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Patient Selection

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Machine learning for selecting patients with Crohn's disease for abdominopelvic computed tomography in the emergency department.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: Patients with Crohn's disease (CD) frequently undergo abdominopelvic computed tomography (APCT) in the emergency department (ED). It's essential to diagnose clinically actionable findings (CAF) as they may need immediate intervention, fre...

Using machine-learning algorithms to identify patients at high risk of upper gastrointestinal lesions for endoscopy.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Endoscopic screening for early detection of upper gastrointestinal (UGI) lesions is important. However, population-based endoscopic screening is difficult to implement in populous countries. By identifying high-risk individuals fr...

Learning and confirming a class of treatment responders in clinical trials.

Statistics in medicine
Clinical trials require substantial effort and time to complete, and regulatory agencies may require two successful efficacy trials before approving a new drug. One way to improve the chance of follow-up success is to identify a subpopulation among w...

Machine learning application for patient stratification and phenotype/genotype investigation in a rare disease.

Briefings in bioinformatics
Alkaptonuria (AKU, OMIM: 203500) is an autosomal recessive disorder caused by mutations in the Homogentisate 1,2-dioxygenase (HGD) gene. A lack of standardized data, information and methodologies to assess disease severity and progression represents ...

Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials.

Clinical and translational science
Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a m...

Machine Learning and Surgical Outcomes Prediction: A Systematic Review.

The Journal of surgical research
BACKGROUND: Machine learning (ML) has garnered increasing attention as a means to quantitatively analyze the growing and complex medical data to improve individualized patient care. We herein aim to critically examine the current state of ML in predi...

The path to precision medicine for MS, from AI to patient recruitment: an interview with Mauricio Farez and Helen Onuorah.

Communications biology
This year’s World Brain Day is focused on stopping Multiple Sclerosis (MS). Although amazing progress has resulted in the development of relatively successful MS therapies, access to such therapies is a major problem for most of the world. In additio...

Emerging role of artificial intelligence in stroke imaging.

Expert review of neurotherapeutics
: The recognition and therapy of patients with stroke is becoming progressively intricate as additional treatment choices become accessible and new associations between disease characteristics and treatment response are incessantly uncovered. Therefo...