Customize cTAKES for Automated Adverse Drug Event Surveillance in Pediatric Pulmonary Hypertension
Channel: The ASF
Date: 2020-10-14

Customize cTAKES for Automated Adverse Drug Event Surveillance in Pediatric Pulmonary Hypertension
Chen Lin

A presentation from ApacheCon @Home 2020
https://apachecon.com/acah2020/

Based on the Apache clinical Text Analysis Knowledge Extraction System (cTAKES), an open-source NLP system, we built a customized pipeline and processed 149,038 notes for 984 pediatric Pulmonary Hypertension (PH) patients for detecting textual mentions and signs/symptoms that may represent adverse drug events (ADE). Our pipeline featured a customized dictionary for interested term mentions and emphasized term negation, temporality of events, proximity among mentions, for a refined detection for co-occurrence of medications and potential drug effects. Analysis showed our automatic ADE detection system identified up to 7-fold higher ADE rates than those ascertained from diagnostic codes.

Chen is an Applications Development Specialist in the Children’s Hospital Informatics Program-Natural Language Processing (CHIP-NLP) group. Chen is actively incorporating statistical and machine learning technologies into advanced NLP tasks being investigated here at CHIP-NLP. Topics include automatic feature selection, coreference resolution, disease activity classification based on clinical narratives, etc. Chen has worked on several projects including the development of a novel complementary mining process that made use of unused features by a priori defined phenotypes; he authored interactive phenotype-mining and visualizing software; in addition Chen has research experience in deriving human cancer gene interaction networks based on genome-wide survival analysis.