text mining in biomedicine michael krauthammer department of pathology yale university school of...

Text Mining in Biomedicine

Michael Krauthammer

Department of Pathology

Yale University School of Medicine

Definition

• Text mining is – the process of automatically extracting knowledge

from large text collections– data mining applied to text documents / knowledge

discovery from text– a modular process similar to reading, where facts

from different articles / books are combined for novel inference (de Bruijn 2002)

Examples in Biomedicine

Protein A

activates

Protein B

Protein C

triggers

Apoptosis

Protein B

activates

Protein C

Text Mining System

Protein A

Protein B

Apoptosis

Protein C

Signal Transduction

© Max Planck Institute of Molecular Physiology

Signal Transduction - Apoptosis

© Daniel Focosi / Molecular Medicine

Mining Molecular Interactions

Information Explosion

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 20010

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5x 104

YEAR

Number of articles 10

4

MEDLINE - keyword "apoptosis"

Mining Molecular Interactions

Protein A

activates

Protein B

Protein C

triggers

Apoptosis

Protein B

activates

Protein C

GeneWays System

Protein A

Protein B

Apoptosis

Protein C

Network-based Candidate Gene Prediction

Text Mining - Components

Information Extraction

• Information Extraction: “the activity of populating a structured information source (or database) from an unstructured, or free text, information source” (Gauzuskas & Wilks 1998)


• Many information sources are free text: • Law (Court Orders)• Academic Research (Research Articles)• Finance (Quarterly Reports)• Medicine (Discharge Summaries)• Biology (Molecular Interactions)

• Data analysis on free text is difficult• Transformation of free text into structured data

(machine-readable)


DISCHARGE SUMMARY

(free text)PATIENT DATABASE

(structured data)

Name Smith

Symptom fever

Symptom weight

loss

Patient Smith

reports

fever and

weight loss

INFORMATION

EXTRACTION


SCIENTIFIC ARTICLE

(free text)RESEARCH DATABASE

(structured data)

Substance Protein A

Interaction activation

Substance Protein B

INFORMATION

EXTRACTION

We observed

the activation

of protein A

by protein B


SCIENTIFIC ARTICLE


(structured data)

Substance Protein A


Substance Protein B

INFORMATION

EXTRACTION

We observed

the activation

of protein A

by protein B

Natural Language Processing


SCIENTIFIC ARTICLE


(structured data)

Substance Protein A


Substance Protein B

INFORMATION

EXTRACTION

We observed

the activation

of protein A

by protein B

Statistical methods

Pattern matching

Full/Shallow parsing

Statistical Methods

• Stapley (2000): Measuring gene associations• Venn diagram of a set of Medline documents showing

the• Intersection of documents containing both genes i and j.• BioBibliometric distance: dij=(|i|+|j|) / (|ij|)

gene i gene j

Stapley, B. J. and G. Benoit (2000). “Biobibliometrics: information retrieval and visualization from co- occurrences of gene names in Medline

abstracts.” Pac Symp Biocomput: 529-40.

Pattern Matching

• Pattern matching (~regexp) to extract protein-protein interactions

• <gene> <interact with> <gene>

Blaschke, C., M. A. Andrade, et al. (1999). “Automatic extraction of biological information from scientific text: protein-protein interactions.” Proc Int Conf Intell Syst Mol Biol: 60-7.

Ng, S. K. and M. Wong (1999). “Toward Routine Automatic Pathway Discovery from On-line Scientific Text Abstracts.” Genome Inform Ser Workshop Genome Inform 10: 104-112.

Ono, T., H. Hishigaki, et al. (2001). “Automated extraction of information on protein-protein interactions from the biological literature.” Bioinformatics 17(2): 155-61.

Full Parsing

• Parsing: Detect sequence of grammar rules that describe internal structure of sentence

• Grammar rule: S -> NP VP

• [The house]NP [was demolished]VP.

• Syntax parse tree:

Full Parsing

• Language Parsing in Biomedicine• MedLEE and GENIES semantic grammar parsers• Columbia University, Dr. Carol Friedman• MedLEE: Clinical medicine parser: discharge summaries, radiology

reports, pathology reports• the patient has a family history of coronary artery disease

<problem v = "disease" idref = "p64">

<bodyloc v = "coronary artery" idref = "p60">/bodyloc>

<status v=”family history”> </status>

</problem>

Full Parsing

• GENIES: parser for molecular domain. Extracts molecular interactions.

