AIMC Topic: Sex Factors
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Rule-based natural language processing to examine variation in worsening heart failure hospitalizations by age, sex, race and ethnicity, and left ventricular ejection fraction.
American heart journal
Sep 7, 2024
BACKGROUND: Prior studies characterizing worsening heart failure events (WHFE) have been limited in using structured healthcare data from hospitalizations, and with little exploration of sociodemographic variation. The current study examined the impa...
Sex-Based Performance Disparities in Machine Learning Algorithms for Cardiac Disease Prediction: Exploratory Study.
Journal of medical Internet research
Aug 26, 2024
BACKGROUND: The presence of bias in artificial intelligence has garnered increased attention, with inequities in algorithmic performance being exposed across the fields of criminal justice, education, and welfare services. In health care, the inequit...
Gender-specific factors of suicidal ideation among high school students in Yunnan province, China: A machine learning approach.
Journal of affective disorders
Aug 13, 2024
BACKGROUND: Suicidal ideation (SI) assumes a pivotal role in predicting suicidal behaviors. The incidence of SI among high (junior and senior) school students is significantly higher than that of other age groups. The aim of this study is to explore ...
Sex and population differences in the cardiometabolic continuum: a machine learning study using the UK Biobank and ELSA-Brasil cohorts.
BMC public health
Aug 6, 2024
BACKGROUND: The temporal relationships across cardiometabolic diseases (CMDs) were recently conceptualized as the cardiometabolic continuum (CMC), sequence of cardiovascular events that stem from gene-environmental interactions, unhealthy lifestyle i...
A competing risks machine learning study of neutron dose, fractionation, age, and sex effects on mortality in 21,000 mice.
Scientific reports
Aug 2, 2024
This study explores the impact of densely-ionizing radiation on non-cancer and cancer diseases, focusing on dose, fractionation, age, and sex effects. Using historical mortality data from approximately 21,000 mice exposed to fission neutrons, we empl...
Gender Differences in Letters of Recommendations and Personal Statements for Neurotology Fellowship over 10 Years: A Deep Learning Linguistic Analysis.
Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
Jul 25, 2024
OBJECTIVE: Personal statements (PSs) and letters of recommendation (LORs) are critical components of the neurotology fellowship application process but can be subject to implicit biases. This study evaluated general and deep learning linguistic diffe...
Using machine learning-based algorithms to construct cardiovascular risk prediction models for Taiwanese adults based on traditional and novel risk factors.
BMC medical informatics and decision making
Jul 22, 2024
OBJECTIVE: To develop and validate machine learning models for predicting coronary artery disease (CAD) within a Taiwanese cohort, with an emphasis on identifying significant predictors and comparing the performance of various models.
A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction.
Journal of medical systems
Jul 19, 2024
Artificial intelligence (AI) based predictive models for early detection of cardiovascular disease (CVD) risk are increasingly being utilised. However, AI based risk prediction models that account for right-censored data have been overlooked. This sy...
Gender-based linguistic differences in letters of recommendation for rhinology fellowship over time: A dual-institutional follow-up study using natural language processing and deep learning.
International forum of allergy & rhinology
Jul 16, 2024
This follow-up dual-institutional and longitudinal study further evaluated for underlying gender biases in LORs for rhinology fellowship. Explicit and implicit linguistic gender bias was found, heavily favoring male applicants.
Recognition of Patient Gender: A Machine Learning Preliminary Analysis Using Heart Sounds from Children and Adolescents.
Pediatric cardiology
Jun 27, 2024
Research has shown that X-rays and fundus images can classify gender, age group, and race, raising concerns about bias and fairness in medical AI applications. However, the potential for physiological sounds to classify sociodemographic traits has no...
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