AI Medical Compendium Topic:
Prognosis

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A data-centric machine learning approach to improve prediction of glioma grades using low-imbalance TCGA data.

Scientific reports
Accurate prediction and grading of gliomas play a crucial role in evaluating brain tumor progression, assessing overall prognosis, and treatment planning. In addition to neuroimaging techniques, identifying molecular biomarkers that can guide the dia...

Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury.

BMC medical informatics and decision making
INTRODUCTION: Sepsis-associated acute kidney injury (SA-AKI) is strongly associated with poor prognosis. We aimed to build a machine learning (ML)-based clinical model to predict 1-year mortality in patients with SA-AKI.

A prognostic framework for predicting lung signet ring cell carcinoma via a machine learning based cox proportional hazard model.

Journal of cancer research and clinical oncology
PURPOSE: Signet ring cell carcinoma (SRCC) is a rare type of lung cancer. The conventional survival nomogram used to predict lung cancer performs poorly for SRCC. Therefore, a novel nomogram specifically for studying SRCC is highly required.

Combining Metabolomics and Machine Learning to Identify Diagnostic and Prognostic Biomarkers in Patients with Non-Small Cell Lung Cancer Pre- and Post-Radiation Therapy.

Biomolecules
Lung cancer is the leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) accounting for over 85% of cases and poor prognosis in advanced stages. This study explored shifts in circulating metabolite levels in NSCLC p...

Machine learning-based derivation and validation of three immune phenotypes for risk stratification and prognosis in community-acquired pneumonia: a retrospective cohort study.

Frontiers in immunology
BACKGROUND: The clinical presentation of Community-acquired pneumonia (CAP) in hospitalized patients exhibits heterogeneity. Inflammation and immune responses play significant roles in CAP development. However, research on immunophenotypes in CAP pat...

Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review.

PloS one
BACKGROUND: This systematic review aimed to evaluate the performance of machine learning (ML) models in predicting post-treatment survival and disease progression outcomes, including recurrence and metastasis, in head and neck cancer (HNC) using clin...

Machine learning in the prediction of treatment response in rheumatoid arthritis: A systematic review.

Seminars in arthritis and rheumatism
OBJECTIVE: This study aimed to investigate the current status and performance of machine learning (ML) approaches in providing reproducible treatment response predictions.

Cluster analysis of thoracic muscle mass using artificial intelligence in severe pneumonia.

Scientific reports
Severe pneumonia results in high morbidity and mortality despite advanced treatments. This study investigates thoracic muscle mass from chest CT scans as a biomarker for predicting clinical outcomes in ICU patients with severe pneumonia. Analyzing el...

Computational approaches for clinical, genomic and proteomic markers of response to glucagon-like peptide-1 therapy in type-2 diabetes mellitus: An exploratory analysis with machine learning algorithms.

Diabetes & metabolic syndrome
INTRODUCTION: In 2021, the International Diabetes Federation reported that 537 million people worldwide are living with diabetes. While glucagon-like peptide-1 agonists provide significant benefits in diabetes management, approximately 40% of patient...

A stacking ensemble model for predicting the occurrence of carotid atherosclerosis.

Frontiers in endocrinology
BACKGROUND: Carotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking ensemble machine learning techniques to enhance the prediction of CAS occurrence, incorporatin...