Logistic regression and artificial neural networks are the models of choice in many medical data classification tasks. In this review, we summarize the differences and similarities of these models from a technical point of view, and compare them with other machine learning algorithms. We provide considerations useful for critically assessing the quality of the models and the results based on these models. Finally, we summarize our findings on how quality criteria for logistic regression and artificial neural network models are met in a sample of papers from the medical literature.
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Leveraging eCAP thresholds in a logistic model to predict speech outcomes after implantation in anomalous cochlea.
Acta Otolaryngol
January 2025
Neuro-Otology, Department of Neurosurgery, SGPGIMS, Lucknow, Uttar Pradesh, India.
Background: Pediatric cochlear implant (CI) recipients with cochlear malformations face challenges due to variable speech recognition outcomes.
Aims/objectives: This study assesses the predictive value of intraoperative electrically evoked compound action potential (eCAP) thresholds, residual hearing, age at implantation, Intelligent Quotient (IQ), and malformation type for speech recognition outcomes.
Material And Methods: A prospective cohort of 52 children (aged 1-4 years) with cochlear malformations who underwent CI between 2016 and 2024 was analyzed.
Body Composition at Diagnosis and Early Response in Pediatric Hodgkin Lymphoma.
Cancer Epidemiol Biomarkers Prev
January 2025
University of Alabama at Birmingham, Birmingham, AL, United States.
Background: The association between skeletal muscle and adipose tissue (body composition) and early response using positron emission tomography (PET) in pediatric Hodgkin lymphoma (HL) remains unstudied.
Methods: Patients enrolled on Children's Oncology Group studies AHOD0031 (intermediate-risk HL) and AHOD0831 (high-risk HL) with digital abdominal computed tomography (CT) scans at diagnosis and PET scans after 2 cycles (PET2) were included. Two consecutive slices at the third lumbar vertebra were identified and skeletal muscle index (SMI, in cm2/m2) and total adipose tissue index (TATI, in cm2/m2) were calculated using sliceOmatic (Magog, Canada) and height at diagnosis.
Enhancing Small-for-Gestational-Age Prediction: Multi-Country Validation of Nuchal Thickness, Estimated Fetal Weight, and Machine Learning Models.
Prenat Diagn
January 2025
Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
Objective: The first objective is to develop a nuchal thickness reference chart. The second objective is to compare rule-based algorithms and machine learning models in predicting small-for-gestational-age infants.
Method: This retrospective study involved singleton pregnancies at University Malaya Medical Centre, Malaysia, developed a nuchal thickness chart and evaluated its predictive value for small-for-gestational-age using Malaysian and Singapore cohorts.
Prevalence of posttraumatic stress disorder (PTSD) in Canada during the COVID-19 pandemic: results from the Survey on COVID-19 and Mental Health.
Health Promot Chronic Dis Prev Can
January 2025
Department of Psychology, University of Regina, Regina, Saskatchewan, Canada.
Introduction: This study provides a descriptive overview of the prevalence of posttraumatic stress disorder (PTSD) in Canada, across sociodemographic characteristics, mental health-related variables and negative impacts of the COVID-19 pandemic.
Methods: Data were obtained from cycles 1 and 2 of the Survey on COVID-19 and Mental Health (SCMH), collected in fall 2020 (N = 14 689) and spring 2021 (N = 8032). The prevalence of PTSD was measured using the PTSD Checklist for DSM-5 (PCL-5) Cross-sectional associations were quantified using logistic regression, while controlling for sociodemographic characteristics.
Association and mechanism of montelukast on sleep disorders: insights from NHANES 2005-2018 data analysis and a network pharmacology study.
J Asthma
January 2025
School of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China.
Background: Studies have suggested associations between montelukast and increased risks of sleep disorders, including overall sleeping problems and insomnia. However, the results of observational studies are not consistent. Understanding these associations is crucial, particularly in patients solely diagnosed with allergic rhinitis, where montelukast use remains prevalent.
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