AIMC Topic: Recurrence

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Pre-operatively predicting kidney stone recurrence: integrating radiomic features and clinical variables using machine learning.

BMC medical imaging
BACKGROUND: Radiomics and artificial intelligence have shown strong predictive capabilities in urinary stone research, particularly concerning stone composition, characteristics, and treatment outcomes. However, the association of stone radiomics and...

Development and validation of a multidimensional and interpretable artificial intelligence model to predict gout recurrence in hospitalised patients: a real-world, ambispective multicentre cohort study in China.

BMC medicine
BACKGROUND: Gout is the most common inflammatory arthritis. Recurrent flares are common among hospitalised patients and contribute to substantial clinical and economic burden. However, the accurate prediction of inpatient recurrence remains challengi...

A predictive model for evaluating the risk of latent tuberculosis relapse via machine learning.

BMC infectious diseases
BACKGROUND: Reactivation of latent tuberculosis infection (LTBI) is a major obstacle to tuberculosis eradication. Predicting LTBI relapse is crucial for effective disease management but remains underexplored.

Requirements and Concerns of Remitted Individuals With Depression for an Early Relapse Detection mHealth App: Focus Group Study.

JMIR mHealth and uHealth
BACKGROUND: Major depressive disorder is often a recurrent condition, with a high risk of relapse for individuals remitted from depression. Early detection of relapse is critical to improve clinical outcomes. Mobile health (mHealth) technologies offe...

Re-examining the association between region-specific pain recurrence and muscle force strategies in patients with patellofemoral pain via OpenSim and artificial intelligence: a prospective cohort study toward targeted rehabilitation.

Journal of neuroengineering and rehabilitation
BACKGROUND: This study utilized artificial intelligence (AI)-based machine learning algorithms, alongside the shapley additive explanations (SHAP) framework, to identify lower-limb muscle force patterns associated with recurrent patellofemoral pain (...

Multiple Sclerosis Relapse Treatment During Pregnancy and Offspring Functional and Structural Neurodevelopment: A Cross-Sectional Study.

Neurology
BACKGROUND AND OBJECTIVES: High-dose methylprednisolone (MP) is the global standard for treating pregnancy-associated relapses in multiple sclerosis (MS). Given that glucocorticoids cross the placenta and may interfere with fetal brain development, c...

Integrating multiple feature assessment methods to identify key predictors of repeat suicide attempts in Taiwan.

BMC psychiatry
BACKGROUND: The high rate of repeat attempts among individuals who have previously attempted suicide presents a critical challenge in public health and suicide prevention. While early and targeted intervention is crucial for this high-risk group, eff...

Altered effective connectivity in patients with drug-naïve first-episode, recurrent, and medicated major depressive disorder: A multi-site fMRI study.

Behavioural brain research
BACKGROUND: Major depressive disorder (MDD) has been diagnosed through subjective and inconsistent clinical assessments. Resting-state functional magnetic resonance imaging (rs-fMRI) with connectivity analysis has been valuable for identifying neural...

Combining mucosal microbiome and host multi-omics data shows prognostic potential in paediatric ulcerative colitis.

Nature communications
Current first-line treatments of paediatric ulcerative colitis (UC) maintain a 6-month remission in only half of the patients. Relapse prediction at diagnosis could enable earlier introduction of immunosuppressants. We collected intestinal biopsies f...

Early warning signals of bipolar relapse: Investigating critical slowing down in smartphone data.

Journal of affective disorders
BACKGROUND: Early warning signals (EWS) based on dynamical systems theory, such as increased autocorrelation (AR) and variance, may indicate impending mood episodes in bipolar disorder (BD). This study examines whether smartphone-based digital phenot...