1.
Ontology & Oncology: NLP
for Precision Medicine
Sean Mullane
Data Scientist
Quality & Performance Improvement

2.
Why Precision Medicine? And Why Oncology?
The Challenge
• Single patient may have tumors with
a variety of mutations and phenotypes.
• A single treatment may work against
one but not against others.
• Incomplete treatment buys a patient
little to no additional time.

3.
Why Precision Medicine? And Why Oncology?
The Promise
• Allow treatments to be tailored to unique
genetic changes in a person’s cancer.
• Target specific molecules that are
involved in the growth, progression,
and spread of cancer.
• Can be more effective against cancer
cells while being less toxic to the patient.

4.
Milestones in Cancer Understanding
1937 1960 1976 1979 1994 1998 2001 2010 2011 2014
https://www.cancer.gov/research/progress/250-years-milestones

5.
Fighting Information Overload
The Needle in a Haystack Problem
• Scientific publications
increasing at an
exponential rate.
• Increasingly difficult
for physicians to keep
up.
• How can we help?
PubMed: “Cancer”

6.
TREC Scientific Abstracts Query Task
Challenge: Return the most relevant publications for treatment of a unique patient.
Query topics are synthetic patient cases created by MD Anderson
precision oncologists.

7.
Want a Text Model for Query Relevance
• Vector space-based scoring model
• Term frequency/inverse document frequency weighting
• Field length normalization
• Coordination factors
• Term/query clause boosting

8.
Simple Solution: Use Elasticsearch
• JSON-based REST API
• Lucene query language
• Open source

9.
Problem Solved? No.
How can we improve on this?
A tremendous amount of knowledge exists in medical science.
How do we leverage that collected knowledge to improve query
relevance results?
Solution: Text term embedding and graph-based query expansion.

10.
Medical Ontologies
Unified Medical Language System®
• A set of ontologies sources from National Library of Medicine.
• The UMLS ® Metathesaurus and Semantic Network encode
sets of concepts and relationships between them.
Major groupings of semantic types:
• organism
• anatomical structure
• biologic function
• chemical
• physical object
• idea or concept
Major categories of non-hierarchical relationships:
• physically related to
• spatially related to
• temporally related to
• functionally related to
• conceptually related to

11.
How to use ontologies effectively?
Text Term Embedding
Apache cTAKES: annotates
documents with UMLS
concepts.
Maps words and phrases in the
text to one or more discrete,
identified concepts (CUIs) in the
Metathesaurus.

12.
How to use ontologies effectively?
Text Term Embedding
cTAKES annotates:
• PubMed article abstracts
with UMLS concepts.
• Queries for diagnosis,
gene, demographics.
Article Embedding
…, C0278883, C0030012, C0025202,
C1523298, C0599156, …
Query Terms
{
"diagnosis": "melanoma",
"diagnosisCUI": "C0025202",
"gene": "BRAF (V600E)",
"geneCUI": "C3250916 ",
"demographics": " 64-year-old male",
"demoCUI": "C0001675"
}

13.
Benefit of Concept Embedding
malignant neoplasm
cancer
malignancy
C0006826
C0006826
Embedding reduces dimensionality of the vector space and
increases probability of exact article <-> query matches.

14.
Performance Gain from Concept Embedding

15.
Query Expansion
Definition: Rewriting a given query to improve document retrieval performance
Example:
“Melanoma” {Melanoma, Melanocytic Neoplasm,
MIRLET7A3}
Would like to increase both precision and recall.
• Concept embedding improved precision.
• Can we increase improve recall as well?

16.
Introduce Relatedness Graph solution
Use visuals to explain relatedness graph

17.
Introduce Relatedness Graph solution
Use visuals to explain relatedness graph

18.
An Exclusivity-Based Relatedness Metric
Hulpuş, Prangnawarat, and Hayes. “Path-Based Semantic Relatedness on Linked Data and Its Use to Word and Entity
Disambiguation, 2015

19.
Introduce Relatedness Graph solution
Use visuals to explain relatedness graph

20.
Full System
Overall best median performance from queries expanded with exact concepts combined
with boosted, expanded concepts based on graph neighbors selected by relatedness

21.
Conclusion
Precision medicine can only succeed with careful targeting of
treatments to patients.
Existing medical knowledge base is difficult to apply, but valuable.
Concept embedding and concept graph traversal improve
precision and recall of relevant literature for individual patients.

22.
Q&A
https://github.com/seanmullane/TREC_2018_UVA
sean.mullane@virginia.edu