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2025

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Gambaran Kasus Gondongan Pada Sekolah Dasar Negeri 010 Makroman, Samarinda, Dwi Listiani, Ashri Budi Astuti, Jami'an Jami'an, Eko Prasetya Budi, Arif Sumarianto, Irfansyah Baharuddin Pakki Jan 2025

Gambaran Kasus Gondongan Pada Sekolah Dasar Negeri 010 Makroman, Samarinda, Dwi Listiani, Ashri Budi Astuti, Jami'an Jami'an, Eko Prasetya Budi, Arif Sumarianto, Irfansyah Baharuddin Pakki

Jurnal Epidemiologi Kesehatan Indonesia

Mumps is an infectious disease caused by a viral infection, often attacking the salivary glands, especially the parotid glands. This disease is self-limiting and common in elementary school children. This study aims to describe the prevalence, symptoms, and impact of mumps in students of SD Negeri 010 Makroman, Samarinda, as well as evaluate risk factors and relevant prevention efforts. With a descriptive research design, data was obtained from a population of 307 students through secondary data from the Makroman Health Center and direct on-site investigations. An attack rate of 3.58% indicates a limited impact on at-risk populations, but it is …

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The Impact Of Covid-19 Pandemic On Economic Convergence Between Developed And Developing Nations, Lucile Sabas, Williams Kwasi Peprah [email protected], Carmelita Troy, Quentin Sahly Jan 2025

The Impact Of Covid-19 Pandemic On Economic Convergence Between Developed And Developing Nations, Lucile Sabas, Williams Kwasi Peprah [email protected], Carmelita Troy, Quentin Sahly

Faculty Publications

This study investigates convergence between developed and developing countries’ GDP during the pre-COVID-19 era (1960-2019) and post-COVID-19 era (2020-2023) using World Bank data and the Augmented Dickey-Fuller (ADF) test. For the pre-COVID period, results show no significant convergence, with an ADF statistic of −1.78 and a P = 0.39, likely due to disparities in industrialization and governance. Similarly, post-COVID results (ADF statistic −1.2, P = 0.67) also suggest no convergence, possibly exacerbated by pandemic-related challenges. The study highlights that structural barriers and the pandemic have hindered economic convergence.

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Editorial, Daniel Gonzalez-Socoloske, Miriam Marmontel [email protected] Jan 2025

Editorial, Daniel Gonzalez-Socoloske, Miriam Marmontel [email protected]

Faculty Publications

No abstract provided.

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Dr. Eric Angel Ramos - A Shooting Star That Inspired Marine Mammalogists And Beyond (1987 – 2024), Laura J. May-Collado, Jeremy J. Kiszka, Nataly Castelblanco-Martínez, Beth Brady, G. Rieucau, Daniel Gonzalez-Socoloske, Renata Sousa-Lima, Diana Reiss, Macelo Magnasco, Roderic Mast, Wayne Sentman, Joy S. Reidenberg Jan 2025

Dr. Eric Angel Ramos - A Shooting Star That Inspired Marine Mammalogists And Beyond (1987 – 2024), Laura J. May-Collado, Jeremy J. Kiszka, Nataly Castelblanco-Martínez, Beth Brady, G. Rieucau, Daniel Gonzalez-Socoloske, Renata Sousa-Lima, Diana Reiss, Macelo Magnasco, Roderic Mast, Wayne Sentman, Joy S. Reidenberg

Faculty Publications

No abstract provided.

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Effects Of Interventions For The Prevention And Management Of Maternal Anemia In The Advent Of The Covid-19 Pandemic: Systematic Review And Meta-Analysis, John Kyalo Muthuka Dr., Diana Kageni Mbari-Fondo, Francis Muchiri Wambura, Kelly Oluoch, Japheth Mativo Nzioki, Nabaweesi Rosemary Dr Jan 2025

Effects Of Interventions For The Prevention And Management Of Maternal Anemia In The Advent Of The Covid-19 Pandemic: Systematic Review And Meta-Analysis, John Kyalo Muthuka Dr., Diana Kageni Mbari-Fondo, Francis Muchiri Wambura, Kelly Oluoch, Japheth Mativo Nzioki, Nabaweesi Rosemary Dr

Faculty Publications

No abstract provided.

