An(Introduc=on(to(( Clinical(Natural(( … Ques&ons!addressed!in!this!½!day!tutorial!!! (((•...

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Leonard D’Avolio Dina Demner-‐Fushman Wendy W. Chapman

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An Introduc=on to Clinical Natural

Language Processing

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Ques&ons addressed in this ½ day tutorial

•  What is natural language processing (NLP)?

•  Why does it maDer?

•  How is it being used? •  What are the basic approaches to it?

•  What considera=ons are there in using it?

•  How should you evaluate it? •  Where is the field today?

•  Where is it headed?

•  How can I learn more? 2

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Format

•  Focus on clinical NLP •  Some discussion of literature & phenotyping

•  70% basic, 30% intermediate

•  A lot of material covered at a high level

•  PLEASE interrupt with ques=ons •  Planned 15 minute break

•  Don’t forget your survey •  Part 2 in Jefferson East

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Outline

1.  What is NLP and how is it used in medicine? (Dina)

2.  Goals and challenges of clinical NLP (Wendy)

3.  The methods of NLP (Leonard)

4.  Annota=on & evalua=on (Dina) 5.  Implementa=on considera=ons (Wendy)

6.  Current state, future progress, available resources (Leonard)

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Dina Demner-‐ Fushman

What is NLP and how is it being used

in medicine?

Why natural language processing?

•  Increasing amounts of biomedical literature

–  Extrac=ng facts, rela=ons, events into knowledge repositories (text mining)

–  Model organism database cura=on

–  Ques=on answering (TREC Genomics track)

–  Literature based discovery

•  Increasing demands for use of EMR data –  Phenotyping for genomic-‐

related analysis –  Linking evidence for

Evidence-‐based medicine –  Biosurveillance –  Quality measures

•  Majority of EMR data is free text

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•  Classify

•  Extract

•  Summarize

What is natural language processing? Electronic Medical Records

MEDLINE Ar=cles / Abstracts

Natural Language Processing

Structured Data (Machine

interpretable)

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Examples of Uses of Clinical NLP

•  Classify

•  Extract

•  Summarize

BioNLP Examples •  Classify

•  Extract

•  Summarize

Classify a chief complaint into a syndrome category

“SOB/cough” = Respiratory

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Triage of ar=cles likely to have experimental evidence

Find evidence to assign top-‐level GO terms

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Examples of Uses of Clinical NLP

# of lymph nodes removed during colorectal cancer surgery

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Extract bio-‐molecular events

phosphoryla=on of TRAF2 -‐> (Type:Phosphoryla=on,

Theme:TRAF2)

•  Classify

•  Extract

•  Summarize

•  Classify

•  Extract

•  Summarize

BioNLP Examples

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Examples of Uses of Clinical NLP •  Classify

•  Extract

•  Summarize

From a H&P note, list chronic condi=on

Summarize family history of prostate cancer

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BioNLP Examples •  Classify

•  Extract

•  Summarize

Summarize full text documents

Gene Reference into func=on (GeneRif)

Biomedical Usage

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Wendy Chapman

Goals and Challenges of Clinical NLP

Detect Nosocomial Infec=ons

An=bio=c Assistant* (LDS Hospital)

* Evans RS, et al. N Eng J Med 1998

temperature

white blood cell count

infiltrate compa=ble with pneumonia

. . .

1) Alert physician: pa=ent might need An=infec=ve therapy

2) Suggest type and dose of an=bio=c -‐ allergies -‐  insurance -‐  age -‐  renal func=on

infiltrate compa=ble with pneumonia chest x-‐ray report

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Phenotyping Iden=fy symptoms that co-‐occur with lung cancer

ED Report

NLP System

Feature 1: Feature 2:

… Feature n:

Classifier

Predic=ve of Lung Cancer

Not

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Two Simple NLP Tasks

1.  Find all relevant phrases in ED Report

2.  Map individual phrases to standard features

Your Task

•  Highlight every instance of features in sample report

•  Mark most specific instance –  E.g., “chest pain” preferred over “pain”

•  Do not mark =me, nega=on, or uncertainty

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Find Relevant Features in ED Report

Produc=ve cough

Dyspnea

Sinusi=s

Pneumonia

Wheezing

Tachypnea

Fever

Rales

Cervical adenopathy

Possible values: acute, historical, absent 16

How did you do?

