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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.
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.
Nov 2021 DOI 10.14302/issn.3070-3360.ijco-21-3995
U. Nwoha PolycarpCorresponding author
Centre for Scientific Investigations and Training, Owerri, Imo State, Nigeria
Proceeding to hospital immediately stroke occurs is important for early intervention that would minimize the consequences of stroke. But most stroke patients in developing countries prefer herbal centers than hospital. Reasons for this attitude have not been established. Two well-trained assistants were used to interview 117 stroke survivors who attended Bebe Herbal Center (BHC) in Nigeria for at least two visits. The survivors self-reported their experiences in hospitals visited and at BHC. Data obtained were analyzed using Independent t-test, Pearson’s chi-squared test, on SPSS package version 23. Significant value was set at p<0.05. Results showed the survivors comprised 48.7% males and 51.3% females, with mean age 63.98±10.41 years (range: 40-84 years). Following onset of stroke, 61.5% went firstly to hospital, 21.4% to traditional healing places, and 17.1% to BHC. Eventually all survivors went to BHC and 99.1% said they were satisfied with treatment received at BHC. Seventy-nine (68.1%) said they experienced substantial recovery under one month, 25.9% between 1-6 months. All the survivors who went firstly to hospitals said they received inadequate care in them. None of the hospitals they visited had CT or MRI equipment. Pearson’s chi-squared test showed that the impact of stroke had a significant difference between males and females regarding checking of blood pressure after stroke (χ2=7.62; df=3; P<0.05). The inadequate care received in hospitals and the early satisfactory recovery in BHC influence stroke patients in Nigeria to reject going to hospital.
Apr 2018 DOI 10.14302/issn.2766-6204.jmpt-18-2079
Young CeciliaCorresponding author
Independent Researcher, 105A, 1/F Liberte Place, 833 Lai Chi Kok Road, Kowloon, Hong Kong
A review links maxillofacial trauma to psychological stress responses. It discusses screening, early interventions, and multidisciplinary care to improve recovery.
Aug 2016 DOI 10.14302/issn.2474-9273.jbtm-16-1202
V.J. Basso RobertCorresponding author
RSW Faculty of Social Work, Wilfrid Laurier University
Our objective was the early identification, assessment and treatment of aggression by primary school children four to eleven years old, with the goal of preventing school expulsion. The children were identified by teachers and other professionals for their aggressive behavior. Children were assessed for five symptoms which are linked to the development and persistence of social and/or physical aggressive behaviors: inattention, hyperactivity, anxiety, poor social functioning, and oppositional behavior. Long term follow-up continued for up to 9 years. Conners’ Scales for parents and teachers were used to assess the severity of predisposing symptoms and emotional lability. The children were treated with psychosocial and pharmacological interventions by social workers and a physician, in addition to utilizing community and school resources. Teachers reported a reduction in some of the predisposing symptoms: hyperactivity, emotional lability, oppositionality, and improved social functioning. Parents reported improvements in all five of the children’s physically aggressive behaviors. Early intervention for children’s aggressive behaviors was found to be effective. None of the children in the study were expelled from school.