AIMC Topic: Humans

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The Effect of Artificial Intelligence on Patient-Physician Trust: Cross-Sectional Vignette Study.

Journal of medical Internet research
BACKGROUND: Clinical decision support systems (CDSSs) based on routine care data, using artificial intelligence (AI), are increasingly being developed. Previous studies focused largely on the technical aspects of using AI, but the acceptability of th...

Using AI-Based Technologies to Help Nurses Detect Behavioral Disorders: Narrative Literature Review.

JMIR nursing
BACKGROUND: The behavioral and psychological symptoms of dementia (BPSD) are common among people with dementia and have multiple negative consequences. Artificial intelligence-based technologies (AITs) have the potential to help nurses in the early p...

A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability.

Critical care (London, England)
BACKGROUND: Sepsis, an acute and potentially fatal systemic response to infection, significantly impacts global health by affecting millions annually. Prompt identification of sepsis is vital, as treatment delays lead to increased fatalities through ...

PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.

Journal of neural engineering
. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings.. We introdu...

Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU.

Frontiers in public health
INTRODUCTION: Neonatal intensive care unit (NICU) admission is a stressful experience for parents. NICU parents are twice at risk of depression symptoms compared to the general birthing population. Parental mental health problems have harmful long-te...

Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation.

Frontiers in public health
INTRODUCTION: Exercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. However, the contribution of well-known risk factors for prognosis of coronary artery disease (CAD) to p...

Machine learning model for cardiovascular disease prediction in patients with chronic kidney disease.

Frontiers in endocrinology
INTRODUCTION: Cardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD). This study aimed to develop CVD risk prediction models using machine learning to support clinical decision making and improve pati...

Interpretable deep learning reveals the role of an E-box motif in suppressing somatic hypermutation of AGCT motifs within human immunoglobulin variable regions.

Frontiers in immunology
INTRODUCTION: Somatic hypermutation (SHM) of immunoglobulin variable (V) regions by activation induced deaminase (AID) is essential for robust, long-term humoral immunity against pathogen and vaccine antigens. AID mutates cytosines preferentially wit...

Machine learning-based model for predicting tumor recurrence after interventional therapy in HBV-related hepatocellular carcinoma patients with low preoperative platelet-albumin-bilirubin score.

Frontiers in immunology
INTRODUCTION: This study aimed to develop a prognostic nomogram for predicting the recurrence-free survival (RFS) of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients with low preoperative platelet-albumin-bilirubin (PALBI) scor...