AIMC Topic: Cohort Studies
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XGBoost-SHAP-based interpretable diagnostic framework for knee osteoarthritis: a population-based retrospective cohort study.
Arthritis research & therapy
Dec 19, 2024
OBJECTIVE: To use routine demographic and clinical data to develop an interpretable individual-level machine learning (ML) model to diagnose knee osteoarthritis (KOA) and to identify highly ranked features.
Detection of late gadolinium enhancement in patients with hypertrophic cardiomyopathy using machine learning.
International journal of cardiology
Dec 18, 2024
BACKGROUND: Late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) in hypertrophic cardiomyopathy (HCM) typically represents myocardial fibrosis and may lead to fatal ventricular arrhythmias. However, CMR is resource-intensive and some...
Dual biomarkers CT-based deep learning model incorporating intrathoracic fat for discriminating benign and malignant pulmonary nodules in multi-center cohorts.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Dec 16, 2024
BACKGROUND: Recent studies in the field of lung cancer have emphasized the important role of body composition, particularly fatty tissue, as a prognostic factor. However, there is still a lack of practice in combining fatty tissue to discriminate ben...
Utilizing machine learning approaches to investigate the relationship between cystatin C and serious complications in esophageal cancer patients after esophagectomy.
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Dec 16, 2024
BACKGROUND: The purpose of this study is to investigate the relationship between preoperative cystatin C levels and the risk of serious postoperative complications in esophageal cancer (EC) patients, utilizing advanced machine learning (ML) methodolo...
Does machine learning improve prediction accuracy of the Endoscopic Third Ventriculostomy Success Score? A contemporary Hydrocephalus Clinical Research Network cohort study.
Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Dec 10, 2024
PURPOSE: This Hydrocephalus Clinical Research Network (HCRN) study had two aims: (1) to compare the predictive performance of the original ETV Success Score (ETVSS) using logistic regression modeling with other newer machine learning models and (2) t...
Impact of Inflammation After Cardiac Surgery on 30-Day Mortality and Machine Learning Risk Prediction.
Journal of cardiothoracic and vascular anesthesia
Dec 9, 2024
OBJECTIVES: To investigate the impact of systemic inflammatory response syndrome (SIRS) on 30-day mortality following cardiac surgery and develop a machine learning model to predict SIRS.
Deciphering the climate-malaria nexus: A machine learning approach in rural southeastern Tanzania.
Public health
Dec 6, 2024
OBJECTIVES: Malaria remains a critical public health challenge, especially in regions like southeastern Tanzania. Understanding the intricate relationship between environmental factors and malaria incidence is essential for effective control and elim...
Prediction of mortality in sepsis patients using stacked ensemble machine learning algorithm.
Journal of postgraduate medicine
Dec 6, 2024
INTRODUCTION: Machine learning (ML) has been tried in predicting outcomes following sepsis. This study aims to identify the utility of stacked ensemble algorithm in predicting mortality.
Machine Learning-Based Suicide Risk Prediction Model for Suicidal Trajectory on Social Media Following Suicidal Mentions: Independent Algorithm Validation.
Journal of medical Internet research
Dec 5, 2024
BACKGROUND: Previous efforts to apply machine learning-based natural language processing to longitudinally collected social media data have shown promise in predicting suicide risk.
AI-Based Evaluation of Prostate MR Imaging at a Modern Low-field 0.55 T Scanner Compared to 3 T in a Screening Cohort.
Academic radiology
Dec 2, 2024
PURPOSE: To evaluate the effect of lower field strength on quantitative apparent-diffusion-coefficient (ADC) values, contrast of the T2-weighted MR images and the performance of an AI-based segmentation.
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