AIMC Topic: Case-Control Studies

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A Transformer-Based Framework for Counterfactual Estimation of Antihypertensive Treatment Effect on COVID-19 Infection Risk - A Proof-of-Concept Study.

American journal of hypertension
BACKGROUND: Transformer-based neural networks excel in modelling high-dimensional, time-series data with complex dependencies. This proof-of-concept study applies a transformer-X-learner framework to estimate treatment effects using real-world data, ...

MACHINE LEARNING AND SHOCK INDICES-DERIVED SCORE FOR PREDICTING CONTRAST-INDUCED NEPHROPATHY IN ACUTE CORONARY SYNDROME PATIENTS.

Shock (Augusta, Ga.)
Background: Contrast-induced nephropathy (CIN) is a serious complication following acute coronary syndrome (ACS), leading to increased morbidity and mortality. Machine learning (ML), combined with parameters such as shock indices, can potentially imp...

Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder.

Journal of affective disorders
BACKGROUND: Early diagnosis and treatment of mental illnesses is hampered by the lack of reliable markers. This study used machine learning models to uncover diagnostic and risk prediction markers for eating disorders (EDs), major depressive disorder...

Segmentation with artificial intelligence and automated calculation of the corpus callosum index in multiple sclerosis.

Saudi medical journal
OBJECTIVES: To determine the corpus callosum index (CCI) differences between chronic phase multiple sclerosis (MS) patients and healthy individuals and to evaluate the corpus callosum segmentation in MS patients using artificial intelligence technolo...

Altered cerebral functional activity and its associated genetic profiles underlying chronic insomnia disorder before and after treatment.

The world journal of biological psychiatry : the official journal of the World Federation of Societies of Biological Psychiatry
OBJECTIVES: The resting-state cerebral functional activity underlying chronic insomnia disorder (CID) remains inconsistent, and the effects of pharmacotherapy on such activity are unclear.

Predicting Placenta Accreta Spectrum Disorder Through Machine Learning Using Metabolomic and Lipidomic Profiling and Clinical Characteristics.

Obstetrics and gynecology
OBJECTIVE: To perform metabolomic and lipidomic profiling with plasma samples from patients with placenta accreta spectrum (PAS) to identify possible biomarkers for PAS and to predict PAS with machine learning methods that incorporated clinical chara...

Upregulation of immune genes in the proliferative phase endometrium enables classification into women with recurrent pregnancy loss versus controls.

Human reproduction (Oxford, England)
STUDY QUESTION: Does the transcriptome of preconceptional endometrium in the proliferative phase show a specific profile in women with recurrent pregnancy loss (RPL)?