AIMC Topic: Biomarkers

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Statistical and Machine Learning Methods for Discovering Prognostic Biomarkers for Survival Outcomes.

Methods in molecular biology (Clifton, N.J.)
Discovering molecular biomarkers for predicting patient survival outcomes is an essential step toward improving prognosis and therapeutic decision-making in the treatment of severe diseases such as cancer. Due to the high-dimensionality nature of omi...

AIM-CICs: an automatic identification method for cell-in-cell structures based on convolutional neural network.

Journal of molecular cell biology
Edited by Luonan Chen Whereas biochemical markers are available for most types of cell death, current studies on non-autonomous cell death by entosis rely strictly on the identification of cell-in-cell structures (CICs), a unique morphological readou...

Clinical significance of hepatic function in Graves disease with type 2 diabetic mellitus: A single-center retrospective cross-sectional study in Taiwan.

Medicine
Graves disease (GD) and type 2 diabetes mellitus (T2DM) both impair liver function; we therefore explored the possibility of a relationship among diabetic control, thyroid function, and liver function. This retrospective, cross-sectional study compar...

Care Models for Acute Chest Pain That Improve Outcomes and Efficiency: JACC State-of-the-Art Review.

Journal of the American College of Cardiology
Existing assessment pathways for acute chest pain are often resource-intensive, prolonged, and expensive. In this review, the authors describe existing chest pain pathways and current issues at the patient and system level, and provide an overview of...

Robust Random Forest-Based All-Relevant Feature Ranks for Trustworthy AI.

Studies in health technology and informatics
Feature selection is a fundamental challenge in machine learning. For instance in bioinformatics, it is essential when one wishes to detect biomarkers. Tree-based methods are predominantly used for this purpose. In this paper, we study the stability ...

Characterization of Type 2 Diabetes Using Counterfactuals and Explainable AI.

Studies in health technology and informatics
Type 2 diabetes mellitus is a metabolic disorder of glucose management, whose prevalence is increasing inexorably worldwide. Adherence to therapies, along with a healthy lifestyle can help prevent the onset of disease. This preliminary study proposes...

SAINTENS: Self-Attention and Intersample Attention Transformer for Digital Biomarker Development Using Tabular Healthcare Real World Data.

Studies in health technology and informatics
BACKGROUND: Deep learning currently struggles with tabular data, but it can benefit from multimodal learning. SAINT is a deep learning model for tabular data on which we base our presented developments.

Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes.

American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperpr...