Vol 14 Issue 1 April 2026-September 2026

Abstract: Persistent demographic imbalance in U.S. clinical trials continues to undermine external validity, equity in treatment outcomes, and regulatory confidence in therapeutic efficacy across heterogeneous populations. This study proposes a novel Constrained Multi-Objective Fair Representation Optimization (CMOFRO) framework designed to simultaneously maximize predictive performance and demographic fairness in clinical trial recruitment and cohort construction. The framework integrates constrained optimization theory with advanced machine learning techniques to address representation bias across protected attributes such as race, gender, age, and socioeconomic status.

The proposed model formulates trial cohort selection as a multi-objective optimization problem, where competing objectives include (i) maximizing statistical power and predictive accuracy of clinical outcomes, (ii) minimizing demographic disparity metrics such as Demographic Parity Difference (DPD) and Equal Opportunity Gap (EOG), and (iii) ensuring regulatory-compliant representation thresholds. A hybrid architecture combining Pareto-efficient optimization, fairness-aware gradient boosting, and a constraint-regularized deep neural network (CR-DNN) is developed. The system leverages real-world datasets including synthetic EHR-derived cohorts and publicly available trial registries from ClinicalTrials.gov. To benchmark performance, CMOFRO is compared against established fairness-aware algorithms including FairBatch, Adversarial Debiasing, Reweighing, and Pareto Multi-Task Learning (PMTL). Experimental results demonstrate that CMOFRO achieves a 23–31% reduction in demographic disparity metrics while maintaining or improving predictive performance (AUC: 0.87–0.92) relative to baseline models. Notably, the proposed model reduces underrepresentation of minority subgroups by up to 28% in simulated enrollment scenarios without compromising statistical robustness. Graph-based visualizations, including Pareto frontiers and fairness–accuracy trade-off curves, reveal that CMOFRO consistently operates in the optimal efficiency region compared to competing methods. Furthermore, the framework incorporates a dynamic constraint adaptation mechanism that adjusts fairness thresholds in response to evolving recruitment patterns, enabling real-time optimization in decentralized and hybrid clinical trial environments. Sensitivity analysis confirms the robustness of the model across varying population distributions and missing data conditions. The results suggest that integrating constrained multi-objective learning into clinical trial design can significantly enhance inclusivity while preserving scientific rigor. This research contributes a scalable and interpretable solution for regulatory-compliant, fairness-aware clinical trial optimization, with implications for FDA-aligned diversity initiatives and precision medicine advancement.

Keywords: Algorithmic Fairness; Clinical Trial Optimization; Multi-Objective Learning; Demographic Representation; Fairness-Constrained Machine Learning.

Title: Algorithmic Fairness and Demographic Representation Optimization in U.S. Clinical Trials Using Constrained Multi-Objective Learning

Author: Ifiala Agwu Ifiala, Onuh Matthew Ijiga, Emmanuel Igba

International Journal of Healthcare Sciences

ISSN 2348-5728 (Online)

Vol. 14, Issue 1, April 2026 - September 2026

Page No: 40-57

Research Publish Journals

Website: www.researchpublish.com

Published Date: 20-April-2026

Abstract: The rapid integration of artificial intelligence (AI) into military operations has transformed the conduct of warfare, raising significant ethical, legal, and public health concerns. However, existing scholarship has largely examined AI warfare and public health in isolation, with limited attention to the governance mechanisms linking these domains. The purpose of this study was to examine how ethical governance and regulatory frameworks shape the public health implications of AI-enabled warfare and to develop a governance-centered conceptual framework connecting AI capabilities to population health outcomes. This study employed a qualitative policy analysis and structured narrative review of international legal instruments, policy documents, and peer-reviewed literature. Data were analyzed using thematic coding and comparative policy analysis, guided by the AI Warfare–Public Health Impact (AIW–PHI) framework. Findings indicate that current governance structures are fragmented, lack enforceable mechanisms, and remain insufficiently adapted to the complexities of AI-enabled warfare. Ethical governance mechanisms—such as human oversight, accountability, and transparency—are inconsistently implemented, while public health considerations are largely absent from existing regulatory frameworks. These gaps contribute to increased health system vulnerability and heightened risks to civilian populations. This study introduces the AIW–PHI framework as a novel, governance-centered model that conceptualizes AI-enabled warfare as a structural determinant of population health mediated by governance processes. The findings underscore the need to integrate public health into AI governance and to strengthen regulatory approaches to mitigate systemic harm. This study contributes to emerging scholarship at the intersection of AI, governance, and public health and provides a foundation for future empirical research and policy development.

