AIMC Topic: Cohort Studies

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Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm.

BMC medical research methodology Sep 27, 2024
BACKGROUND: Imbalanced datasets pose significant challenges in predictive modeling, leading to biased outcomes and reduced model reliability. This study addresses data imbalance in diabetes prediction using machine learning techniques. Utilizing data...
Male Middle Aged Cohort Studies Diabetes Mellitus, Type 2 Humans Female Machine Learning Reproducibility of Results Algorithms Risk Factors Adult
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Interpretable machine learning models for the prediction of all-cause mortality and time to death in hemodialysis patients.

Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy Sep 26, 2024
INTRODUCTION: The elevated mortality and hospitalization rates among hemodialysis (HD) patients underscore the necessity for the development of accurate predictive tools. This study developed two models for predicting all-cause mortality and time to ...
Cause of Death Time Factors Logistic Models Support Vector Machine Kidney Failure, Chronic Aged Humans Male Middle Aged Machine Learning Renal Dialysis Retrospective Studies Cohort Studies Female
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Identifying Potential Factors Associated With Racial Disparities in COVID-19 Outcomes: Retrospective Cohort Study Using Machine Learning on Real-World Data.

JMIR public health and surveillance Sep 26, 2024
BACKGROUND: Racial disparities in COVID-19 incidence and outcomes have been widely reported. Non-Hispanic Black patients endured worse outcomes disproportionately compared with non-Hispanic White patients, but the epidemiological basis for these obse...
Retrospective Studies Female Risk Factors Aged Male Middle Aged Adolescent Young Adult Adult Humans White Cohort Studies Machine Learning Florida Socioeconomic Factors COVID-19 Black or African American Health Status Disparities
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Maternal dietary practices during pregnancy and obesity of neonates: a machine learning approach towards hierarchical and nested relationships in a Tibet Plateau cohort study.

The British journal of nutrition Sep 26, 2024
Studies on obesity and risk factors from a life-course perspective among residents in the Tibet Plateau with recent economic growth and increasing obesity are important and urgently needed. The birth cohort in this area provides a unique opportunity ...
Obesity Fruit Maternal Nutritional Physiological Phenomena Tibet Female Infant, Newborn Risk Factors Young Adult Humans Adult Prevalence Machine Learning Diet Pregnancy Cohort Studies Pediatric Obesity
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Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative.

EBioMedicine Sep 24, 2024
BACKGROUND: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term...
SARS-CoV-2 Cohort Studies United States Post-Acute COVID-19 Syndrome Crowdsourcing Humans Machine Learning COVID-19 Algorithms
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Prediction of preterm birth in multiparous women using logistic regression and machine learning approaches.

Scientific reports Sep 20, 2024
To predict preterm birth (PTB) in multiparous women, comparing machine learning approaches with traditional logistic regression. A population-based cohort study was conducted using data from the Ontario Better Outcomes Registry and Network (BORN). Th...
Risk Factors Adult Cohort Studies ROC Curve Ontario Female Infant, Newborn Premature Birth Parity Logistic Models Neural Networks, Computer Machine Learning Pregnancy Humans
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Effects of Individual Research Practices on fNIRS Signal Quality and Latent Characteristics.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 20, 2024
Functional near-infrared spectroscopy (fNIRS) is an increasingly popular tool for cross-cultural neuroimaging studies. However, the reproducibility and comparability of fNIRS studies is still an open issue in the scientific community. The paucity of ...
Deep Learning Young Adult Humans Functional Neuroimaging Cohort Studies Italy Algorithms Adult Support Vector Machine Data Accuracy Female Male Reproducibility of Results Spectroscopy, Near-Infrared
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Machine learning-based prediction of the risk of moderate-to-severe catheter-related bladder discomfort in general anaesthesia patients: a prospective cohort study.

BMC anesthesiology Sep 19, 2024
BACKGROUND: Catheter-related bladder discomfort (CRBD) commonly occurs in patients who have indwelling urinary catheters while under general anesthesia. And moderate-to-severe CRBD can lead to significant adverse events and negatively impact patient ...
Female Aged Adult Male Middle Aged Humans Machine Learning Prospective Studies Urinary Catheterization Urinary Catheters Cohort Studies Predictive Value of Tests Anesthesia, General Catheters, Indwelling
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Evaluation and analysis of risk factors for adverse events of the fractured vertebra post-percutaneous kyphoplasty: a retrospective cohort study using multiple machine learning models.

Journal of orthopaedic surgery and research Sep 18, 2024
BACKGROUND: Adverse events of the fractured vertebra (AEFV) post-percutaneous kyphoplasty (PKP) can lead to recurrent pain and neurological damage, which considerably affect the prognosis of patients and the quality of life. This study aimed to analy...
Machine Learning Retrospective Studies Female Risk Factors Aged Humans Cohort Studies Spinal Fractures Fractures, Compression Kyphoplasty Osteoporotic Fractures Aged, 80 and over Male Middle Aged Postoperative Complications
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Deep Learning Model for Pathological Grading and Prognostic Assessment of Lung Cancer Using CT Imaging: A Study on NLST and External Validation Cohorts.

Academic radiology Sep 17, 2024
RATIONALE AND OBJECTIVES: To develop and validate a deep learning model for automated pathological grading and prognostic assessment of lung cancer using CT imaging, thereby providing surgeons with a non-invasive tool to guide surgical planning.
Lung Neoplasms Carcinoma, Non-Small-Cell Lung Neoplasm Grading Tomography, X-Ray Computed Female Reproducibility of Results Aged Middle Aged Deep Learning Humans Sensitivity and Specificity Cohort Studies Male Prognosis
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