AIMC Topic: Treatment Outcome
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Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning.
BMC infectious diseases
Feb 17, 2025
BACKGROUND: Tuberculosis (TB) continues to pose a significant threat to global public health. Enhancing patient prognosis is essential for alleviating the disease burden.
Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review.
International journal of medical informatics
Feb 16, 2025
BACKGROUND: Transcatheter aortic valve implantation (TAVI) therapy has demonstrated its clear benefits such as low invasiveness, to treat aortic stenosis. Despite associated benefits, still post-procedural complications might occur. The severity of t...
Advanced prognostic modeling with deep learning: assessing long-term outcomes in liver transplant recipients from deceased and living donors.
Journal of translational medicine
Feb 16, 2025
BACKGROUND: Predicting long-term outcomes in liver transplantation remain a challenging endeavor. This research aims to harness the power of deep learning to develop an advanced prognostic model for assessing long-term outcomes, with a specific focus...
Stress hyperglycemia ratio and machine learning model for prediction of all-cause mortality in patients undergoing cardiac surgery.
Cardiovascular diabetology
Feb 15, 2025
BACKGROUND: The stress hyperglycemia ratio (SHR) was developed to reduce the effects of long-term chronic glycemic factors on stress hyperglycemia levels, which was associated with adverse clinical outcomes. This study aims to evaluate the relationsh...
Female
Risk Factors
Aged
Male
Middle Aged
Risk Assessment
Blood Glucose
Time Factors
Decision Support Techniques
Databases, Factual
Biomarkers
Retrospective Studies
Humans
Machine Learning
Cause of Death
Hospital Mortality
Cardiac Surgical Procedures
Hyperglycemia
Stress, Physiological
Treatment Outcome
Predictive Value of Tests
CT-based detection of clinically significant portal hypertension predicts post-hepatectomy outcomes in hepatocellular carcinoma.
European radiology
Feb 14, 2025
BACKGROUND: While the CT-based method of detecting clinically significant portal hypertension (CSPH) emerged as a noninvasive alternative for evaluating CSPH, its predictive ability for post-hepatectomy outcomes is unknown. Therefore, this study aime...
Artificial intelligence for individualized treatment of persistent atrial fibrillation: a randomized controlled trial.
Nature medicine
Feb 14, 2025
Although pulmonary vein isolation (PVI) has become the cornerstone ablation procedure for atrial fibrillation (AF), the optimal ablation procedure for persistent and long-standing persistent AF remains elusive. Targeting spatio-temporal electrogram d...
A deep-learning model for predicting tyrosine kinase inhibitor response from histology in gastrointestinal stromal tumor.
The Journal of pathology
Feb 14, 2025
Over 90% of gastrointestinal stromal tumors (GISTs) harbor mutations in KIT or PDGFRA that can predict response to tyrosine kinase inhibitor (TKI) therapies, as recommended by NCCN (National Comprehensive Cancer Network) guidelines. However, gene seq...
Tyrosine Kinase Inhibitors
Receptor, Platelet-Derived Growth Factor alpha
Protein Kinase Inhibitors
Pyrroles
Triazines
Proto-Oncogene Proteins c-kit
Middle Aged
Deep Learning
Treatment Outcome
Mutation
Gastrointestinal Neoplasms
Gastrointestinal Stromal Tumors
Imatinib Mesylate
Pyrazoles
Humans
Female
Aged
Male
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 Treatment Outcome in Bipolar Disorder: When Can We Expect Clinical Relevance?
Biological psychiatry
Feb 13, 2025
Long-term pharmacological treatment is the cornerstone of the management of bipolar disorder (BD). Clinicians typically select mood-stabilizing medications from among several options through trial and error. This process could be optimized by using r...
Machine-learning approaches to predict individualized treatment effect using a randomized controlled trial.
European journal of epidemiology
Feb 13, 2025
Recent advancements in machine learning (ML) for analyzing heterogeneous treatment effects (HTE) are gaining prominence within the medical and epidemiological communities, offering potential breakthroughs in the realm of precision medicine by enablin...
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