• Frame representation: Each frame is a list beginning with the elements type, value, possibly followed by additional frames:

[protein, Il-2, [state, active]]

• For example, the parse of Raf-1 activates Mek-1 is

[action, activate,

[protein, Raf-1], [protein, Mek-1]]

Full Parsing

• Handles nested sentences (context free language):• mediation of sonic hedgehog-induced expression of Coup-Tfii by a protein

phosphatase

[action,promote,[geneorprotein, phosphatase],

[action,activate,[geneorprotein,sonic hedgehog],

[action,express,X,[geneorprotein,Coup-Tfii]]]]

Full Parsing

Hafner, C. D., K. Baclawski, et al. (1994). “Creating a knowledge base of biological research papers.” Proc Int Conf Intell Syst Mol Biol 2: 147-55.

Friedman, C., P. Kra, et al. (2001). GENIES: A Natural-Language System for the Extraction of Molecular Pathways from Complete Journal Articles. Proc Int Conf Intell Syst Mol Biol, Kopenhagen.

Yakushiji A, Tateisi Y, Miyao Y, Tsujii J. Event extraction from biomedical papers using a full parser.Pac Symp Biocomput. 2001:408-19.

McDonald DM, Chen H, Su H, Marshall BB. Extracting gene pathway relations using a hybrid grammar: the Arizona relation parser.Bioinformatics. 2004 Jul 15

Leroy G, Chen H, Martinez JD. A shallow parser based on closed-class words to capture relations in biomedical text.J Biomed Inform. 2003 Jun;36(3):145-58.

Koike A, Niwa Y, Takagi T. Automatic extraction of gene/protein biological functions from biomedical text.Bioinformatics. 2004 Oct 27

Daraselia N, Yuryev A, Egorov S, Novichkova S, Nikitin A, Mazo I. Extracting human protein interactions from MEDLINE using a full-sentence parser.Bioinformatics. 2004 Mar 22;20(5):604-11. Epub 2004 Jan 22

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Display&dopt=pubmed_pubmed&from_uid=15033866

Shallow Semantic Parsing

Medical Abstracts

Zocor (Arg0) reduced cholesterol (Arg1)

“The article discussed that Zocor reduced cholesterol in the intervention group.”

Medicine action blood test

DATABASE

What medicine decreased a blood test?

How did a medicine affect a blood test?

Shallow Semantic Parsing

• Shallow Semantic Parsing Technique (SSPT)– Successfully applied in non-medical domain*

– “Predicate-centric”

– Dissect sentences into simple WHAT did WHAT to WHOM/WHAT, and Modifiers (WHEN, WHERE, WHY and HOW)

• The article discussed that Zocor (What) reduced (did What) cholesterol (to What) in the intervention group (modifiers).

– Thus two core arguments, “Zocor” (Argument 0) and “cholesterol” (Argument 1), are related by the predicate “reduce(d)”

– Modifier “in the intervention group”– “The article discussed that” is a null argument, i.e. it is not part of the predicate

arguments.

* S. Pradhan, D. Jurafsky, et al. In Proc. Of NAACL-HLT 2004.

• Treebank contains the Wall Street Journal (WSJ) corpus annotate with syntactic information

• Propbank annotates the same WSJ corpus found in Treebank with semantic information

• Given the syntactic and semantic features, we can build a machine learning-based Information Extraction (IE) system, using shallow semantic parsing

• Advantage of using Treebank and Propbank is its re-use of an existing corpora to do ‘free’ information extraction in the medical domain

Treebank and Propbank

“Pierre Vinken, 61 years old, will join the board as a nonexecutive director Nov. 29.”

( (S (NP-SBJ (NP (NNP Pierre) (NNP Vinken) ) (, ,) (ADJP (NP (CD 61) (NNS years) ) (JJ old) ) (, ,) ) (VP (MD will) (VP (VB join) (NP (DT the) (NN board) ) (PP-CLR (IN as) (NP (DT a) (JJ nonexecutive) (NN director) )) (NP-TMP (NNP Nov.) (CD 29) ))) (. .) ))

Introduction: Treebank

\\treebank\parsed\mrg\wsj_0001.mrg

wsj/00/wsj_0001.mrg 0 8 gold join.01 vf--a

0:2-ARG0 7:0 ARGM-MOD 8:0-rel 9:1-ARG1 11:1-ARGM-PRD 15:1-ARGM-TMP

Verb ‘Join’

Location in Treebank

Argument 0

Argument 1 Argument M

Introduction: Propbank

Pierre Vinken, 61 years old, will join the board as a nonexecutive director Nov. 29.

Overall idea

SyntaxFrom Treebank

Overall idea

SyntaxFrom Treebank

Arg0- the eater

Arg1- the thing eaten

predicts

Predicate ArgumentsFrom Propbank

Problem:

• WSJ corpus = business domain

• In order to use WSJ, we have to make sure that the predicate distribution is “representative” for medical sentences.

• We found that 99 out of top 100 predicates in medical abstracts can be found in the WSJ corpus.

