AIMC Topic: Surveys and Questionnaires

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SymScore: Machine learning accuracy meets transparency in a symbolic regression-based clinical score generator.

Computers in biology and medicine
Self-report questionnaires play a crucial role in healthcare for assessing disease risks, yet their extensive length can be burdensome for respondents, potentially compromising data quality. To address this, machine learning-based shortened questionn...

Trajectory of breastfeeding among Chinese women and risk prediction models based on machine learning: a cohort study.

BMC pregnancy and childbirth
BACKGROUND: Breastfeeding is the optimal source of nutrition for infants and young children, essential for their healthy growth and development. However, a gap in cohort studies tracking breastfeeding up to six months postpartum may lead caregivers t...

Identifying the Relative Importance of Factors Influencing Medication Compliance in General Patients Using Regularized Logistic Regression and LightGBM: Web-Based Survey Analysis.

JMIR formative research
BACKGROUND: Medication compliance, which refers to the extent to which patients correctly adhere to prescribed regimens, is influenced by various psychological, behavioral, and demographic factors. When analyzing these factors, challenges such as mul...

Ethics and trustworthiness of artificial intelligence in Hepato-Pancreato-Biliary surgery: a snapshot of insights from the European-African Hepato-Pancreato-Biliary Association (E-AHPBA) survey.

HPB : the official journal of the International Hepato Pancreato Biliary Association
BACKGROUND: Hepato-Pancreato-Biliary (HPB) surgery is a complex specialty and Artificial Intelligence (AI) applications have the potential to improve pre- intra- and postoperative outcomes of HPB surgery. While ethics guidelines have been developed f...

Patient and practitioner perceptions around use of artificial intelligence within the English NHS diabetic eye screening programme.

Diabetes research and clinical practice
AIMS: Automated retinal image analysis using Artificial Intelligence (AI) can detect diabetic retinopathy as accurately as human graders, but it is not yet licensed in the NHS Diabetic Eye Screening Programme (DESP) in England. This study aims to ass...

Development and validation of a machine learning-based predictive model for compassion fatigue in Chinese nursing interns: a cross-sectional study utilizing latent profile analysis.

BMC medical education
BACKGROUND: Compassion fatigue is a significant issue in nursing, affecting both registered nurses and nursing students, potentially leading to burnout and reduced quality of care. During internships, compassion fatigue can shape nursing students' ca...

Efficient Screening in Obstructive Sleep Apnea Using Sequential Machine Learning Models, Questionnaires, and Pulse Oximetry Signals: Mixed Methods Study.

Journal of medical Internet research
BACKGROUND: Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by frequent pauses or shallow breathing during sleep. Polysomnography, the gold standard for OSA assessment, is time consuming and labor intensive, thus limiting di...

XGBoost-SHAP-based interpretable diagnostic framework for knee osteoarthritis: a population-based retrospective cohort study.

Arthritis research & therapy
OBJECTIVE: To use routine demographic and clinical data to develop an interpretable individual-level machine learning (ML) model to diagnose knee osteoarthritis (KOA) and to identify highly ranked features.

Demographic factors, knowledge, attitude and perception and their association with nursing students' intention to use artificial intelligence (AI): a multicentre survey across 10 Arab countries.

BMC medical education
BACKGROUND: Artificial intelligence (AI) is becoming increasingly important in healthcare, with a significant impact on nursing practice. As future healthcare practitioners, nursing students must be prepared to incorporate AI technologies into their ...