AIMC Topic: Humans

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Machine-Learning Model Identifies Patients With Alpha-1 Antitrypsin Deficiency Using Claims Records.

COPD
Identifying patients with rare diseases like alpha-1 antitrypsin deficiency (AATD) is challenging. Machine-learning models may be trained to identify patients with rare diseases using large-scale, real-world databases, whereas electronic medical reco...

Referring Image Segmentation with Multi-Modal Feature Interaction and Alignment Based on Convolutional Nonlinear Spiking Neural Membrane Systems.

International journal of neural systems
Referring image segmentation aims to accurately align image pixels and text features for object segmentation based on natural language descriptions. This paper proposes NSNPRIS (convolutional nonlinear spiking neural P systems for referring image seg...

Influence of panic disorder and paroxetine on brain functional hubs in drug-free patients.

Journal of psychopharmacology (Oxford, England)
BACKGROUND: The effects of panic disorder (PD) and pharmacotherapy on brain functional hubs in drug-free patients, and the utility of their degree centrality (DC) in diagnosing and predicting treatment response (TR) for PD, remained unclear.

Identification of four novel acute-on-chronic liver failure clusters with distinct clinical trajectories and mortality using machine learning methods.

Alimentary pharmacology & therapeutics
BACKGROUND AND AIMS: Machine learning (ML) can identify the hidden patterns without hypothesis in heterogeneous diseases like acute-on-chronic live failure (ACLF). We employed ML to describe and predict yet unknown clusters in ACLF.

Deep evidential learning for radiotherapy dose prediction.

Computers in biology and medicine
BACKGROUND: As we navigate towards integrating deep learning methods in the real clinic, a safety concern lies in whether and how the model can express its own uncertainty when making predictions. In this work, we present a novel application of an un...

A flexible 2.5D medical image segmentation approach with in-slice and cross-slice attention.

Computers in biology and medicine
Deep learning has become the de facto method for medical image segmentation, with 3D segmentation models excelling in capturing complex 3D structures and 2D models offering high computational efficiency. However, segmenting 2.5D images, characterized...

Interpretable machine learning model predicting immune checkpoint inhibitor-induced hypothyroidism: A retrospective cohort study.

Cancer science
Hypothyroidism is a known adverse event associated with the use of immune checkpoint inhibitors (ICIs) in cancer treatment. This study aimed to develop an interpretable machine learning (ML) model for individualized prediction of hypothyroidism in pa...

Effective genome editing with an enhanced ISDra2 TnpB system and deep learning-predicted ωRNAs.

Nature methods
Transposon (IS200/IS605)-encoded TnpB proteins are predecessors of class 2 type V CRISPR effectors and have emerged as one of the most compact genome editors identified thus far. Here, we optimized the design of Deinococcus radiodurans (ISDra2) TnpB ...