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Why is NLP Difficult?

Named en=ty recogni=on

Linguis=c varia=on Polysemy

Finding valida=on Implica=on

Contextual aDribute assignment

Nega=on Uncertainty Temporality

Discourse processing

Report structure Coreference

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Linguis=c Varia=on Different Words with the Same Meaning

Deriva=on medias=nal = medias=num

Inflec=on opacity = opaci=es; cough = coughed

Synonymy Addison’s Disease: Addison melanoderma, adrenal insufficiency, adrenocor=cal insufficiency, asthenia pigemntosa, bronzed disease, melasma addisonii, …

Chest wall tenderness: chest wall did demonstrate some slight tenderness when the pa=ent had pressure applied to the right side of the thoracic cage 19

Polysemy One Word With Mul=ple Meanings

General polysemy Pa=ent was prescribed codeine upon discharge The discharge was yellow and purulent

Acronyms and Abbrevia=ons APC: ac=vated protein c, adenomatosis polyposis coli, adenomatous polyposis coli, an=gen presen=ng cell, aerobic plate count, advanced pancrea=c cancer, age period cohort, alfalfa protein concentrated, allophycocyanin, anaphase promo=ng complex, anoxic precondi=oning, anterior piriform cortex, an=body producing cells, atrial premature complex, …

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Nega=on Approximately half of all clinical concepts in dictated

reports are negated*

Explicit nega=on “The medias=num is not widened”

Medias=nal widening: absent

Implied absence without nega=on “Lungs are clear upon ausculta=on”

Rales/crackles: absent Rhonchi: absent Wheezing: absent

*Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG. Evalua=on of nega=on phrases in narra=ve clinical reports. Proc AMIA Sym. 2001:105-‐9.

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Uncertainty

Unsure

treated for a presump=ve sinusi=s

Reasoning

It was felt that the pa=ent probably had a cerebrovascular accident involving the lev side of the brain. Other differen=als entertained were perhaps seizure and the pa=ent being post-‐ictal when he was found, although this considera=on is less likely

Reason for exam

R/O out pneumonia. 22

Temporality Clinical reports tell a story

Past medical history History of CHF presen=ng with shortness of lev-‐sided chest pain.

Hypothe=cal or non-‐specific men=ons He should return for fever or increased shortness of breath.

Temporal course of disease Pa=ent presents with chest pain … Aver administra=on of nitroglycerin, the chest pain resolved.

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Finding Valida=on Men=on of a finding in the text does not guarantee the pa=ent has the finding

She received her influenza vaccine His temperature was taken in the ED

Some findings require values Fever

Temperature 38.5C Oxygen desatura=on

Oxygen satura=on low Oxygen satura=on 85% on room air

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Implica=on

Audience for pa=ent reports is physicians Lay people less accurate at determining if a chest x-‐ray report shows evidence of Pneumonia Pneumonia not men=oned in 2/3 of posi=ve reports

Sentence level inference “There were hazy opaci=es in the lower lobes” à

Localized infiltrate Report level inference

Localized infiltrates à Probable pneumonia

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Report Structure

Anatomic Loca=on some=mes in sec=on header NECK: no adenopathy.

Some sec=ons carry more weight IMPRESSION: atelectasis

Some reports contain pasted text difficult to process

Cardiovascular: [ ] Angina [ ] MI [x ] HTN [ ] CHF [ ] PVD [ ] DVT [ ] Arrhythmias [ ] Previous PTCA [ ] Previous Cardiac Surgery [ ] Nega=ve -‐ Denies CV problems

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Coreference

Chest x-‐ray again shows a well-‐circumscribed nodule located in the lev upper lobe. The tumor has increased in size since the last exam with a diameter of approximately 2 cm. How big is the nodule? Has the nodule increased in size? Where is the tumor?

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References "Mutalik PG, Deshpande A, Nadkarni PM. Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS. J Am Med Inform Assoc. 2001 Nov-Dec;8(6):598-609."

"Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform. 2001 Oct;34(5):301-10."

