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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.
Jun 2023 DOI 10.14302/issn.2766-8681.jcsr-23-4526
Isea RaúlCorresponding author
A large volume of data is being generated in public administration and it is necessary to develop new computational methodologies to classify and analyze it to do a better analysis and decision making. For this reason, the goal of this paper is to present a computational methodology that allows classifying and prioritizing a series of complaints using Artificial Intelligence techniques. To test this model, we generate 600 complaints in four sectors of the public administration to prove the concept. Later, we calculated the tree decision with the help of the Confusion Matrix, and finally the Priority Matrix (based on the Eisenhower model) allows setting priorities on the complaints, and offers the possibility of delegating and even postponing the response to them. In this way, it is possible to prioritize the complaints made in the public administration.
May 2021 DOI 10.14302/issn.2997-1969.ijhs-21-3814
E.E Enwereji,Corresponding author
College of Medicine and Health Sciences, Abia State University, Uturu Abia State.
Introduction Adolescence is a critical stage in human development that is characterized by peer pressure, confusion, exuberance and experimentation, particularly with sexual relationships. This is why attention should be paid to adolescents’ reproductive health issues so as to reduce their exposure to aggressive sexual activities which could expose them to sexually transmitted diseases, unwanted pregnancies and others. This study aimed at reducing the factors and conditions that influence teenage pregnancy among in-school adolescents in Umuahia North LGA of Abia State. Materials and Methods The study used a cross sectional descriptive study. A randomly selected sample of 416 adolescents between the ages of 13-19 years were studied. Structured self-administered questionnaire was used for data collection. Descriptive statistics, using frequencies, percentages and means were utilized for data analysis. Results The study found that 198 (47.6%) of the respondents were sexually active and that 89 (45%) of them had been pregnant. More than half 103 (52%) of the study group indicated that peer pressure influenced their sexual activities. About 46(51.7%) of the adolescents said they were pregnant so as to keep the new born baby with motherless babies homes. Conclusion Therefore, there is need for increased sex education for in-school adolescents so as to highlight the effects of teenage pregnancy on adolescents.
May 2020 DOI 10.14302/issn.2642-3146.jec-20-3359
K.G. Palmer WilliamCorresponding author
Independent Researcher, TRI-LEA-EM, 76 Sideroad 33-34, RR 5, Paisley, ON N0G 2N0, Canada.
For 5 years, since the start-up of an array of 140 wind turbines, residents have filed complaints with the Ontario Ministry of the Environment (the regulator), and K2 Wind (the operator). Residents complained that the turbines produce a tonal sound, and that the irritation this produced impacted their sleep, their health, and the enjoyment of their property. To confirm tonality from the wind turbines, this research examined over 200 data examples from two families. These families collected data by two independent methods, a continuously recording system, and by making selected audio recordings. The recorded data was correlated with the wind turbine operational performance, and local weather conditions. The correlated data was analyzed for tonality using international standard evaluation methods. The analysis confirmed over 84% correlation between complaints of irritating conditions, and tonality from 5 dB to over 20 dB. Finally, the results showed that the assumption of the regulator to only require assessment of compliance when the resident was downwind of the nearest wind turbine was incorrect. Most complaints arose from other wind directions. Neither was the regulator’s assumption correct that curtailing the wind turbine operation to continue operating at only partially reduced outputs would give remediation. The research concludes that tonality arises consistent with the wind turbine operation, identifying a critical need to revise the practices to prevent chronic irritation. In the original issuance of this paper, the author inadvertently erred by stating that there was a difference between the method for assessing wind turbine tonality of an expert group panel and the method now prescribed by regulations. That statement was incorrect, and the author apologizes for the error and for any confusion it may have caused. The error, miscalculations resulting from the error, where necessary conclusions drawn from erroneous calculations are corrected.
Mar 2020 DOI 10.14302/issn.2574-612X.ijpr-20-3213
Y Trivedi GunjanCorresponding author
Co-founder, Society for Energy & Emotions, Wellness Space, Ahmedabad, India
Introduction Scientific evidence has established the benefits of meditation and sound vibrations on emotional and physiological health. Aim of the Study The study explored changes in mood and Heart Rate Variability (HRV) after HSB Sound Bath Meditation on healthy individuals. The objectives of the study were to understand if a 40-minute-long seated HSB Sound Bath Meditation results in changes (a) in mood measured via Positive And Negative Affect Scale (PANAS) and Abbreviated Profile of Mood States (POMS) Survey and (b) in physiological parameters, as measured by HRV. Methods The psychological parameters were measured with PANAS (N=77) and Abbreviated POMS, (N=17). The physiology was measured with HRV parameters such as Heart Rate (HR), Stress Index (SI) and Root Mean Square of Standard Deviation (RMSSD) using the EmWave Pro device (N=15). HRV data analysis was conducted with Kubios HRV Premium and analyzed using a paired T-Test. Results All the subjects after meditation showed improvement in Positive Affect (PA) and a reduction in Negative Affect (NA). The HRV parameters showed a trend showing overall relaxation with a significant reduction in HR, SI and an increase in RMSSD. Consistent with changes in positive, negative mood and HRV, all the participants showed a reduction in tension, anger, fatigue, depression and confusion and improvement in esteem related affect and vigor. Conclusion The findings show that seated HSB Sound Bath Meditation session has a positive impact on mood-related measures and physiology. Future work in this area could explore comparison with a control group and a longer study duration comprising multiple sessions.
