AIMC Topic: Male

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Unsupervised Machine Learning in Countermovement Jump and Isometric Mid-Thigh Pull Performance Produces Distinct Combat and Physical Fitness Clusters in Male and Female U.S. Marine Corps Recruits.

Military medicine
INTRODUCTION: Several challenges face the U.S. Marine Corps (USMC) and other services in their efforts to design recruit training to augment warfighter mobility and resilience in both male and female recruits as part of an integrated model. Strength ...

[Exploration of the clinical transformation application of the artificial intelligence diagnosis system for bladder cancer based on cystoscopy].

Zhonghua yi xue za zhi
This study aims to explore the possibility and bottleneck of clinical translation for an artificial intelligence (AI) diagnosis system for bladder cancer based on cystoscopy.We retrospectively collected videos of 101 bladder cancer patients from Janu...

ChatGPT versus clinician: challenging the diagnostic capabilities of artificial intelligence in dermatology.

Clinical and experimental dermatology
BACKGROUND: ChatGPT is an online language-based platform designed to answer questions in a human-like way, using deep learning -technology.

Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer.

World journal of gastroenterology
BACKGROUND: Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical d...

Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis: An observational study.

Medicine
The diagnosis of pneumoconiosis is complex and subjective, leading to inevitable variability in readings. This is especially true for inexperienced doctors. To improve accuracy, a computer-assisted diagnosis system is used for more effective pneumoco...

[Prediction of risk of in-hospital death in patients with chronic heart failure complicated by lung infections using interpretable machine learning].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVE: To predict the risk of in-hospital death in patients with chronic heart failure (CHF) complicated by lung infections using interpretable machine learning.

Acute myocardial infarction prognosis prediction with reliable and interpretable artificial intelligence system.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Predicting mortality after acute myocardial infarction (AMI) is crucial for timely prescription and treatment of AMI patients, but there are no appropriate AI systems for clinicians. Our primary goal is to develop a reliable and interpreta...

Comparing natural language processing representations of coded disease sequences for prediction in electronic health records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Natural language processing (NLP) algorithms are increasingly being applied to obtain unsupervised representations of electronic health record (EHR) data, but their comparative performance at predicting clinical endpoints remains unclear. ...