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Jun 2026 DOI 10.14302/issn.2642-9241.jrd-26-6332
de Melo PhilipCorresponding author
Respiratory diseases remain a major contributor to hospital morbidity and mortality worldwide, particularly among elderly patients and individuals with severe pulmonary compromise. Accurate prediction of respiratory mortality is clinically important for triage, resource allocation, ICU utilization, and early intervention. Traditional statistical models frequently demonstrate limited predictive sensitivity because respiratory mortality is influenced by complex interactions among demographic, diagnostic, physiologic, and severity-related variables. In this study, a machine learning framework was developed to predict in-hospital mortality among patients with respiratory disease using administrative and clinically derived variables, including age, sex, length of stay (LOS), diagnostic descriptions, risk of mortality and severity scores. A Random Forest classifier with balanced class weighting was developed and implemented to address nonlinear relationships and class imbalance within the dataset. Initial modeling demonstrated good overall discrimination performance, with receiver operating characteristic area under the curve (ROC-AUC) values approaching 0.84; however, mortality recall remained limited because deceased patients represented a minority class within the original dataset. To improve mortality detection, a physiologically informed synthetic augmentation strategy was developed. Synthetic clinical variables included oxygen saturation, ICU status, ventilator support, sepsis status, systolic blood pressure, creatinine, and lactate levels. Conditional physiologic consistency rules were incorporated during augmentation to preserve clinically plausible relationships among respiratory failure, hemodynamic instability, and organ dysfunction. The augmented dataset substantially improved model sensitivity and balanced mortality classification performance. Final model evaluation demonstrated strong predictive capability, achieving approximately 97% classification accuracy with balanced precision and recall across mortality classes. Confusion matrix analysis revealed marked reduction in false-negative mortality predictions compared with baseline modeling approaches. Feature importance analysis identified physiologic instability markers, respiratory severity classifications, LOS, and diagnostic respiratory categories as dominant predictors of mortality. These findings suggest that hybrid simulation-augmented machine learning frameworks may provide a valuable strategy for respiratory mortality analytics, particularly in datasets with limited real-world mortality prevalence and incomplete physiologic representation.
Jan 2026
Kaur KirandeepCorresponding author
Background Female infertility has always been an issue and social stigma in India and it’s not only the matter of physical health of the female but also related to psychological health and the social status of the women. The diagnosis of this problem criterion requires medical symptoms for the condition, including clinical and metabolic parameters. The application of AI-driven algorithms into medical science is responsible for the specific phrase Medical Artificial Intelligence (MAI) which has introduced a transformative swing in female reproductive health, qualifying higher diagnostic accuracy, reproducibility, and efficiency in addressing complex conditions such as infertility and hormonal disorders. From Ovarian stimulation to Artificial Intelligence in Assisted Reproductive Technology, there have been significant advancements in the incorporation of machine learning and IoT in the medical Labs. AI algorithms are expected to bring about a more calculated, computed, and standardized approach to the infertility problem today females are facing. Aim This review aims to establish connection between female infertility problems with AI and discusses how an effort can be made to resolve such sensitive health issue, keeping in view the recent and past contribution of emerging AI technologies in health sciences. The AI trained models are based on either supervised learning algorithms of unsupervised learning algorithms. Basically the input to the medical trained AI models is the set of instances referred to as medical dataset which can be categorized, clustered or correlated as per requirements. Each instance is further described by the values of a set of attributes like diagnosis, treatment, laboratory tests data, prescription drugs etc. These medical dataset can be further represented in the form of matrix and later can be used to aid learning methods that is supervised or unsupervised learning methods to train AI medical models. That is why the term MAI is being used in this review paper. Methods Used Studies were explored across IEEE Xplore, PubMed, ResearchGate, SpringerLink and npj Digital Medicine for papers published. Following the database probing, identical articles were pull out and the lasting titles and abstracts were inspected for suitability. Studies were incorporated if they portrayed the role of Artificial Intelligence for female reproductive health or infertility problems in reproductive age. Result Following data was extracted from 12 selected articles. These studies were eventually chosen for complete estimation. Conclusion AI has proved to be an incomparable assistance tool in providing specialist approach in the reproductive health issues in females. But there is need to describe ways to develop a framework of wearable technology in the form of sensor to predict correct ovulation period.