"Uzuner O, Zhang X, Sibanda T. Machine learning and rule-based approaches to assertion classification. J Am Med Inform Assoc. 2009 Jan-Feb;16(1):109-15."

"Sneiderman CA, Rindflesch TC, Aronson AR. Finding the findings: identification of findings in medical literature using restricted natural language processing. Proc AMIA Annu Fall Symp. 1996:239-43"

"Fiszman M, Chapman WW, Aronsky D, Evans RS, Haug PJ. Automatic detection of acute bacterial pneumonia from chest X-ray reports. J Am Med Inform Assoc. 2000 Nov-Dec;7(6):593-604."

"Zhou L, Melton GB, Parsons S, Hripcsak G. A temporal constraint structure for extracting temporal information from clinical narrative. J Biomed Inform. 2005 Sep 15."

"Harkema H, Dowling JN, Thornblade T, Chapman WW. ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports. J Biomed Inform. 2009 Oct;42(5):839-51."

"

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Leonard D’Avolio

The Methods of Clinical NLP

how this stuff works

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Developing / using NLP is a process The NLP Process

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Find the right documents

The NLP Process

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Create the “gold standard”

The NLP Process

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Train the system The NLP Process

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Evaluate the system

The NLP Process

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Methods of NLP

•  A number of approaches have evolved

•  Simple rules-‐based

•  Symbolic, gramma=cal NLP

•  Machine learning

•  NLP can be considered a series of transforms

Think “PIPELINE”

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Research Scenario Posi=ve margins aver RRP = 2 x 4 =mes risk of cancer recurrence

Goal: EXTRACT MARGIN STATUS

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Simple Rules-‐Based Approach

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Heuris=cs, Probabili=es, Combina=on of the two

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Pros

Simple

Regular expressions included in many programming languages

Great for semi-‐structured (consistently formaDed) targets

Cons

PaDerns must consider all possible configura=ons.

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Symbolic or Gramma=cal NLP Approach

Many of the same components…

…plus…

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POS tagging & phrase chunking are ac=ve areas of research

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Some=mes called “concept mapping”


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Pros

Robust – reduces complexity by mapping to standard terms

Great for mapping large numbers of concepts

Cons

Complex – more steps, more opportuni=es to introduce error

Which controlled vocabulary?

Can be slow 48


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Classifica=on Model

Several open source ML packages available

(decision trees, SVMs, neural nets)

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Machine Learning Approach

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Machine Learning: Which ‘features’ to learn from?

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Pros

Targeted approach = high accuracy

Capable of learning from examples

Great for extrac=ng few predetermined targets

Cons

Requires manual training

New target = new training effort

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Also used increasingly in POS tagging & mapping to ontologies

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Machine Learning Approach Not limited to a step in the pipeline

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What if RegExs don’t cut it?

Swap them out for Gramma=cal NLP approach

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The Hybrid Approach

References

Natural language processing: Manning & Schutze. Founda=ons of Natural Language Processing. MIT Press. 1999

Regular Expressions: Java Tutorial, hDp://java.sun.com/docs/books/tutorial/essen=al/regex/

Machine learning: WiDen & Frank. Data Mining, Prac=cal Machine Learning Tools and Techniques with Java Implementa=ons. Academic Press. 2001

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Dina Demner-‐Fushman

Annota=on and Evalua=on

Manual Annota=on

•  Purposes •  Levels •  Guidelines •  Methods (manual, assisted) •  Tools •  Format (embedded/standoff) •  Collec=on size (number needed, representa=ve sample)

•  Annotators (linguists, domain experts, crowdsourcing)

•  Annotator agreement •  Preserva=on/dissemina=on/repor=ng 57

Annota=on purposes

•  System development –  Rule genera=on (manual or automa=c) –  Sta=s=cal modeling – supervised machine learning (training + valida=on/op=miza=on)

•  Evalua=on –  Tes=ng on a held-‐out set or cross-‐valida=on

•  Clinical data quality assurance •  Reusable collec=on (corpus)

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Annota=on levels •  Meta –  informa=on about the corpus

•  Document –  type, relevancy to topic, quality, structure

•  Pragma=c –  purpose of a sentence interpreted in context using world knowledge, involves inference

