AIMC Topic: Patient Selection

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Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial.

IEEE journal of biomedical and health informatics
Clinical trials often fail to recruit an adequate number of appropriate patients. Identifying eligible trial participants is resource-intensive when relying on manual review of clinical notes, particularly in critical care settings where the time win...

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review.

Hepatology (Baltimore, Md.)
Machine learning (ML) utilizes artificial intelligence to generate predictive models efficiently and more effectively than conventional methods through detection of hidden patterns within large data sets. With this in mind, there are several areas wi...

Artificial intelligence in abdominal aortic aneurysm.

Journal of vascular surgery
OBJECTIVE: Abdominal aortic aneurysm (AAA) is a life-threatening disease, and the only curative treatment relies on open or endovascular repair. The decision to treat relies on the evaluation of the risk of AAA growth and rupture, which can be diffic...

Patient selection for proton therapy: a radiobiological fuzzy Markov model incorporating robust plan analysis.

Physical and engineering sciences in medicine
While proton therapy can offer increased sparing of healthy tissue compared with X-ray therapy, it can be difficult to predict whether a benefit can be expected for an individual patient. Predictive modelling may aid in this respect. However, the pre...

Prediction of progression from pre-diabetes to diabetes: Development and validation of a machine learning model.

Diabetes/metabolism research and reviews
AIMS: Identification, a priori, of those at high risk of progression from pre-diabetes to diabetes may enable targeted delivery of interventional programmes while avoiding the burden of prevention and treatment in those at low risk. We studied whethe...

Improving Clinical Trial Participant Prescreening With Artificial Intelligence (AI): A Comparison of the Results of AI-Assisted vs Standard Methods in 3 Oncology Trials.

Therapeutic innovation & regulatory science
BACKGROUND: Delays in clinical trial enrollment and difficulties enrolling representative samples continue to vex sponsors, sites, and patient populations. Here we investigated use of an artificial intelligence-powered technology, Mendel.ai, as a mea...

Out damn bot, out: Recruiting real people into substance use studies on the internet.

Substance abuse
While the Internet has become a popular and effective strategy for recruiting substance users into research, there is a large risk of recruiting duplicate individuals and Internet bots that pose as humans. Strategies to mitigate these issues are outl...