Tag: machine learning

Transcriptomic Fingerprint of Bacterial Infection in Lower Extremity Ulcers @ALPSLimb #ActAgainstAmputation

Important work from our Danish and Aussie colleagues Abstract Background and purpose: Clinicians and researchers utilize subjective, clinical classification systems to stratify lower extremity ulcer infections for treatment and research. The purpose of this study was to examine whether these clinical classifications are reflected in the ulcer's transcriptome. Methods: RNA-sequencing (RNA-seq) was performed on biopsies from clinically... Continue Reading →

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An explainable machine learning model for predicting in‐hospital amputation rate of patients with diabetic foot ulcer

Just published this past week by our collective Chongqing/Singapore/and local LA squad. Diabetic foot ulcer (DFU) is one of the most serious and alarming diabetic complications, which often leads to high amputation rates in diabetic patients. Machine learning is a part of the field of artificial intelligence, which can automatically learn models from data and... Continue Reading →

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Toward Machine-Learning-Based Decision Support in Diabetes Care: A Risk Stratification Study on Diabetic Foot Ulcer and Amputation

Great effort from our colleagues in Aarhus and Copenhagen. Diabetes mellitus is associated with serious complications, with foot ulcers and amputation of limbs among the most debilitating consequences of late diagnosis and treatment of foot ulcers. Thus, prediction and on-time treatment of diabetic foot ulcers (DFU) are of great importance for improving and maintaining patients'... Continue Reading →

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