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The Role Of Artificial Intelligence In Early Tumor Detection: An Xgboost Risk Assessment Model For Egyptian Patients, Toufik Mzili, Mourad Mzili, Saif Islam Bouderba, Ahmed Abatal, Widi Aribowo, Zahra Oughannou Jan 2025

The Role Of Artificial Intelligence In Early Tumor Detection: An Xgboost Risk Assessment Model For Egyptian Patients, Toufik Mzili, Mourad Mzili, Saif Islam Bouderba, Ahmed Abatal, Widi Aribowo, Zahra Oughannou

Mesopotamian Journal of Artificial Intelligence in Healthcare

This study developed an XGBoost-based risk assessment model to enhance early tumor detection among Egyptian patients, addressing the challenges of late diagnosis and limited healthcare resources. Utilizing a retrospective dataset of 178 patients, the model incorporated demographic, clinical, and biochemical variables, including AFP levels, viral hepatitis status (HBV/HCV), and liver function markers. The model demonstrated strong predictive performance, achieving an accuracy of 0.833, precision of 0.846, and an AUC of 0.86, though recall remained moderate (0.688), indicating room for improvement in identifying high-risk cases. Feature importance analysis highlighted AFP levels and hepatitis status as the most influential predictors, aligning with …

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Lightweight Deep Learning For Iot-Based Medical Diagnosis: A Survey, Balqees Talal Hasan, Ali Mohsin Ahmed Al-Sabaawi, Zaid J. Al-Araji Jan 2025

Lightweight Deep Learning For Iot-Based Medical Diagnosis: A Survey, Balqees Talal Hasan, Ali Mohsin Ahmed Al-Sabaawi, Zaid J. Al-Araji

Mesopotamian Journal of Artificial Intelligence in Healthcare

The rapid expansion of Internet of Things (IoT) technologies in healthcare has enabled continuous patient monitoring and data-driven clinical decision support. Deep learning models excel at extracting intricate patterns from high-dimensional medical data but face significant deployment challenges on resource-constrained IoT devices due to high computational and memory demands. This survey systematically analyzes four core strategies to adapt deep learning for edge based medical diagnosis: model pruning, quantization, knowledge distillation, and inherently lightweight architectures. For each approach, we dissect the underlying methodologies, evaluate the trade-offs between model efficiency and predictive performance, and present relevant medical case studies. We further review …

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Prediction Of Cardiac Arrest Using A Hybrid Voting Classifier Combining Random Forest And Support Vector Machine, Ahmed Hamid Elias, Amr Badr, Raad S. Alhumaima Jan 2025

Prediction Of Cardiac Arrest Using A Hybrid Voting Classifier Combining Random Forest And Support Vector Machine, Ahmed Hamid Elias, Amr Badr, Raad S. Alhumaima

Mesopotamian Journal of Artificial Intelligence in Healthcare

Early and adequate identification of patients at risk of cardiac arrest remains a fundamental challenge in medical environments. Within this piece of work, we design and evaluate a hybrid machine-learning pipeline integrating Random Forest (RF) and Support Vector Machine (SVM) classifiers by soft voting to detect cardiac arrest occurrences from a publicly available dataset of 303 patient records with 13 clinical features. After median imputation and data cleaning, continuous features were normalized and the cohort was divided into 70 % training and 30 % test sets. RF scored 97 % with an AUC of 1.00 on its own, and SVM …

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Data Mining Driven Segmentation Of Health Insurance Policyholders Using K-Means Clustering, Farah Ali Khairi, Laith Farhan, Oluwaseun A. Adelaja Jan 2025

Data Mining Driven Segmentation Of Health Insurance Policyholders Using K-Means Clustering, Farah Ali Khairi, Laith Farhan, Oluwaseun A. Adelaja

Mesopotamian Journal of Artificial Intelligence in Healthcare

This study illustrates a data‐driven approach to the segmentation of health insurance policyholders based on K-Means clustering of an open insurance dataset. Key demographic and financial features like age, body mass index (BMI), dependents, annual medical spending, and premium payment were normalized first to ensure comparability. The optimal number of clusters (k = 3) was determined using silhouette analysis, and three clusters were formed: (1) young, low‐cost individuals, (2) middle‐aged medium‐cost individuals, and (3) old, high‐cost individuals. Cluster centroids provide actionable profiles that can be utilized by insurers for target marketing, risk profiling, and development of customized plans. A set …

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Adaboost-Based Classification For Bone Sarcoma Outcome Prediction: A Comparative Machine Learning Approach, El-Sayed M. El-Kenawy, Ahmed Mohamed Zaki, Marwa M. Eid Jan 2025

Adaboost-Based Classification For Bone Sarcoma Outcome Prediction: A Comparative Machine Learning Approach, El-Sayed M. El-Kenawy, Ahmed Mohamed Zaki, Marwa M. Eid

Mesopotamian Journal of Artificial Intelligence in Healthcare

Bone sarcomas represent aggressive malignancies with complex clinical presentations where traditional prognostic methods often lack precision in predicting patient outcomes. This study developed and validated machine learning models for predicting bone sarcoma patient outcomes using a comprehensive dataset from Memorial Sloan Kettering Cancer Center spanning 2010-2020, comparing multiple algorithms including AdaBoost, Logistic Regression, Ridge Classifier, Quadratic Discriminant Analysis, and Linear Discriminant Analysis. AdaBoost demonstrated superior performance with 0.84 accuracy, 0.875 sensitivity, 0.7955 specificity, 0.8448 precision, 0.8333 negative predictive value, and 0.8596 F1 score, outperforming other algorithms which achieved 0.83 accuracy and 0.8496 F1 score. Statistical analysis confirmed significant differences between …

