Zexian Zeng

TL;DR: TIMER2.0 (http://timer.cistrome.org/) provides more robust estimation of immune infiltration levels for The Cancer Genome Atlas (TCGA) or user-provided tumor profiles using six state-of-the-art algorithms.

TL;DR: Challenges and opportunities remain across the frontier of NLP application to EHR-based pharmacovigilance, including proper characterization of ADE context, differentiation between off- and on-label drug-useADEs, recognition of the importance of polypharmacy-induced ADEs, better integration of heterogeneous data sources, creation of shared corpora, and organization of shared-task challenges to advance the state-of-the-art.

TL;DR: In this article, the authors present a review of recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping, which includes diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction (DDI), and adverse drug event (ADE) detection, as well as genome-wide and phenome-wide association studies.

TL;DR: Challenges and opportunities remain for NLP-based computational phenotyping, including better model interpretability and generalizability, and proper characterization of feature relations in clinical narratives.

TL;DR: Experimental results suggest that the model has the potential to assist clinicians in identifying patients at greater risk of new onset of AKI in critical care setting and potentially instituting interventions to decrease the likelihood of developing AKI.