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All Journal Jurnal Gizi Kerja dan Produktivitas Jurnal Ibu dan Anak Journal of Artificial Intelligence and Legal Technology
Muhammad Sobri Maulana
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Health Professions Medicine & Pharmacology Nursing Public Health

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EFEKTIVITAS TERAPI VITAMIN D DALAM MENGATASI NYERI KRONIK : LAPORAN KASUS BERBASIS BUKTI Muhammad Sobri Maulana
Jurnal Gizi Kerja dan Produktivitas Volume 1, Nomor 2, Tahun 2020
Publisher : Fakultas Kedokteran Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52742/jgkp.v1i2.8804

Abstract

Nyeri kronik non spesifik muskuloskeletal sudah menjadi masalah disabilitas mendunia dan sangat umum terjadi (4.2-13.3%) pada populasi umum. Sudah dinyatakan bahwa defisiensi vitamin D sangat sering terjadi dan prevalensinya mencapai 25-50% diantara populasi umum. Hasil penelitian yang menghubungkan defisiensi vitamin D dengan nyeri kronik non spesifik muskuloskeletal sudah banyak dilakukan namun hasilnya masih kontroversial.Pencarian literature dilakukan dengan memanfaatkan beberapa database pada tangal 27 -28 Januari 2018. Kata kunci yang digunakan adalah “Vitamin D”, “Efficacy”, “Pain”. Jumlah artikel yang diperoleh dari hasil pencarian adalah  130 artikel. Dari artikel ini diterapkan kriteria inklusi dan eksklusi. Kemudian dilakukan telaah untuk melihat kesamaan artikel antar database, ketersediaan naskah lengkap, serta relevansi dengan pertanyaan klinis hingga diperoleh 1 artikel. Pasien dengan suplementasi vitamin D 1.38 kali lebih memungkinkan melaporkan perbaikan nyeri dibandingan dengan placebo.Pemberian vitamin D dapat bermanfaat pada nyeri non-spesifik kronik, terutama pada populasi dengan defisiensi 25-Hydroxyvitamin D.
The Impact of Prenatal Yoga on Pregnancy Outcomes and Maternal Psychological Well-Being: A Systematic Review and Meta-Analysis Muhammad Sobri Maulana; Dwitia Pratiwi; Arditya Prayogi
JURNAL IBU DAN ANAK Vol 14 No 1 (2026): Jurnal Ibu dan Anak Volume 14 Nomor 1 Tahun 2026
Publisher : Poltekkes Kemenkes Riau Under Health Ministry Republik Indonesia

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Abstract

Prenatal yoga integrates physical postures, breathing, relaxation, and mindfulness. It is increasingly recommended as a low-intensity adjunct to routine antenatal care, yet estimates of its effects on pregnancy outcomes and maternal psychological well-being vary across trials and reviews. This article aim to synthesize evidence on whether prenatal yoga improves maternal psychological outcomes, including stress, anxiety and depression, and pregnancy or birth outcomes, including labor duration, vaginal birth, cesarean section, perineal trauma, and preterm birth. A systematic review and meta-analysis framework was developed according to PRISMA 2020. The planned databases were PubMed/MEDLINE, Embase, Cochrane CENTRAL, ProQuest, and Scopus from inception to 9 May 2026. Eligible studies were randomized or controlled trials enrolling pregnant women and comparing prenatal yoga with usual antenatal care, education, other exercise, or non-yoga controls. Risk of bias was appraised using the Cochrane RoB 2 domains, and certainty was summarized with GRADE. Random-effects synthesis was prioritized because intervention content and outcomes varied. For result, the core evidence map was anchored in a broad review of 31 studies including 2,413 pregnant women, of which 29 studies with 2,217 participants contributed to meta-analysis. Prenatal yoga was associated with lower perceived stress (SMD -1.03, 95% CI -1.55 to -0.52), anxiety (SMD -0.91, 95% CI -1.49 to -0.33), and depression (SMD -0.47, 95% CI -0.90 to -0.04). It was also associated with shorter labor duration (MD -117.75 minutes, 95% CI -153.80 to -81.71) and increased odds of normal vaginal birth (OR 2.58, 95% CI 1.46 to 4.56). A newer RCT-only synthesis in nulliparous women reported lower cesarean section (RR 0.45, 95% CI 0.38 to 0.54), preterm birth (RR 0.29, 95% CI 0.15 to 0.56), and second-degree perineal laceration (RR 0.41, 95% CI 0.25 to 0.69). Prenatal yoga appears to improve psychological well-being and several birth outcomes. Confidence is limited by heterogeneity, incomplete reporting of randomization and allocation concealment, unavoidable lack of participant blinding, small trials, and variable intervention fidelity. Well-powered multicenter RCTs using standardized yoga protocols and harmonized outcome measures are needed.
Multi-Modal AI for Early Sepsis Prediction Using EHR Time-Series and Vital Signs: A Comparative Study: Performance, Calibration, and Governance Considerations in ICU Early Warning Systems Brigivta Zalsaqira Lovly; Muhammad Sobri Maulana
Journal of Artificial Intelligence and Legal Technology Vol. 2 No. 2 (2026): August 2026
Publisher : PT Solusi Administrasi Hukum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.24012207

Abstract

Purpose: Early detection of sepsis can reduce mortality by enabling timely antibiotics, fluids, and escalation of care, yet prediction is challenging because clinical deterioration is heterogeneous and data are fragmented across electronic health records (EHR) and bedside monitoring. Methods: We present a comparative study of multi-modal machine learning approaches that integrate EHR time-series (labs, medications, diagnoses, and nurse-charted vitals) with high-frequency vital-sign waveforms (heart rate, respiratory rate, and oxygen saturation). Using a de-identified, ICU-like synthetic cohort, we define sepsis onset by a proxy clinical rule and evaluate prediction at 3–12 hours before onset. We compare classic baselines (logistic regression, random forest, XGBoost) to deep temporal models (LSTM over EHR sequences, convolutional encoders for waveforms) and a late-fusion Transformer that combines modality-specific embeddings. Results: Across horizons, the late-fusion Transformer achieves the best discrimination and precision-recall, improving AUROC by 0.02–0.04 and AUPRC by 0.04–0.08 over the strongest single-modality model. Calibration improves with temperature scaling and modality dropout. Explainability analyses using attention rollout and SHAP suggest that rising lactate, increasing vasopressor dose, and tachycardia patterns contribute most to early warnings. Novelty/Value: This paper provides a structured, reproducible comparison of multi-modal architectures for early sepsis prediction, with practical guidance on data alignment, missingness, and evaluation under clinically meaningful horizons, which can inform safer deployment and governance of predictive models in hospitals.
Co-Authors Arditya Prayogi Brigivta Zalsaqira Lovly Dwitia Pratiwi