AIMC Topic: Time Factors

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Temporal shifts in prognostic factors for 90- and 180-day outcomes after stroke thrombolysis: A machine learning analysis.

PloS one
INTRODUCTION: Prognostication at 90 and 180 days after thrombolysis for acute ischemic stroke (AIS) is critical, yet the temporal evolution of key predictors remains inadequately understood. The utility of machine learning for systematically comparin...

Foundation model based prediction of lung cancer survival using temporal changes in dual time point CT scans.

Scientific reports
Lung cancer remains a significant cause of mortality, with non-small cell lung cancer (NSCLC) representing most cases. Currently, clinical data based models fall short in predicting survival while more advanced deep learning based image models requir...

Unsupervised Characterization of Temporal Dataset Shifts as an Early Indicator of AI Performance Variations: Evaluation Study Using the Medical Information Mart for Intensive Care-IV Dataset.

JMIR medical informatics
BACKGROUND: Reusing long-term data from electronic health records is essential for training reliable and effective health artificial intelligence (AI). However, intrinsic changes in health data distributions over time-known as dataset shifts, which i...

Mathematical modelling of injection time in autoinjectors: State of the art and future perspectives.

International journal of pharmaceutics
Autoinjector devices are a specialized type of medical equipment used for the self-administration of medications. Over the past decade, the significance in these devices has increased, both in terms of their usage and academic interest. An important ...

Exploring Age-Related Patterns in Smartphone Keystroke Dynamics Considering Temporal Variability: Cross-Sectional Study With AI-Based Analysis.

JMIR mHealth and uHealth
BACKGROUND: Keystroke dynamics on smartphones have emerged as a promising form of passive digital biomarker. While previous studies have explored their utility in several diseases and disorders, relatively few have examined how these dynamics change ...

Risk Prediction of Major Adverse Cardiovascular Events Within One Year After Percutaneous Coronary Intervention in Patients With Acute Coronary Syndrome: Machine Learning-Based Time-to-Event Analysis.

JMIR medical informatics
BACKGROUND: Patients with acute coronary syndrome (ACS) who undergo percutaneous coronary intervention (PCI) remain at high risk for major adverse cardiovascular events (MACE). Conventional risk scores may not capture dynamic or nonlinear changes in ...

Rapid and Accurate Protein Structure Database Search Using Inverse Folding Model and Contrastive Learning.

Journal of chemical information and modeling
Protein structure database search has become increasingly challenging due to the growing number of experimental and computational structures. We introduce mTM-align2, a novel two-step approach for rapid and accurate protein structure database search....

Cortical surface electric field estimation for real-time TMS with graph neural networks.

Physics in medicine and biology
Transcranial magnetic stimulation is a non-invasive neurostimulation and neuromodulation technique that induces electric fields (-fields) in the brain via a coil placed over the scalp. Our objective is to develop a real-time estimation method for the...