Joel T. Dudley

TL;DR: A comprehensive overview of the development and uptake of NLP methods applied to free-text clinical notes related to chronic diseases is provided, including the investigation of challenges faced by NLP methodologies in understanding clinical narratives.

TL;DR: In this paper, a single model that directly combines image features, patient and hospital process data outperforms a Naive Bayes ensemble of an image-only model prediction, patient, and hospital processes data.

TL;DR: In this article, the authors present a review of the use of machine learning methods compared to rule-based approaches in clinical NLP, showing that the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from free text or integration of clinical notes with structured data.

TL;DR: An attempt to develop a data-driven, electronic-medical record-wide (EMR-wide) feature selection approach and subsequent machine learning to predict readmission probabilities and results are encouraging and reveal the utility of such datadriven machine learning.

TL;DR: This study shows that ELS in a postnatal sensitive period increases sensitivity to adult stress in female mice, consistent with earlier findings in male mice, and provides transcriptomic evidence that E LS increasesensitivity to future stress through enhancement of known programs of cortical plasticity.