AIMC Topic: Retrospective Studies
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Cost-Effectiveness Analysis of a Machine Learning-Based eHealth System to Predict and Reduce Emergency Department Visits and Unscheduled Hospitalizations of Older People Living at Home: Retrospective Study.
JMIR formative research
Apr 11, 2025
BACKGROUND: Dependent older people or those losing their autonomy are at risk of emergency hospitalization. Digital systems that monitor health remotely could be useful in reducing these visits by detecting worsening health conditions earlier. Howeve...
Machine-Learning-Based Computed Tomography Radiomics Regression Model for Predicting Pulmonary Function.
Academic radiology
Apr 10, 2025
RATIONALE AND OBJECTIVES: Chest computed tomography (CT) radiomics can be utilized for categorical predictions; however, models predicting pulmonary function indices directly are lacking. This study aimed to develop machine-learning-based regression ...
Interpretable Machine Learning Radiomics Model Predicts 5-year Recurrence-Free Survival in Non-metastatic Clear Cell Renal Cell Carcinoma: A Multicenter and Retrospective Cohort Study.
Academic radiology
Apr 10, 2025
RATIONALE AND OBJECTIVES: To develop and validate a computed tomography (CT) radiomics-based interpretable machine learning (ML) model for predicting 5-year recurrence-free survival (RFS) in non-metastatic clear cell renal cell carcinoma (ccRCC).
Prediction of Clavien Dindo Classification ≥ Grade III Complications After Epithelial Ovarian Cancer Surgery Using Machine Learning Methods.
Medicina (Kaunas, Lithuania)
Apr 10, 2025
Ovarian cancer surgery requires multiple radical resections with a high risk of complications. The aim of this single-centre, retrospective study was to determine the best method for predicting Clavien-Dindo grade ≥ III complications using machine l...
Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy.
BMC medical informatics and decision making
Apr 10, 2025
BACKGROUND: The flexible ureteroscopy lithotripsy (F-URL) is an important treatment for upper urinary tract stones. However, urolithiasis, surgical procedures, and catheter placement are risk factors for fungal infections. Our study aimed to construc...
Prediction of retinopathy of prematurity development and treatment need with machine learning models.
BMC ophthalmology
Apr 10, 2025
BACKGROUND: To evaluate the effectiveness of machine learning (ML) models in predicting the occurrence of retinopathy of prematurity (ROP) and treatment need.
Preoperative prediction of pulmonary ground-glass nodule infiltration status by CT-based radiomics combined with neural networks.
BMC cancer
Apr 10, 2025
OBJECTIVE: The infiltration status of pulmonary ground-glass nodules (GGNs) exhibits significant variability, demanding tailored surgical strategies and individualized postoperative adjuvant therapies. This study explored the preoperative assessment ...
Identification of Patients With Congestive Heart Failure From the Electronic Health Records of Two Hospitals: Retrospective Study.
JMIR medical informatics
Apr 10, 2025
BACKGROUND: Congestive heart failure (CHF) is a common cause of hospital admissions. Medical records contain valuable information about CHF, but manual chart review is time-consuming. Claims databases (using International Classification of Diseases [...
Habitat Radiomics Based on MRI for Predicting Metachronous Liver Metastasis in Locally Advanced Rectal Cancer: a Two‑center Study.
Academic radiology
Apr 9, 2025
RATIONALE AND OBJECTIVES: This study aimed to explore the feasibility of using habitat radiomics based on magnetic resonance imaging (MRI) to predict metachronous liver metastasis (MLM) in locally advanced rectal cancer (LARC) patients. A nomogram wa...
Preoperative assessment in lymph node metastasis of pancreatic ductal adenocarcinoma: a transformer model based on dual-energy CT.
World journal of surgical oncology
Apr 9, 2025
BACKGROUND: Deep learning(DL) models can improve significantly discrimination of lymph node metastasis(LNM) of pancreatic ductal adenocarcinoma(PDAC), but have not been systematically assessed.
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