AIMC Topic: Prognosis

Clear Filters Showing 3161 to 3170 of 3837 articles

Current State of Fibrotic Interstitial Lung Disease Imaging.

Radiology
Interstitial lung disease (ILD) diagnosis is complex, continuously evolving, and increasingly reliant on thin-section chest CT. Multidisciplinary discussion aided by a thorough radiologic review can achieve a high-confidence diagnosis of ILD in the m...

Predictive Modeling of Heart Failure Outcomes Using ECG Monitoring Indicators and Machine Learning.

Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc
BACKGROUND: Heart failure (HF) is a major driver of global morbidity and mortality. Early identification of patients at risk remains challenging due to complex, multivariate clinical relationships. Machine learning (ML) methods offer promise for more...

Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine-Learning and CT Features.

Clinical transplantation
INTRODUCTION: Primary graft dysfunction (PGD) is a significant barrier to survival in lung transplant (LTx) recipients. PGD in patients with systemic sclerosis (SSc) remains especially underrepresented in research.

Leveraging deep learning to discover interpretable cellular spatial biomarkers for prognostic predictions based on hepatocellular carcinoma histology.

The journal of pathology. Clinical research
The spatial structure of various cell types in the tumour microenvironment (TME) can provide valuable insights into disease progression. However, identifying the spatial organization of diverse cell types that significantly correlates with patient pr...

Predictive modeling of ARDS mortality integrating biomarker/cytokine, clinical and metabolomic data.

Translational research : the journal of laboratory and clinical medicine
Acute Respiratory Distress Syndrome (ARDS), characterized by the rapid onset of respiratory failure and mortality rates of ∼40%, remains a significant challenge in critical care medicine. Despite advances in supportive care, accurate prediction of AR...

A novel metabolomic aging score - better than conventional metrics in predicting short-term mortality.

Expert review of molecular diagnostics
INTRODUCTION: Accurate prediction of short-term mortality is crucial for optimizing clinical prognosis and providing treatment decisions. Conventional metrics, including physiological indicators, laboratory indexes and scoring systems, suffer from li...

Machine learning prediction of pathological complete response to neoadjuvant chemotherapy with peritumoral breast tumor ultrasound radiomics: compare with intratumoral radiomics and clinicopathologic predictors.

Breast cancer research and treatment
PURPOSE: Noninvasive, accurate and novel approaches to predict patients who will achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) could assist treatment strategies. The aim of this study was to explore the application...

Prediction of post stroke depression with machine learning: A national multicenter cohort study.

Journal of psychiatric research
OBJECTIVE: Post-stroke depression (PSD) is a common psychiatric complication following stroke, with low clinical detection rates and delayed diagnosis. Most existing PSD prediction models suffer from incomplete data inclusion, which limits their clin...

Evaluating an information theoretic approach for selecting multimodal data fusion methods.

Journal of biomedical informatics
OBJECTIVE: Interest has grown in combining radiology, pathology, genomic, and clinical data to improve the accuracy of diagnostic and prognostic predictions toward precision health. However, most existing works choose their datasets and modeling appr...

Machine Learning Model for Predicting Pheochromocytomas/Paragangliomas Surgery Difficulty: A Retrospective Cohort Study.

Annals of surgical oncology
OBJECTIVE: We aimed to develop a machine learning (ML) model to preoperatively predict surgical difficulty for pheochromocytomas and paragangliomas (PPGLs) using clinical and radiomic features.