AIMC Topic: Adult

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Beyond total scores: Enhancing psychotherapy outcome prediction with item-level scores.

Journal of consulting and clinical psychology
OBJECTIVE: This study aims at improving dropout and treatment nonresponse prevention by optimizing the performance of models for their prediction through the integration of item-level data.

Vedolizumab in inflammatory bowel disease: Real-world outcomes and their prediction with machine learning-the IG-IBD LIVE study.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND AND AIMS: Real-world studies on vedolizumab in inflammatory bowel disease (IBD) are often limited by small sample size and short follow-up. In this study, we investigated the 2-year effectiveness and safety of vedolizumab in patients with ...

Behavioral biases and Fintech adoption: Investigating the role of financial literacy.

Acta psychologica
This paper studies the influence of behavioral biases on Fintech adoption. Additionally, the role of financial literacy in adaptation of Fintech services is evaluated. Primary data from customers in the banking sector is gathered using a structured q...

Phenomenological psychopathology meets machine learning: A multicentric retrospective study (Mu.St.A.R.D.) targeting the role of Aberrant Salience assessment in psychosis detection.

Schizophrenia research
BACKGROUND: The Aberrant Salience (AS) model conceptualizes psychosis onset as the altered attribution of salience to neutral stimuli. The Aberrant Salience Inventory (ASI), a psychometric tool, measures this phenomenon. This study utilized a multi-c...

Machine Learning Model for Predicting Pheochromocytomas/Paragangliomas Surgery Difficulty: A Retrospective Cohort Study.

Annals of surgical oncology
OBJECTIVE: We aimed to develop a machine learning (ML) model to preoperatively predict surgical difficulty for pheochromocytomas and paragangliomas (PPGLs) using clinical and radiomic features.

Evaluating natural language processing derived linguistic features associated with current suicidal ideation, past attempts, and future suicidal behavior.

Journal of psychiatric research
BACKGROUND: People with psychosis have a higher suicide risk than the general population. Natural language processing (NLP) has been used to understand communication in psychosis and suicide risk prediction, but not to predict future suicidal behavio...

Development and preliminary testing of a secure large language model-based chatbot for brief alcohol counseling in young adults.

Drug and alcohol dependence
OBJECTIVE: Young adults face elevated risks from alcohol use yet encounter significant barriers to accessing evidence-based interventions. Large language models (LLMs) represent a promising advancement for delivering personalized behavioral intervent...

An informed machine learning based environmental risk score for hypertension in European adults.

Artificial intelligence in medicine
BACKGROUND: The exposome framework seeks to unravel the cumulated effects of environmental exposures on health. However, existing methods struggle with challenges including multicollinearity, non-linearity and confounding. To address these limitation...

Deep Learning Model for Real-Time Nuchal Translucency Assessment at Prenatal US.

Radiology. Artificial intelligence
Purpose To develop and evaluate an artificial intelligence-based model for real-time nuchal translucency (NT) plane identification and measurement in prenatal US assessments. Materials and Methods In this retrospective multicenter study conducted fro...