AIMC Topic: Machine Learning

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Machine learning to understand risks for severe COVID-19 outcomes: a retrospective cohort study of immune-mediated inflammatory diseases, immunomodulatory medications, and comorbidities in a large US health-care system.

The Lancet. Digital health
BACKGROUND: In the context of immune-mediated inflammatory diseases (IMIDs), COVID-19 outcomes are incompletely understood and vary considerably depending on the patient population studied. We aimed to analyse severe COVID-19 outcomes and to investig...

The Florida Scoring System for stratifying children with suspected Sjögren's disease: a cross-sectional machine learning study.

The Lancet. Rheumatology
BACKGROUND: Childhood Sjögren's disease is a rare, underdiagnosed, and poorly-understood condition. By integrating machine learning models on a paediatric cohort in the USA, we aimed to develop a novel system (the Florida Scoring System) for stratify...

Understanding and Mitigating Bias in Imaging Artificial Intelligence.

Radiographics : a review publication of the Radiological Society of North America, Inc
Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model development, with potential for exacerbating health disparities. However, bias in imaging AI is a complex topic that encompasses multiple coexisting definitions. m...

Machine Learning-Based Prediction of Hospitalization During Chemoradiotherapy With Daily Step Counts.

JAMA oncology
IMPORTANCE: Toxic effects of concurrent chemoradiotherapy (CRT) can cause treatment interruptions and hospitalizations, reducing treatment efficacy and increasing health care costs. Physical activity monitoring may enable early identification of pati...

Semi-supervised Learning for Generalizable Intracranial Hemorrhage Detection and Segmentation.

Radiology. Artificial intelligence
Purpose To develop and evaluate a semi-supervised learning model for intracranial hemorrhage detection and segmentation on an out-of-distribution head CT evaluation set. Materials and Methods This retrospective study used semi-supervised learning to ...

Develop and Validate a Prognostic Index With Laboratory Tests to Predict Mortality in Middle-Aged and Older Adults Using Machine Learning Models: A Prospective Cohort Study.

The journals of gerontology. Series A, Biological sciences and medical sciences
BACKGROUND: Prognostic indices can enhance personalized predictions of health burdens. However, a simple, practical, and reproducible tool is lacking for clinical use. This study aimed to develop a machine learning-based prognostic index for predicti...

Don't Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology.

Archives of pathology & laboratory medicine
CONTEXT: Automated prostate cancer detection using machine learning technology has led to speculation that pathologists will soon be replaced by algorithms. This review covers the development of machine learning algorithms and their reported effectiv...

An interpretable machine learning model to predict off-pump coronary artery bypass grafting-associated acute kidney injury.

Advances in clinical and experimental medicine : official organ Wroclaw Medical University
BACKGROUND: Off-pump coronary artery bypass grafting-associated acute kidney injury (OPCAB-AKI) is related to 30-day perioperative mortality. Existing mathematical models cannot be applied to help clinicians make early diagnosis and intervention deci...

Screening Automation in Systematic Reviews: Analysis of Tools and Their Machine Learning Capabilities.

Studies in health technology and informatics
Systematic reviews provide robust evidence but require significant human labor, a challenge that can be mitigated with digital tools. This paper focuses on machine learning (ML) support for the title and abstract screening phase, the most time-intens...