AIMC Topic: Retrospective Studies
Clear Filters Showing 601 to 610 of 9163 articles
Preoperatively predicting failure to achieve the minimum clinically important difference and the substantial clinical benefit in patient-reported outcome measures for total hip arthroplasty patients using machine learning.
BMC musculoskeletal disorders
Feb 14, 2025
BACKGROUND: Attention to the collection of patient-reported outcomes measures (PROMs) associated with total hip arthroplasty (THA) is growing. The aim of this study was to preoperatively predict failure to achieve the minimal clinically important dif...
Survival machine learning model of T1 colorectal postoperative recurrence after endoscopic resection and surgical operation: a retrospective cohort study.
BMC cancer
Feb 14, 2025
OBJECTIVE: To construct a postoperative recurrence prediction model for patients with T1 colorectal cancer after endoscopic resection and surgical operation via survival machine learning algorithms.
An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body radiation therapy.
Journal of cancer research and clinical oncology
Feb 14, 2025
PURPOSE: Hepatocellular carcinoma (HCC) remains a global health concern, marked by increasing incidence rates and poor outcomes. This study seeks to develop a robust predictive model by integrating radiomics and deep learning features with clinical d...
Deep learning on pre-procedural computed tomography and clinical data predicts outcome following stroke thrombectomy.
Journal of neurointerventional surgery
Feb 14, 2025
BACKGROUND: Deep learning using clinical and imaging data may improve pre-treatment prognostication in ischemic stroke patients undergoing endovascular thrombectomy (EVT).
Prediction of 90 day mortality in elderly patients with acute HF from e-health records using artificial intelligence.
ESC heart failure
Feb 13, 2025
AIMS: Mortality risk after hospitalization for heart failure (HF) is high, especially in the first 90 days. This study aimed to construct a model automatically predicting 90 day post-discharge mortality using electronic health record (EHR) data 48 h ...
Progression-Free Survival Prediction Performance of ChatGPT: Analysis With Real Life Data in Early and Locally Advanced Prostate Cancer.
The Prostate
Feb 13, 2025
OBJECTIVE: To evaluate the progression-free survival (PFS) time in patients with early-stage and locally advanced prostate cancer and to compare the estimates provided by ChatGPT with actual survival data.
Prognostic Implications of Machine Learning Algorithm-Supported Diagnostic Classification of Myocardial Injury Using the Fourth Universal Definition of Myocardial Infarction.
Heart, lung & circulation
Feb 13, 2025
BACKGROUND: With widespread adoption of high-sensitivity troponin assays, more individuals with myocardial injury are now identified, with type 1 myocardial infarction (T1MI) being less common despite having the most well-established evidence base to...
A risk prediction model for venous thromboembolism in hospitalized patients with thoracic trauma: a machine learning, national multicenter retrospective study.
World journal of emergency surgery : WJES
Feb 13, 2025
BACKGROUND: Early treatment and prevention are the keys to reducing the mortality of VTE in patients with thoracic trauma. This study aimed to develop and validate an automatic prediction model based on machine learning for VTE risk screening in pati...
Large Language Models-Supported Thrombectomy Decision-Making in Acute Ischemic Stroke Based on Radiology Reports: Feasibility Qualitative Study.
Journal of medical Internet research
Feb 13, 2025
BACKGROUND: The latest advancement of artificial intelligence (AI) is generative pretrained transformer large language models (LLMs). They have been trained on massive amounts of text, enabling humanlike and semantical responses to text-based inputs ...
Diagnostic of fatty liver using radiomics and deep learning models on non-contrast abdominal CT.
PloS one
Feb 13, 2025
PURPOSE: This study aims to explore the potential of non-contrast abdominal CT radiomics and deep learning models in accurately diagnosing fatty liver.
Stay Ahead of Medical AI
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 31285
- Neural Networks, Computer 29824
- Algorithms 26978
- Deep Learning 26499
- Artificial Intelligence 24426
- Robotics 13330
- Image Processing, Computer-Assisted 9585
- Retrospective Studies 9163
- Magnetic Resonance Imaging 6215
- Reproducibility of Results 5608
- Tomography, X-Ray Computed 4795
- Support Vector Machine 4708
- Computational Biology 4269
- Brain 4059
- Computer Simulation 3892
Recent Journals
- Scientific reports 6215
- Sensors (Basel, Switzerland) 4794
- PloS one 4077
- Neural networks : the official journal of the International Neural Network Society 2930
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1868
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1144
- Journal of chemical information and modeling 1019
- Nature communications 971
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 863
- Briefings in bioinformatics 853
- IEEE transactions on neural networks and learning systems 831