Keywords: Artificial intelligence; AI-enabled warfare; ethical governance; international humanitarian law; public health; health system vulnerability; population health; autonomous weapons; military AI; governance frameworks; civilian protection; AI regulation.

Title: Artificial Intelligence Warfare and Public Health: A Governance Framework for Evaluating Population Health Implications

Author: Dr. David Bull

International Journal of Healthcare Sciences

ISSN 2348-5728 (Online)

Vol. 14, Issue 1, April 2026 - September 2026

Page No: 21-39

Research Publish Journals

Website: www.researchpublish.com

Published Date: 15-April-2026

Abstract: The study aimed to assess the depressive symptoms and quality of life (QOL) among patients undergoing hemodialysis in a tertiary care hospital at Kolkata. A cross-sectional descriptive study approach was used. Fifty patients with chronic kidney disease (CKD) receiving hemodialysis were chosen by non-probability purposive sampling. Data were gathered using a structured demographic questionnaire, Beck’s Depression Inventory (BDI-II), and the World Health Organization Quality of Life-BREF (WHOQOL-BREF) scale. Data analysis was conducted using SPSS version 20, including both descriptive and inferential statistics. Out of 50 participants, 40 (80%) exhibited depressive symptoms. The overall QOL was found to be poor (p<0.05). The social relationships domain had the highest mean score (44.82±19.66). Males showed better QOL than females, while older age, associated illness, and reliance on caregivers negatively affected physical and social domains. A strong negative correlation (r = -1) was observed between depressive symptoms and QOL.  High prevalence of depressive symptoms and poor QOL were evident among hemodialysis patients. Factors such as gender, age, comorbid conditions, and mobility level significantly influenced QOL. Early psychological screening and supportive interventions are essential to enhance well-being of this population.

Keywords: Chronic Kidney Disease, Hemodialysis, Depressive symptoms, Quality of Life, Beck Depression Inventory.

Title: A Study to Assess the Depressive Symptoms and Quality of Life among Patients Undergoing Hemodialysis in a Tertiary Care Hospital at Kolkata

Author: Maj Kanchan Pal, Brig Subhashini K R, Lt Col Sreekala B, Maj Shaliya Saleem

International Journal of Healthcare Sciences

ISSN 2348-5728 (Online)

Vol. 14, Issue 1, April 2026 - September 2026

Page No: 8-20

Research Publish Journals

Website: www.researchpublish.com

Published Date: 03-April-2026

Abstract: Background: Lateral epicondylitis, commonly known as tennis elbow, is a frequently occurring musculoskeletal disorder characterized by elbow pain and reduced functional capacity. It is often accompanied by non-specific neck pain, likely due to the biomechanical relationship between the cervical spine and the upper limb.

Objective: To determine the effectiveness of the Tyler Twist technique, inhibition–ischemic compression, and conventional therapy in alleviating pain and enhancing functional outcomes in individuals with lateral epicondylitis associated with non-specific neck pain.

Methodology: A quasi-experimental study using a one-group pre-test–post-test design was carried out on individuals aged 30–40 years diagnosed with lateral epicondylitis and non-specific neck pain. A total of 36 participants underwent interventions including the Tyler Twist technique, inhibition–ischemic compression technique, and conventional physiotherapy. Outcome measures included the Numerical Pain Rating Scale (NPRS), Patient-Rated Tennis Elbow Evaluation (PRTEE), Neck Disability Index (NDI), grip strength, and Myofascial Diagnostic Scale (MFDS). Data were analysed using descriptive statistics.

Results: The experimental group exhibited significant improvements in pain levels, muscle strength, and functional performance following the intervention.

Conclusion: The combined use of the Tyler Twist technique, inhibition–ischemic compression, and conventional therapy is effective in the management of lateral epicondylitis associated with non-specific neck pain.

Keywords: Lateral epicondylitis, non-specific neck pain, Neck Disability Index, Numerical Pain Rating Scale, Patient-Rated Tennis Elbow Evaluation, Myofascial Diagnostic Scale.

Title:  Effectiveness of Tyler Twist Technique, Inhibition – Ischemic Compression Technique and Conventional Therapy in managing Lateral Epicondylitis with Non-Specific neck pain

Author: Amisha Kapoor, Mansi Tuli

International Journal of Healthcare Sciences

ISSN 2348-5728 (Online)

Vol. 14, Issue 1, April 2026 - September 2026

Page No: 1-7

Research Publish Journals

Website: www.researchpublish.com

Published Date: 03-April-2026