•  Discourse –  contextual features, links between instances of concepts, or concepts across sentences

•  Seman=cs –  formal representa=on of meaning using concepts, frames

•  Syntax –  part of speech, phrases, rela=ons between phrases

•  Lexical 59

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Meta: XYZ hospital, respiratory problems, … Document: Pa=ent #13, Discharge summary # 1, …

Annota=on levels example

Annota=on guidelines

•  Define task and annota=on purpose •  Be clear •  Be concise •  Avoid bias •  Itera=vely refine using representa=ve sample

•  Come to consensus

•  Finalize before annota=ng reference standard

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Annota=on methods Trade-‐off of manual vs assisted annota=on

Bias

Accuracy / consistency

Speed-‐up

Training

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Annota=on tools

•  Read and write formaDed text (markup language)

•  Allow to define/ link annota=on schema

•  Provide for span selec=on & markup (color-‐coding)

•  Minimize annota=on steps and naviga=on

•  Link ontologies •  Compute inter-‐annotator agreement

•  Provide for reconcilia=on of annotator disagreement

•  Provide web-‐service/API 63

References Linguis=c annota=on: Wynne M (editor). 2005. Developing

Linguis4c Corpora: a Guide to Good Prac4ce. Oxford: Oxbow Books. Available from hDp://ahds.ac.uk/linguis=c-‐corpora/

Issues: Hovy E, Lavid J. Corpus annota=on tutorial. hDp://www.lrec-‐conf.org/lrec2008/IMG/pdf/Corpus_annota=on.Tutorial-‐outline.pdf

Clinical text AMIA NLP-‐SIG annota=on project Available from hDp://understandit.net/r02.01.11/index.php?=tle=Annota=onProjectAnnota=onSchema

Annotator agreement: Hripcsak G, Rothschild AS. Agreement, the f-‐measure, and reliability in informa=on retrieval. J Am Med Inform Assoc. 2005 May-‐Jun;12(3):296-‐8. hDp://www.ncbi.nlm.nih.gov/pmc/ar=cles/PMC1090460/

Overview Judges Metrics

Evalua=on methods Large-‐scale evalua=ons

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Dina Demner-‐Fushman

Evalua=ng NLP

Evalua=on roots Human/biomedical studies

Subjects Outcomes/Sta=s=cs

Sovware/NLP evalua=on Quality of the algorithm Quality of implementa=on Quality of results

Human-‐computer interac=on Usability tes=ng

Heuris=c User-‐centered Scenario-‐based

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What is evaluated?

Sovware System components Black/Glass-‐box (results/algorithm and implementa=on)

Task-‐specific (intrinsic/extrinsic) Manual/automa=c

Applica=on Interface (HCI)

Qualita=ve/quan=ta=ve Access (API, service)

Impact/Outcome Healthcare process Pa=ent’s experience

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Judges: who is evalua=ng?

•  Experts vs. convenience popula=on vs. end-‐users •  How many?

•  Consensus (reliability, agreement) vs. pyramid

•  Capturing judgments in reusable test collec=ons

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Evalua&on Metrics

Reference Standard

NLP output

posi&ve nega&ve

posi&ve a (TP) b (FP)

nega&ve c (FN) d (TN)

Recall (Sensi=vity) = a / (a + c) Precision (PPV) = a / (a + b) Fall-‐out (1-‐Specificity) = b / (b + d) = 1 -‐ d/(b+d)

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Evalua&on Metrics

F-‐measure: harmonic mean of precision and recall

What if enumera=ng all true posi=ve/nega=ve examples is not possible or prac=cal?

Mean average precision, binary preference 70

Reference Standard

NLP output

posi&ve nega&ve

posi&ve a (TP) b (FP)

nega&ve c (FN) d (TN)

Sovware evalua=on

•  Establish strong baseline –  For extrac=on of pa=ent-‐oriented outcomes from MEDLINE abstracts selec=ng

3 last sentences achieves 75% accuracy

•  Select evalua=on metrics appropriate for the task –  U=lity measure for text categoriza=on (Genomics track) –  “This measure contains coefficients for the u=lity of retrieving a relevant and

retrieving a nonrelevant document normalized by the best possible score” hDp://ir.ohsu.edu/genomics/2005protocol.html

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End-‐user evalua=on

Use Log files observa=on Surveys

Impact Cost / =me Outcomes

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Community-‐wide evalua=ons

Format Post-‐hoc ( TREC ) Gold standard provided Pros/cons cost vs. coverage

Clinical I2b2 cmc

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References Friedman CP, WyaD JC. Evalua=on Methods in Biomedical Informa=cs. 2nd

ed., New York: Springer, 2006.

