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i2b2 AUG 2013
Program
NLP Workshop
- UMLS Ontologies and Ontology Resources (Olivier Bodenreider)
- Ontology-based De-identification of Clinical Naratives (Finch and McMurry)
- Ontology-based Discovery of Disease Activity from the Clinical Record (Lin)
- Ontology Normalisation of the Clinical Narrative (Chen)
- Ontology Concept Selection (Yu)
- Active Learning for Ontology-based Phenotyping (Dligach)
- Conclusion
Academic User Group
- Genomic Cell (Shawn Murphy and Lori Philips)
- SMART Apps (Wattanasin)
- i2b2 Roadmap (Shawn Murphy)
- Planning for the future (Kohane)
- From Genetic Variants to i2b2 using NoSQL database (Matteo Gabetta - Pavia)
- Extending i2b2 with the R Statistical Platform
- Integrated Data Repository Toolkit (IDRT) and ETL Tools (Sebastian Mate - Erlangen; Christian Bauer - Goettingen)
i2b2 SHRINE Conference
- SHRINE Clinical Trials (CT) Functionality and Roadmap (Shawn Murphy)
- SHRINE National Pilot Lessons Learned
- SHRINE Ontology Panel
- University of California Research Exchange (UC ReX) (Doug Berman)
- Preparation for Patient-Centred Research (Ken Mandl)
- Case Study: Improve Care Now (Peter Margolis)
NLP Workshop
UMLS Ontologies and Ontology Resources
Presentation showing how UMLS resources can be used with NLP to extract information from free text.
NLP has two stages:
- Entity Recognition - Identifying important terms within text
- Relationship Extraction - linking entities together
Entity Recognition
Three major problems when identifying entities within a text:
- Entities are missed
- Entities are partially matched - part of the term is matched but another part is missed leading to incomplete information or context. For example, in the term 'bilateral vestibular' only the second word may be matched.
- Ambiguous terms - terms that may have two meanings.
Entities are identified by a combination of normalisation and longest term matching.
Normalisation is the process whereby a term is manipulated to produce a form of words that will match a large number of potential matches. The process involves removing noise words, standardising inflections and derivatives (e.g., remove plural), removing punctuation, converting to lower case, and sorting the words into alphabetical order.
In order to extract the most meaning from the text, an attempt is made to try to match the term with the most number of matching words. For example, 'left atrium' as opposed to just 'atrium'.
Types of Resources useful for Entity Recognition
There are several types of resource:
- Lexical resources - lists of terms with variant spellings, derivatives and inflections, associated with the part of speach to which they refer. These can be either general or include specialist medical terms.
- Ontologies - set of entities with relationships between the entities.
- Technical resources - set of terms and identifiers used to map a term to an ontology.
- Hybrid - A mixture of 1 and 2. They are not strictly speaking ontologies as the relationships may not always be true (e.g., a child may not always be a part of the parent). They are useful for finding terms, but should not be used for aggregation.
Lexical Resources
- UMLS Specialist Lexicon - Medical and general English
- WordNet - General English
- LVG Lexical Variant Generation - specialist tool
- BioLexicon - EU project. Not as general. Mainly focused on genes.
- BioThesaurus - Focused on proteins and genes.
- RxNorm - Drug specific.
Ontological Resources
Terminology Resources
- MetaThesaurus
- Groups terms from many ontologies
- Produces a graph of all the relationships
- Graph is not acyclic and contains contradictions because it reproduces its source ontologies exactly.
- Allows standards to be mapped between.
- RxNorm
- Map between many drug lists.
- Map between branded and generic drug names.
- MetaMap
- Free with licence agreement
- Based on UMLS MetaThesaurus.
- Parses text to find terms.
- Used in IBM's Watson tool.
- Terms can be translated between various standards, including Snomed.
- Copes with term negation and disambiguation.
- TerMine
- WhatIzIt
Relationship Extraction
Orbit Project
The Orbit Project is the Online Registry of Biomedical Informatics Tools.
Ontology-based De-identification of Clinical Naratives
Presentation showing a method to remove Protected Health Information (PHI) from free text fields, using the Apache cTakes lexical annotation tool.
The normal method for attempting to de-identify free text is to train software to recognise personal information. However, the number of training examples available is usually quite small. This team attempted to reverse the task by training the software to recognise non-PHI data.
Pipeline:
- cTakes
- Frequency of term in medical journal articles.
- Match terms to ontologies. Diseases (etc) named after people can be a problem, but matching terms with more than one word implies that it is not a name. For example, 'Hodgkins Lymphoma' would not match 'Mr Hodgkins'
- Remove items from known PHI lists - presumably the person's name and address, etc.
Ontology-based Discovery of Disease Activity from the Clinical Record
Presentation of a project to use NLP to find evidence of disease activity and find its temporal relationship to drug events to identify patients as responders or non-responders for genetic analysis.
This talk put forward a method of using 3 data sets when training the software:
- Annotated training set
- Known set - a pre-annotated set that is used repeatedly to test the software, but not to train it.
- Unknown random set - a random set of a larger set that is used once for testing. The results of the test are manually assesed after the run.
Ontology Normalisation of the Clinical Narrative
Introduction to the Apache cTakes project: a set of tools for parsing text, one of which is a UMLS component, but can also use custom dictionary.
Can be used to extract UMLS concepts (or other codes), but also extracts who the patient is where (for example, knee) and also negation.
