Marc Vincent

TL;DR: In this large phase 3 study, statistically significant associations were found between DPYD variants (D949V and V732I) and increased incidence of grade 3 or greater fluorouracil AEs in patients treated with adjuvant fluorOURacil-based combination chemotherapy.

TL;DR: The prediction accuracy reached by the connectivity prediction method compares favorably with respect to all but the most complex other approaches and does not need any model selection or hyperparameter tuning, a property that makes it less prone to overfitting and prediction accuracy overestimation.

TL;DR: The authors applied machine learning to arsenic species and metallomics profiles of toenails to evaluate associations of environmental arsenic with incident cancer cases, user satisfaction with an AI system for chest X-ray analysis implemented in a hospital’s emergency setting; scaling AI projects for radiology causes and consequences; ECG classification using combination of linear and non-linear features with neural network;dataset comparison tool: utility and privacy; when context matters for credible measurement of drug-drug interactions based on real-world data; a lightweight and interpretable model to classify bundle branch blocks from ECG signals; analysis of stroke assistance in Covid-19 pandemic by process mining techniques; automatic diagnosis of autism spectrum disorder condition using shape based features extracted from brainstem; using explainable supervised machine learning, and an image based object recognition system for wound detection and classification of diabetic foot and venous leg ulcers.

TL;DR: This work applies machine learning techniques to automatically predict whether a text is positive or negative (the opinion classification task) and briefly evaluates the merits of applying feature selection algorithms to the authors' models.