Aug 2019 DOI 10.14302/issn.2640-690X.jfm-19-2989
Blondon K.Corresponding author
Division of General Internal Medicine, University Hospitals of Geneva, Geneva, Switzerland
Background Medication adherence remains a challenge for patient management. Changes in the drug regimen after a hospital stay can lead to confusion or misunderstandings. We implemented a structured patient-centered interview during which a computer-generated individualized medication plan was discussed and provided to patients at discharge. Objective To explore whether a medication plan can be a quality indicator, in terms of its content (quality) and its implementation in the resident’s workflow (feasibility). Methods An observational mixed method study with interviews of 174 patients from general internal medicine wards at 1 week and 1 month after discharge, and of 91 physicians at baseline. We report the quality of the medication plan in terms of content and state of completion. We describe feasibility for residents to complete this plan, as well as patient and resident satisfaction with the plan. Results 83% of participants received a medication plan. Physicians verified renal function (83%) to adapt doses but did not regularly assess for medication interactions (43%). Incomplete plans (61%), were due to blanks when physicians considered the information irrelevant for their patients. Error rate was <3%. Patients reported low use of their plan after discharge (64% found it useful after 1 week, whereas only 37% used it when taking their medication 1 week after discharge). Conclusion Although the plans were considered useful by both patients and physicians, their implementation could have been optimized by considering the overall process (creation to patient use). Mobile apps could help fill gaps in supporting patients for medication adherence.
Dec 2018 DOI 10.14302/issn.2578-8590.ipj-18-2532
Habibzadeh NasimCorresponding author
PhD in Sport Science, Department of Sport Science, Teesside University, UK
Body mass index (BMI) seemingly is an important scale for the body types determination in individual with different ethnicity. Accordingly, individual with BMI< 18.5 are classified as slim or underweight and people with BMI between 18.5 -24.9 are called normal body types. Subsequently, those individual with BMI between 25-29.9 are categorized as overweight and people with BMI > 30 are classified as obese people. Nonetheless, important question is where the muscular individual are located in this BMI scale ? Macular induvial also called overweight or obese in BMI scale which can create kind of confusion for induvial because the might try to lose weight whilst they do not actually need it. Thus ,it seems BMI measure is not sensible measure for muscular induvial as otherwise the can be at risk of health problems in various ways. Uses of the another apparatus such an ordinary weight scale or computational devices which could estimate the body type according to the BMI more accurately can be helpful.
Aug 2018 DOI 10.14302/issn.2470-5020.jnrt-18-2258
Meyer Peter-WolfgangCorresponding author
Department for General Psychiatry, Center of Psychosocial Medicine, University of Heidelberg, Heidelberg, Germany
We present a case of a 77-year-old male patient who was treated in our outpatient clinic for memory disorders because of episodic confusion and retrograde amnesia. The patient reported having symptoms repeatedly following intraocular treatment with Anti-Vascular Endothelial Growth Factor Agents (Ranibizumab and Bevacizumab) as a treatment for wet macular degeneration. EEG showed a localized deceleration that intensified under prolonged voluntary hyperventilation. Symptoms resolved after the intraocular Anti-Vascular Endothelial Growth Factor treatment was stopped and anticonvulsive treatment with lamotrigine was begun. This case is important in that it describes a potential association between intraocular treatments with Anti-Vascular Endothelial Growth Factor Agents and seizures. Symptoms occurred in temporal correlation with intraocular treatment. Clinicians should be aware of this potential side effect on intraocular treatment with Anti-Vascular Endothelial Growth Factor Agents in patients with high risk for seizures.
Jan 2016 DOI 10.14302/issn.2379-7835.ijn-15-880
J Howard SimonCorresponding author
MBBS MSc MFPH, Specialty Registrar in Public Health, Health Education North East, Waterfront 4, Goldcrest Way, Newburn Riverside, Newcastle upon Tyne, NE15 8NY
Objective To provide data on the consistency of recommended serving sizes of single bars and bags of chocolate confectionery products sold in UK supermarkets, in terms of weight and energy content. Methods Data were obtained from supermarket websites on the weight, calorific content and recommended serving size of all products classified as single bars or bags of chocolate confectionery products in at least two of the three supermarkets with the largest share of the grocery market in the United Kingdom. Results The number of servings per product varies from 1 to 3. Recommended serving sizes vary widely in terms of weight and energy content (ranges 18-83.4g and 88-265kcal respectively). Recommended serving sizes vary even between identical products sold in different size packages. Conclusions There is potential for consumer confusion over a reasonable serving size of chocolate products, especially in the wider nutritional context of well-described portion sizes for food categories such as fruit. Alternatively, the inconsistency may derive from a reasonable attempt to make front-of-pack labelling easy for consumers to understand by using intuitive fractions of the contents.