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Global Medical and Health Communication
Published by Universitas Islam Bandung
ISSN : 23019123     EISSN : 24605441     DOI : https://doi.org/10.29313/gmhc
Core Subject : Health, Science,
Dentistry Health Professions Immunology & microbiology Nursing Public Health
Global Medical and Health Communication is a journal that publishes research articles on medical and health published every 4 (four) months (April, August, and December). Articles are original research that needs to be disseminated and written in English. Subjects suitable for publication include but are not limited to the following fields of anesthesiology and intensive care, biochemistry, biomolecular, cardiovascular, child health, dentistry, dermatology and venerology, endocrinology, environmental health, epidemiology, geriatric, hematology, histology, histopathology, immunology, internal medicine, nursing sciences, midwifery, nutrition, nutrition and metabolism, obstetrics and gynecology, occupational health, oncology, ophthalmology, oral biology, orthopedics and traumatology, otorhinolaryngology, pharmacology, pharmacy, preventive medicine, public health, pulmonology, radiology, and reproductive health.
Arjuna Subject : Kedokteran - Epidemilogi
Articles 12 Documents
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Search results for , issue "Vol 11, No 3 (2023)" : 12 Documents clear
Effectiveness of Machine Learning for COVID-19 Patient Mortality Prediction Using WEKA Khuluq, Husnul; Yusuf, Prasandhya Astagiri; Perwitasari, Dyah Aryani
Global Medical & Health Communication (GMHC) Vol 11, No 3 (2023)
Publisher : Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/gmhc.v11i3.12119

Abstract

Timely detection of patients with a high mortality risk in coronavirus disease 2019 (COVID-19) can substantially improve triage, bed allocation, time reduction, and potential outcomes. A potential solution is using machine learning (ML) algorithms to predict mortality in COVID-19 hospitalized patients. The study's objective was to create and verify individual risk assessments for mortality using anonymous demographic, clinical, and laboratory findings at admission, as well as to assess the possibility of death using machine learning. We used a standardized format and electronic medical records. Data from 2,313 patients were collected from two Muhammadiyah hospitals from January 2020 to July 2022. Utilizing each patient's clinical manifestation state at admission and laboratory parameters, 24 demographic, clinical, and laboratory results were studied. The algorithms analyzed were AdaBoost, logistic regression, random forest, support vector machine, naïve Bayes, and decision tree, which were applied through WEKA version 3.8.6. Random forest performed better than the other machine learning techniques, with precision, sensitivity, receiver operating characteristic (ROC), and accuracy of 78.6%, 78.7%, 85%, and 78.65%, respectively. The three top predictors were septic shock (OR=21.518, 95% CI=4.933–93.853), respiratory failure (OR=15.503, 95% CI=8.507–28.254), and D-dimer (OR=3.288, 95% CI=2.510–4.306). Machine learning–based predictive models, especially the random forest algorithm, may make it easier to identify patients at high risk of death and guide physicians' appropriate interventions.
Difference between Nutrition Status in First and Recurrent Ischemic Stroke Patients: a Retrospective Cross-Sectional Study Amalia, Lisda; Khairunnisa, Shafa Ayu
Global Medical & Health Communication (GMHC) Vol 11, No 3 (2023)
Publisher : Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/gmhc.v11i3.11051

Abstract

Malnutrition in stroke patients can be caused by neurological deficits such as decreased consciousness, dysphagia, cranial nerve paresis, and hemiparesis/hemiplegia. The condition of malnutrition seriously impacts healing and can exacerbate the underlying disease, in this case, stroke, so malnutrition in stroke patients extends the length of stay and increases morbidity and mortality. This study compares nutritional status between first and recurrent ischemic stroke patients based on body mass index (BMI) and subjective global assessment (SGA). This study is a comparative analysis of the medical records of ischemic stroke patients in Dr. Hasan Sadikin General Hospital Bandung from January 2018 until December 2020. The chi-square and Fisher's exact tests were used for statistical analysis. The significance criteria are the p-value if p≤0.05 means statistically significant. A total of 236 subjects in both groups of first and recurrent ischemic stroke patients consisting of 130 men and 106 women with an average age of 56.64 and 61.75 years, and the majority had risk factors for hypertension. The first ischemic stroke group has a good nutrition status compared with the recurrent stroke group (p<0.05). Thirteen patients (11.02%) of first ischemic stroke and 11 patients of recurrent ischemic stroke (9.32%) were underweight, 67 patients (56.78%) of first ischemic stroke and 74 patients of recurrent ischemic stroke (62.71%) had average weight, 31 patients (26.27%) first ischemic stroke and 33 patients (27.97%) recurrent ischemic stroke were overweight, five patients (4.24%) first ischemic stroke and seven patients (5.93%) recurrent ischemic stroke were obese (p<0.05). In conclusion, there was a significant difference in the nutritional status of first and repeated ischemic stroke patients. The nutritional status of recurrent ischemic stroke patients is worse than that of first ischemic stroke patients.

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