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Retinal Vessel Image Segmentation Based On Guided Filter And Neural Networks, Ibrahem Al-Jadir, Jawad S. Hameed, Husam Yahya, Dalia N. Al-Oboudi Jan 2025

Retinal Vessel Image Segmentation Based On Guided Filter And Neural Networks, Ibrahem Al-Jadir, Jawad S. Hameed, Husam Yahya, Dalia N. Al-Oboudi

Mesopotamian Journal of Artificial Intelligence in Healthcare

Accurate segmentation of retinal blood vessels is essential for the early diagnosis and monitoring of various ophthalmic and systemic diseases. This work shows how to use a modified U-Net architecture to improve how retinal blood vessels are segmented. The method uses rolling guidance filters with two different sets of parameters to make the edges of the vessels sharper and make it easier to tell them apart. Separate U-Net models process each filtered image independently, and then the outputs are combined to make the final segmentation result. Both networks are trained at the same time using a shared loss function. Tests …

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Biogpt: A Generative Transformer-Based Framework For Personalized Genomic Medicine And Rare Disease Diagnosis, Ghada Al-Kateb, Emine Cengiz, Murat Gök Jan 2025

Biogpt: A Generative Transformer-Based Framework For Personalized Genomic Medicine And Rare Disease Diagnosis, Ghada Al-Kateb, Emine Cengiz, Murat Gök

Mesopotamian Journal of Artificial Intelligence in Healthcare

This paper introduces BioGPT, a generative transformer-based framework designed to advance personalized genomic medicine and rare disease diagnosis. Unlike conventional models that process either genomic sequences or clinical narratives in isolation, BioGPT employs a cross-modal architecture that effectively fuses both data streams, enabling precise classification and interpretable natural language generation. The model is pre-trained on large-scale genomic and electronic health record datasets and fine-tuned for rare disease tasks. Comprehensive experiments demonstrate BioGPT’s superiority over state-of-the-art biomedical models, including RarePT, BioBERT, and DNABERT, with improvements of up to 10% in F1-score and over 20 BLEU points in justification fluency. Ablation studies …

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Optimizing Decision Tree Classifiers For Healthcare Predictions: A Comparative Analysis Of Model Depth, Pruning, And Performance, Hussein Alkattan, Ali Subhi Alhumaima, Mohammed Shakir Mohmood, Ghassan Dhahir Mohammed Al-Thabhawee Jan 2025

Optimizing Decision Tree Classifiers For Healthcare Predictions: A Comparative Analysis Of Model Depth, Pruning, And Performance, Hussein Alkattan, Ali Subhi Alhumaima, Mohammed Shakir Mohmood, Ghassan Dhahir Mohammed Al-Thabhawee

Mesopotamian Journal of Artificial Intelligence in Healthcare

This study presents of Decision Tree classifiers for predictive modeling in medicine, focusing on model depth optimization, pruning techniques, and performance evaluation. On the basis of a synthetic healthcare dataset containing over 55,000 records, each with features such as age, gender, blood type, bill amount, and medical condition, we investigate the impact of varying tree depth from 1 to 5 on predictive accuracy, interpretability, and generalizability. Shallow models have strong transparency but poor classification strength, and deep models obtain stronger interactions but suffer from overfitting. With pruning, we find a balance between model simplicity and precision and yield strong classifiers …

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Clinical Data Analysis Using Machine Learning Algorithms To Predict The Progression Of Type 2 Diabetes, Niki Syrou, George Mpourazanis, Panagiotis Tsirkas, Jovanna Adamopoulou, Jovanna Adamopoulou Jan 2025

Clinical Data Analysis Using Machine Learning Algorithms To Predict The Progression Of Type 2 Diabetes, Niki Syrou, George Mpourazanis, Panagiotis Tsirkas, Jovanna Adamopoulou, Jovanna Adamopoulou

Mesopotamian Journal of Artificial Intelligence in Healthcare

Type 2 diabetes mellitus (T2DM) is a growing global health concern requiring early detection strategies. This study applies a Random Forest machine learning model to predict diabetes progression using a structured clinical dataset of 100 patients. The dataset includes demographic, physiological, and biochemical variables such as age, BMI, blood pressure, glucose levels, and lipid profiles. After preprocessing and training, the model achieved strong performance metrics: accuracy of 0.80, precision of 0.84, recall of 0.94, and an AUC of 0.88. Feature importance analysis revealed that systolic blood pressure, fasting glucose, and BMI are the most critical predictors. These findings are consistent …

