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Self-training EEG discrimination model with weakly supervised sample construction: An age-based perspective on ASD evaluation.

Neural networks : the official journal of the International Neural Network Society
Deep learning for Electroencephalography (EEG) has become dominant in the tasks of discrimination and evaluation of brain disorders. However, despite its significant successes, this approach has long been facing challenges due to the limited availabi...

Identifying smart technology and artificial intelligence solutions for human factors and ergonomic challenges in all-hazard response: A survey study.

Applied ergonomics
Emergency responders face significant human factors and ergonomic (HF/E) challenges related to physical, cognitive, emotional, and training demands during high-stress situations. This study investigates these issues through a survey of 60 emergency r...

The Chest X- Ray: The Ship has Sailed, But Has It?

Journal of insurance medicine (New York, N.Y.)
In the past, the chest X-ray (CXR) was a traditional age and amount requirement used to assess potential mortality risk in life insurance applicants. It fell out of favor due to inconvenience to the applicant, cost, and lack of protective value. With...

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...

EVALUATION OF PROGNOSTIC RISK MODELS BASED ON AGE AND COMORBIDITY IN SEPTIC PATIENTS: INSIGHTS FROM MACHINE LEARNING AND TRADITIONAL METHODS IN A LARGE-SCALE, MULTICENTER, RETROSPECTIVE STUDY.

Shock (Augusta, Ga.)
Background: Age and comorbidity significantly impact the prognosis of septic patients and inform treatment decisions. To provide clinicians with effective tools for identifying high-risk patients, this study assesses the predictive value of the age-a...

CHRONIC CRITICAL ILLNESS IN BONE TRAUMA PATIENTS: AN AI-BASED APPROACH FOR INTENSIVE CARE UNIT HEALTHCARE PROVIDERS.

Shock (Augusta, Ga.)
Background: Chronic critical illness (CCI) is a serious condition characterized by a prolonged course of illness, resulting in elevated morbidity and mortality. CCI presents significant challenges for healthcare providers in intensive care units (ICU...

Hugan Tiaoshen Formula Improves the Comorbid Mechanism of Schizophrenia and Sleep Disorder via Multitarget Interaction Network.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
This study aims to integrate cross-disease omics data and perform multidimensional analysis to uncover the molecular basis of schizophrenia (SCZ) and sleep disorder (SD) comorbidity and to systematically analyze the potential mechanism of the Hugan T...

Predicting treatment outcome in congenital adrenal hyperplasia using urine steroidomics and machine learning.

European journal of endocrinology
OBJECTIVE: Treatment monitoring of individuals with congenital adrenal hyperplasia (CAH) remains unsatisfactory. Comprehensive 24 h urine steroid profiling provides detailed insight into adrenal steroid pathways. We investigated whether 24 h urine st...

ChatGPT artificial intelligence in clinical data analysis: an example comparing standard fusion prostate biopsy outcomes after robotic-assisted radical prostatectomy (RaRP).

Archivio italiano di urologia, andrologia : organo ufficiale [di] Societa italiana di ecografia urologica e nefrologica
OBJECTIVE: To compare statistical outputs from ChatGPT 4.0 and human experts in both comparative and correlation analyses in the evaluation of multiparametric MRI/ultrasound fusion-targeted biopsy plus random biopsy versus standard random biopsy alon...