Results: Verb Frequency

10 most frequently found verbs in medical abstracts

# Occurrences Verb Cumulative frequency

1 1238 reduce 0.036

2 1163 improve 0.070

3 1056 suggest 0.100

4 963 increase 0.129

5 888 use 0.155

6 808 associate 0.178

7 742 compare 0.200

8 733 show 0.221

9 718 provide 0.242

10 593 appear 0.260

Methods: ML Training set and Intra-Domain Testing Set

WSJExtract

sentences with top 5 verbs

15,424 words

Training Set12,500 words

Test Set 2,924 words

Methods: ML Training & Testing (Intra-domain)

ML Training

ML Testing

WSJ Training

Set

SVMTorch*

* http://www.idiap.ch/machine_learning.php?content=Torch/en_SVMTorch.txt

Extraction of syntactic

features from Treebank and

semantic categories

from Propbank

Extraction of syntactic features

WSJTesting Set

Build classifier for semantic categories

Predict semantic categories

Pierre Vinken, 61 years old,

will join [the board]_Arg1 as

a nonexecutive director

Nov. 29.

Syntactic Features

S

NP VP

The Article discussed SBAR

that S

NP VP

Zocor reduced NP

cholesterol PP

in NP

the intervention group

Null

Argument 0Verb

Argument 1

Syntactic Features

• Predicate of the sentences

• Syntactic path from a word to the sentence predicate – For the word Zocor, the paths are NPSVPVBD and

SVPVBD

• Phrase Type

– The syntactic category of the constituent

– NP and S for Zocor


Syntactic Features

• Position of the word relative to the predicate

• Head Word POS• The POS tag of the syntactic head of the constituent

• Sub-categorization• Phrase structure expanding the predicate’s parent node in

the parse tree.

• VPVBD-NP for the predicate reduced

Results: Intra-domain performance

Argument Recall Precision F n

NULL 0.84 0.86 0.86 1574

0 0.55 0.48 0.52 236

1 0.85 0.76 0.81 936

2 0.93 0.45 0.61 152

3 0.00 0.00 N/A 9

4 0.78 0.64 0.71 17

Weighted Avg. 0.82 0.77 0.80

Results: Comparison with Prior Work *(Intra-domain)

*Table 1: Performance on WSJ test set

Arg Precision Recall F

ID (null) 0.86 0.84 0.86

ID + Class 0.77 0.82 0.80


Methods: ML Cross-Domain Testing Set

MedlineAbstracts

Test set (6373 Words)

250 Sentences with

5 target verbs

Manual annotated by

2 Medical Experts

Hand annotated

test set

Methods: ML Testing (cross-domain)

SVMTorch

Extraction of syntactic features

ML Training

ML Testing

RCT Abstracts

Propbank(WSJ)

Extraction of syntactic and

semantic categories

WSJTraining set

Medical Abstracts

Testing set

Predict semantic categories

Results: Cross-domain performance

Arg Recall Precision F n

NULL 0.81 0.70 0.75 3351

0 0.72 0.33 0.45 745

1 0.67 0.86 0.75 1952

2 0.60 0.24 0.34 325

3 0

4 0

Weighted Avg. 0.75 0.68 0.71

Results: Comparison with prior work*(cross-domain)

Table 15*: Performance on the AQUAINT test set.

AQUAINT: collection of text from the NY Times Inc., AP Inc., and Xinhua News Service

Arg Precision Recall F

ID (null) 0.70 0.81 0.75

ID + Class 0.68 0.75 0.71

Discussion

• Our ML classifier for null arguments– Intra-domain F = 86%, and cross-domain F = 75%, difference = 11%

• Pradhan and Jurafsky article for null arguments– Intra-domain F = 92%, and cross-domain F = 81%, difference = 11%

• Reuse of Propbank and Treebank information to automatically annotate medical abstract by using SSPT and ML classifier is feasible

Discussion - Limitations

• Limitation– The results are based on a small medical testing set

• Future directions– Improve the performance by addition of:

• Verb sense feature found in Propbank was not used• Lack of lexical features• Verb Clustering• Temporal cue words

– Test the performance using much larger medical abstract test set

Summary

• Literature is an important resource for biomedical knowledge

• Text mining = framework for accessing the free text in the literature, and transforming it to structured data

• Machine Learning = essential element in the text mining process

Appendix: Sentence Predicate Extraction

• Perl module Lingua::EN::Sentence -> Identified sentences

• Charniak parser1 -> Identified Parts of Speech– Based on WSJ corpus

• Extracted terminals with VB* POS tags

• Program morpha2 -> Normalization of verbs

1. Charniak, E., A Maximum-Entropy-Inspired Parser. 1999, Brown University.2. Minning, G., J. Carroll, and P. D., Applied morphological processing of English.

Natural Language Engineering, 2001. 7(3): p. 207-223.

text mining in biomedicine michael krauthammer department of pathology yale university school of...

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