Paper Info
Title
Portable Phenotyping System: A Portable Machine-Learning Approach to i2b2 Obesity Challenge
Abstract
This paper presents a portable phenotyping system that is capable of integrating both rule-based and statistical machine learning based approaches. Our system utilizes UMLS to extract clinically relevant features from the unstructured text and then facilitates portability across different institutions and data systems by incorporating ODHSI's OMOP Common Data Model (CDM) to standardize necessary data elements. Our system can also store the key components of rule-based systems (e.g., regular expression matches) in the format of OMOP CDM, thus enabling the reuse, adaptation and extension of many existing rule-based clinical NLP systems. We experimented our system on the corpus from i2b2's Obesity Challenge as a pilot study. Our system facilitates portable phenotyping of obesity and its 15 comorbidities based on the unstructured patient discharge summaries, while achieving a performance that often ranked among the top 10 of the challenge participants. This standardization enables a consistent application of numerous rule-based and machine learning based classification techniques downstream.
Year
DOI
Venue
2018
10.1109/ICHI-W.2018.00032
2018 IEEE International Conference on Healthcare Informatics Workshop (ICHI-W)
Keywords
Field
DocType
NLP,Portability,Machine Learning,Obesity,i2b2,OMOP CDM
Data modeling,Data system,Computer science,Support vector machine,Feature extraction,Software portability,Artificial intelligence,Standardization,Unified Medical Language System,Data model,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-5386-6778-1
0
0.34
References 
Authors
0
10
Name
Order
Citations
PageRank
Himanshu Sharma1202.58
Chengsheng Mao2156.09
Yizhen Zhang310919.33
Haleh Vatani400.34
Liang Yao55515.40
Yizhen Zhong611.70
Luke V. Rasmussen710523.74
Guoqian Jiang821050.15
Jyotishman Pathak967776.52
Yuan Luo1013622.83