Mohammed Khalilia

TL;DR: Overall, the RF ensemble learning method outperformed SVM, bagging and boosting in terms of the area under the receiver operating characteristic (ROC) curve (AUC), and has the advantage of computing the importance of each variable in the classification process.

TL;DR: The Digital Slide Archive (DSA) is described, an open-source web-based platform for digital pathology that allows investigators to manage large collections of histologic images and integrate them with clinical and genomic metadata.

TL;DR: This study uses an internal medical natural language processing service to perform named entity extraction and negation detection on clinical notes and compose selected entities into a new text corpus to train document representations, and proposes a multimodal neural network to jointly train time series signals and unstructured clinical text representations.

TL;DR: Comprehend Medical as discussed by the authors is a stateless and Health Insurance Portability and Accountability Act (HIPAA) eligible Named Entity Recognition (NER) and Relationship Extraction (RE) service launched under Amazon Web Services (AWS) trained using state-of-the-art deep learning models.

TL;DR: This article compares five methods for Euclideanizing D to D and concludes that the subdominant ultrametric transformation is a clear winner, producing much better partitions of D ˜ than the other four methods.