{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/developing-a-portable-natural-language","title":"Developing a Portable Natural Language Processing Based Phenotyping System","arxiv_id":"1807.06638","date":"2018-07-17","proceeding":null,"authors":["Himanshu Sharma","Chengsheng Mao","Yizhen Zhang","Haleh Vatani","Liang Yao","Yizhen Zhong","Luke Rasmussen","Guoqian Jiang","Jyotishman Pathak","Yuan Luo"],"abstract":"This paper presents a portable phenotyping system that is capable of\nintegrating both rule-based and statistical machine learning based approaches.\nOur system utilizes UMLS to extract clinically relevant features from the\nunstructured text and then facilitates portability across different\ninstitutions and data systems by incorporating OHDSI's OMOP Common Data Model\n(CDM) to standardize necessary data elements. Our system can also store the key\ncomponents of rule-based systems (e.g., regular expression matches) in the\nformat of OMOP CDM, thus enabling the reuse, adaptation and extension of many\nexisting rule-based clinical NLP systems. We experimented with our system on\nthe corpus from i2b2's Obesity Challenge as a pilot study. Our system\nfacilitates portable phenotyping of obesity and its 15 comorbidities based on\nthe unstructured patient discharge summaries, while achieving a performance\nthat often ranked among the top 10 of the challenge participants. This\nstandardization enables a consistent application of numerous rule-based and\nmachine learning based classification techniques downstream.","url_abs":"http://arxiv.org/abs/1807.06638v1","url_pdf":"http://arxiv.org/pdf/1807.06638v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"developing-a-portable-natural-language","repo_url":"https://github.com/mocherson/portableNLP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}