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Leveraging Artificial Intelligence To Enhance Nursing Workflow In Endoscopy Units, Ribwar Arsalan Mohammed, Sirwan Khalid Ahmed, Abdulqadir J. Nashwan Jan 2025

Leveraging Artificial Intelligence To Enhance Nursing Workflow In Endoscopy Units, Ribwar Arsalan Mohammed, Sirwan Khalid Ahmed, Abdulqadir J. Nashwan

Mesopotamian Journal of Artificial Intelligence in Healthcare

This paper explores the potential of Artificial Intelligence (AI), including natural language processing (NLP), computer vision, and robotics, to transform nursing workflow in endoscopy units. AI technologies offer numerous advantages, including predictive analytics for efficient scheduling and resource allocation, real-time decision support during procedures, and enhanced diagnostic accuracy through image analysis. Additionally, AI simplifies administrative tasks such as patient scheduling and medical documentation, improving operational efficiency and patient safety. Despite these benefits, challenges related to data privacy, algorithm accuracy, and ethical considerations must be addressed. Effective AI integration requires interdisciplinary collaboration, continuous education for nurses, and robust governance frameworks to …

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Reimagining Intelligence: A Comprehensive Review Of Human-Centric Ai Systems And Their Societal And Healthcare Integration, Zakaria Benlalia, Ghada Al-Kateb, Mourad Mzili, Konstantina Diamanti Jan 2025

Reimagining Intelligence: A Comprehensive Review Of Human-Centric Ai Systems And Their Societal And Healthcare Integration, Zakaria Benlalia, Ghada Al-Kateb, Mourad Mzili, Konstantina Diamanti

Mesopotamian Journal of Artificial Intelligence in Healthcare

Human-Centric Artificial Intelligence (HCAI) is rapidly emerging as a transformative paradigm, shifting the focus of AI development from mere algorithmic optimization to ethical alignment, user trust, and societal integration most notably within the critical domain of healthcare. This review offers a comprehensive examination of the principles, architecture, challenges, and future directions underpinning HCAI, with an emphasis on its applications in health-related contexts. We begin by exploring the conceptual foundations of human-centricity, including core values such as transparency, fairness, autonomy, and privacy, all of which are essential in sensitive environments like clinical decision-making and patient data management. The paper then surveys …

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Artificial Intelligence-Powered Robotic Technology For Transforming Palliative Care, Adebo Thomas, Asiku Denis, Wamusi Robert, Simon Peter Kabiito, Zaward Morish, Aziku Samuel, Malik Sallam, Ioannis Adamopoulos Jan 2025

Artificial Intelligence-Powered Robotic Technology For Transforming Palliative Care, Adebo Thomas, Asiku Denis, Wamusi Robert, Simon Peter Kabiito, Zaward Morish, Aziku Samuel, Malik Sallam, Ioannis Adamopoulos

Mesopotamian Journal of Artificial Intelligence in Healthcare

Palliative care seeks to improve the quality of life of patients with life-threatening illnesses by addressing their physical, emotional, and psychological needs. However, global challenges such as workforce shortages, limited access to specialized care, and inconsistent care quality demand innovative solutions. Advances in artificial intelligence (AI)-powered robotics offer transformative potential to overcome these barriers and strengthen palliative care delivery. This study explores how AI-driven robotic technologies support palliative care through applications in symptom monitoring, clinical decision-making, emotional companionship, and personalized care planning. It reviews cutting-edge robotic systems, including assistive, companion, diagnostic, nursing, procedural, service, and rehabilitation robots. Enabled by machine …

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An Artificial Intelligence Model For Predicting Hospital Readmission Using Electronic Health Records Data, Rezarta Cara, Klodian Dhoska, Fjona Cara, Fredrick Kayusi, Linety Juma Jan 2025

An Artificial Intelligence Model For Predicting Hospital Readmission Using Electronic Health Records Data, Rezarta Cara, Klodian Dhoska, Fjona Cara, Fredrick Kayusi, Linety Juma

Mesopotamian Journal of Artificial Intelligence in Healthcare

This study investigates the application of a machine learning model—specifically the Light Gradient Boosting Machine (LightGBM)—to predict 30-day hospital readmissions using structured electronic health record (EHR) data. Hospital readmissions remain a critical challenge in healthcare systems, often indicating gaps in continuity of care and contributing to higher costs. By leveraging demographic, clinical, and diagnostic variables from 350 anonymized patient records, the model aimed to accurately identify individuals at high risk of readmission. Key features included age, number of previous admissions, length of stay, number of medications, chronic disease status, and gender. Data preprocessing, model training, and evaluation were conducted using …

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