AIMC Topic: Machine Learning

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Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy.

World journal of gastroenterology
Assisted diagnosis using artificial intelligence has been a holy grail in medical research for many years, and recent developments in computer hardware have enabled the narrower area of machine learning to equip clinicians with potentially useful too...

Combined multivariate analysis and machine learning reveals a predictive module of metabolic stress response in Arabidopsis thaliana.

Molecular omics
Abiotic stress exposure of plants induces metabolic reprogramming which is tightly regulated by signalling cascades connecting transcriptional with translational and metabolic regulation. Complexity of such interconnected metabolic networks impedes t...

Prediction of Atypical Ductal Hyperplasia Upgrades Through a Machine Learning Approach to Reduce Unnecessary Surgical Excisions.

JCO clinical cancer informatics
PURPOSE: Surgical excision is currently recommended for all occurrences of atypical ductal hyperplasia (ADH) found on core needle biopsies for malignancy diagnoses and treatment of lesions. The excision of all ADH lesions may lead to overtreatment, w...

Bring on the Machines: Could Machine Learning Improve the Quality of Patient Education Materials? A Systematic Search and Rapid Review.

JCO clinical cancer informatics
PURPOSE: Clear and trustworthy information is essential for people who are ill. People with cancer, in particular, are targeted with vast quantities of patient education material, but of variable quality. Machine learning technologies are popular acr...

PANOPLY: Omics-Guided Drug Prioritization Method Tailored to an Individual Patient.

JCO clinical cancer informatics
PURPOSE: The majority of patients with cancer receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework, PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory), to identify and p...

Optimizing Outcome Prediction in Diffuse Large B-Cell Lymphoma by Use of Machine Learning and Nationwide Lymphoma Registries: A Nordic Lymphoma Group Study.

JCO clinical cancer informatics
PURPOSE: Prognostic models for diffuse large B-cell lymphoma (DLBCL), such as the International Prognostic Index (IPI) are widely used in clinical practice. The models are typically developed with simplicity in mind and thus do not exploit the full p...

Predicting Emergency Visits and Hospital Admissions During Radiation and Chemoradiation: An Internally Validated Pretreatment Machine Learning Algorithm.

JCO clinical cancer informatics
PURPOSE: Patients undergoing radiotherapy (RT) or chemoradiotherapy (CRT) may require emergency department evaluation or hospitalization. Early identification may direct preventative supportive care, improving outcomes and reducing health care costs....

Automated Renal Cancer Grading Using Nuclear Pleomorphic Patterns.

JCO clinical cancer informatics
PURPOSE: Nuclear pleomorphic patterns are essential for Fuhrman grading of clear cell renal cell carcinoma (ccRCC). Manual observation of renal histopathologic slides may lead to subjective and inconsistent assessment between pathologists. An automat...

Monitoring of Technology Adoption Using Web Content Mining of Location Information and Geographic Information Systems: A Case Study of Digital Breast Tomosynthesis.

JCO clinical cancer informatics
PURPOSE: To our knowledge, integration of Web content mining of publicly available addresses with a geographic information system (GIS) has not been applied to the timely monitoring of medical technology adoption. Here, we explore the diffusion of a ...