and CVD (0.741). Gradient-boosting algorithms outperformed others (AUROC: 0.789). Models with lab-only predictors had superior performance (AUROC: 0.837) compared to mixed (0.759) and non-lab predictors (0.714). External validations reported reduced AUROC (0.725), underscoring limitations in generalizability. AI models show moderate predictive accuracy for T2DM macrovascular complications, with laboratory-based predictors being key to performance. However, the limited external validation and reliance on high-resource data restrict implementation in low-resource settings. Future efforts should focus on non-lab predictors, external validation, and context-appropriate AI solutions to enhance global applicability.
Hubungan antara pemberian makanan bergizi dan keamanan pangan: Tinjauan literatur Suryoadji, Kemal; Ali, Najma; Setyawan, Dhanis Adrianto; Faruqi, Muhammad; Keane, Arnold; Christian, Christopher; Wardani, Arimbi Kusuma; Suskhan, Rizki Fauzi; Rompies, Albertus Marcio Edbert; Garnette, Keisha Annabel; A’yun, Ilham Qurrota; Putra, Elza Nur Warsa; Simanjuntak, Kevin Tadeus
Journal of Health and Therapy Vol. 5 No. 1 (2025): Journal of Health and Therapy
Publisher : Nur Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53088/jht.v5i1.2388

Abstract

The Free Nutritious Meal (MBG) program aims to improve children’s nutrition and learning outcomes. However, large-scale implementation faces food safety challenges leading to foodborne illness outbreaks in Indonesia. This narrative literature review examined the relationship between nutritious meal programs and food safety risks and identified preventive measures for safer school feeding. Searches were performed in PubMed, Scopus, Cochrane, and Google Scholar using keywords related to food safety, foodborne illness, school feeding, and nutritious meals. Studies were grouped into three themes: nutritional impact, contamination risks, and prevention. Nutritious meal programs reduce stunting and improve school attendance, yet inadequate hygiene and weak supervision remain major causes of food poisoning. Key contaminants include Salmonella spp., Escherichia coli, and Bacillus cereus, associated with poor sanitation, improper storage, and untrained food handlers. The effectiveness of nutritious meal programs depends on robust food safety systems, including hygiene enforcement, regular audits, food handler training, and coordinated government oversight.
Co-Authors Adang Bachtiar Ali, Najma A’yun, Ilham Qurrota Christian, Christopher Faruqi, Muhammad Garnette, Keisha Annabel Nur, Aqsha Putra, Elza Nur Warsa Rompies, Albertus Marcio Edbert Setyawan, Dhanis Adrianto Simanjuntak, Kevin Tadeus Suryoadji, Kemal Suskhan, Rizki Fauzi Tjandra, Sydney Wardani, Arimbi Kusuma Yumnanisha, Defin A.