AIMC Topic: Prognosis

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Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study.

The Lancet. Digital health
BACKGROUND: Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to progno...

Contrast-Enhanced CT-Based Deep Learning Radiomics Nomogram for the Survival Prediction in Gallbladder Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: An accurate prognostic model is essential for the development of treatment strategies for gallbladder cancer (GBC). This study proposes an integrated model using clinical features, radiomics, and deep learning based on contr...

Deep learning-enabled breast cancer endocrine response determination from H&E staining based on ESR1 signaling activity.

Scientific reports
Estrogen receptor (ER) positivity by immunohistochemistry has long been a main selection criterium for breast cancer patients to be treated with endocrine therapy. However, ER positivity might not directly correlate with activated ER signaling activi...

Prognosis Forecast of Re-Irradiation for Recurrent Nasopharyngeal Carcinoma Based on Deep Learning Multi-Modal Information Fusion.

IEEE journal of biomedical and health informatics
Radiation therapy is the primary treatment for recurrent nasopharyngeal carcinoma. However, it may induce necrosis of the nasopharynx, leading to severe complications such as bleeding and headache. Therefore, forecasting necrosis of the nasopharynx a...

Establishment of Biliary Atresia Prognostic Classification System via Survival-Based Forward Clustering - A New Biliary Atresia Classification.

Indian journal of pediatrics
OBJECTIVES: To develop a machine learning algorithm with prognosis data to identify different clinical phenotypes of biliary atresia (BA) and provide instructions for choosing treatment schemes.

Deviation-support based fuzzy ensemble of multi-modal deep learning classifiers for breast cancer prognosis prediction.

Scientific reports
Breast cancer is the fifth leading cause of death in females worldwide. Early detection and treatment are crucial for improving health outcomes and preventing more serious conditions. Analyzing diverse information from multiple sources without errors...

SurvIAE: Survival prediction with Interpretable Autoencoders from Diffuse Large B-Cells Lymphoma gene expression data.

Computer methods and programs in biomedicine
BACKGROUND: In Diffuse Large B-Cell Lymphoma (DLBCL), several methodologies are emerging to derive novel biomarkers to be incorporated in the risk assessment. We realized a pipeline that relies on autoencoders (AE) and Explainable Artificial Intellig...

Deep learning quantification reveals a fundamental prognostic role for ductular reaction in biliary atresia.

Hepatology communications
BACKGROUND: We aimed to quantify ductular reaction (DR) in biliary atresia using a neural network in relation to underlying pathophysiology and prognosis.

A deep learning model predicts the presence of diverse cancer types using circulating tumor cells.

Scientific reports
Circulating tumor cells (CTCs) are cancer cells that detach from the primary tumor and intravasate into the bloodstream. Thus, non-invasive liquid biopsies are being used to analyze CTC-expressed genes to identify potential cancer biomarkers. In this...

A Systematic Approach Focused on Machine Learning Models for Exploring the Landscape of Physiological Measurement and Estimation Using Photoplethysmography (PPG).

Journal of cardiovascular translational research
A non-invasive optical technique known as photoplethysmography (PPG) can be used to provide various physiological measurements and estimations. PPG can be used to assess cardiovascular disease (CVD). Hypertension is a primary risk factor for CVD and ...