Dec 2025 DOI 10.14302/issn.2641-4538.jphi-25-5886
Y. Talbert PatriciaCorresponding author
Objective Diabetes poses significant public health challenges, with many individuals remaining undiagnosed and at risk of complications. This study aimed to evaluate the performance of decision tree ensemble methods for predicting diabetes onset using the Framingham Heart Study Teaching Dataset and to explore sex-specific risk patterns relevant to AI-driven interventions. Methods We analyzed data from 11,627 participants, incorporating demographics, vital signs, smoking status, medication use, and laboratory measures. Random Forest classifiers were developed to predict diabetes incidence at approximately 6-year (Period 2) and 12-year (Period 3) follow-ups. Class imbalance was addressed using undersampling, oversampling, and the Synthetic Minority Over-sampling Technique (SMOTE). Results The models demonstrated robust performance, achieving an Area Under the Curve (AUC) of 0.856 in Period 2, and moderate predictive ability in Period 3 (AUC = 0.732 in males, 0.786 in females). Key predictors included glucose level, BMI, systolic blood pressure, age, and heart rate. Notably, differences emerged in predictive accuracy between men and women, suggesting potential sex-specific vulnerabilities that merit further study. Conclusion Machine learning approaches, particularly Random Forests, show promise for medium- and long-term diabetes risk prediction, supporting early identification and intervention efforts. Future work should focus on hyperparameter tuning and explainability techniques, such as SHapley Additive exPlanations (SHAP) values, to improve model precision, interpretability, and fairness. Equity-focused strategies remain critical to ensure AI-driven tools benefit diverse populations and do not exacerbate existing disparities in diabetes care.
May 2024 DOI 10.14302/issn.2470-5020.jnrt-24-5100
T. Adebisi AbdulyekeenCorresponding author
Exploring the dynamic dimension of functional connectivity in dementia, this article departs from traditional static studies to capture the ever-changing brain networks. Investigating temporal connectivity patterns yields valuable insights into disease progression, individualized treatment, and early intervention. Additionally, the concept of cognitive reserve, therapeutic interventions, and machine learning integration are pivotal in revolutionizing dementia research and care.
May 2024 DOI 10.14302/issn.2998-1506.jpa-24-5058
Shrestha SwatiCorresponding author
Wheat is a staple grain crop in the United States and around the world. Weed infestation, particularly grass weeds, poses significant challenges to wheat production, competing for resources and reducing grain yield and quality. Effective weed management practices, including early identification and targeted herbicide application are essential to avoid economic losses. Recent advancements in unmanned aerial vehicles (UAVs) and artificial intelligence (AI), offer promising solutions for early weed detection and management, improving efficiency and reducing negative environment impact. The integration of robotics and information technology has enabled the development of automated weed detection systems, reducing the reliance on manual scouting and intervention. Various sensors in conjunction with proximal and remote sensing techniques have the capability to capture detailed information about crop and weed characteristics. Additionally, multi-spectral and hyperspectral sensors have proven highly effective in weed vs crop detection, enabling early intervention and precise weed management. The data from various sensors consecutively processed with the help of machine learning and deep learning models (DL), notably Convolutional Neural Networks (CNNs) method have shown superior performance in handling large datasets, extracting intricate features, and achieving high accuracy in weed classification at various growth stages in numerous crops. However, the application of deep learning models in grass weed detection for wheat crops remains underexplored, presenting an opportunity for further research and innovation. In this review we underscore the potential of automated grass weed detection systems in enhancing weed management practices in wheat cropping systems. Future research should focus on refining existing techniques, comparing ML and DL models for accuracy and efficiency, and integrating UAV-based mapping with AI algorithms for proactive weed control strategies. By harnessing the power of AI and machine learning, automated weed detection holds the key to sustainable and efficient weed management in wheat cropping systems.
Dec 2018 DOI 10.14302/issn.2641-5526.jmid-18-2488
Luis Fernández-Martínez JuanCorresponding author
Group of Inverse Problems, Optimization and Machine Learning. Department of Mathematics, Universidad de Oviedo, Oviedo, Asturias, Spain
Background: Triple Negative Breast Cancer (TNBC) is a type of breast cancer with very bad prognosis. Predicting the histological grade (HG) and the lymph nodes metastasis is crucial for developing more suitable treatment strategies. Methods: We present the main clinical and pathological variables to predict the histological grade and lymph nodes metastasis via novel machine learning techniques. These variables are currently being used for prognosis and treatment in medical practice. This analysis was performed using a database of 102 Caucasian women diagnosed with TNBC. The results were cross-validated using random simulations of this dataset. Results: HG was predicted with an accuracy of 93.8% using a list of 6 prognostic variables with significant implications: Ki67 expression, use of Oral contraceptives, Col11A1 expression, Col11A1 score, E-cad truncated and Tumor size. The lymph nodes metastasis was predicted with an accuracy of almost 85% using only 6 prognostic variables: Vascular invasion, Tumor size, Perineural invasion, Age at diagnosis, Ki67 expression, and Col11A1 score. This analysis also served to establish the median signatures of the groups with and without lymph node metastasis, and proved the existence of a kind of small-size tumors (around 2.15 cm) with lymph node metastasis but not showing vascular and perineural invasions and higher protein Col11A1 score. Besides, these signatures proved to be very stable. Conclusions: The additional information conveyed by the prognostic variables found in these two classification problems provides new insight about the genesis and progression of this disease and can be used in medical practice to improve decisions in patient diagnosis and further treatment.