Sparck-‐Jones K, Galliers JR. Evalua=ng Natural Language Processing Systems. Springer, 1996.

van Rijsbergen C.J. Informa=on Retrieval, 2nd ed. London: BuDerworths, 1979. hDp://www.dcs.gla.ac.uk/Keith/pdf/Chapter7.pdf

Hripcsak G, Wilcox A. Reference standards, judges, and comparison subjects: roles for experts in evalua=ng system performance. J Am Med Inform Assoc. 2002 Jan-‐Feb;9(1):1-‐15. Available online from hDp://www.ncbi.nlm.nih.gov/pmc/ar=cles/PMC349383

Passonneau RJ, Nenkova A. Evalua=ng Content Selec=on in Human-‐ or Machine-‐Generated Summaries: The Pyramid Scoring Method hDp://www1.cs.columbia.edu/~library/TR-‐repository/reports/reports-‐2003/cucs-‐025-‐03.pdf

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Wendy Chapman

Implementa=on Considera=ons

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Implemen&ng NLP

• Ge�ng an NLP system up and running •  Case study

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Preprocessing Post-‐processing NLP System

The devil is in the details

Remove extraneous characters control characters foreign characters (é)

Remove extra line feeds, etc.

pul-‐_monary

Preserve/enhance sec=on labels “IMPRESSION:_”

Reformat to improve readability

De-‐iden=fy


Obtain source feeds

Assess completeness

De-‐duplicate

Clean, “sec=onize,” format

De-‐iden=fy

Load database

Hand-‐off to NLP system

Quality assurance

Slide courtesy David Carrell 78


Obtain source feeds

Assess completeness

De-‐duplicate

Clean, “sec=onize,” format

De-‐iden=fy

Load database

Sample

Hand-‐off to NLP system

Quality assurance

Human Subjects/IRB

Source system manager

Network/database administrator

Programmer

Investigator

Informatics/NLP expert

Clinician (“domain expert”)

Chart abstractor

Slide courtesy David Carrell A lot of tasks and a lot of people 79

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Which CUIs map to Produc4ve Cough?

Which combina=on of radiological findings & aDributes = evidence of acute bacterial pneumonia?

Does the pa=ent have a recurrent breast cancer?

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Map NLP output to your vocabulary and

your task

Instance

Report

Pa=ent

Case Study Case-‐control observa=onal GWAS study

Hypothesis Biomarkers in pa=ents with prostate cancer can be used to predict =me to survival, informing course of care

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Targeted Phenotype

•  Prostate cancer •  Co-‐morbidi=es •  Basic demographics •  Disease characteris=cs

•  TNM Staging •  Gleason score •  PSA

•  Treatments administered •  Surgery •  Chemo •  Watchful wai=ng

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The NLP Process

Defining the target

Data challenges

Prostate cancer •  ICD-‐9 codes don’t cut it

•  VA Boston: 18% of path reports 60 days before / aver 1st ICD-‐9 were prostate cancer related

•  No standardized =tles on path reports •  Biopsy? •  Post-‐op?

Into NLP just to find the right documents Phenotyping with NLP is really several projects in one

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The NLP Process

Extrac=ng key variables

Annota=on challenges

TNM Staging Gleason score PSA

•  Gleason score different on post-‐op than biopsy •  Pathological vs. es=mate

•  Pa=ent level vs. document level •  PSA at 4 visits •  Conflic=ng Gleason scores

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•  Start with a wish list and whiDle down •  Cost vs. benefit will become clear

•  Define categorical variables •  Versus highligh=ng strings

•  Create clear instruc=ons •  Training •  Pilot, pilot, pilot

•  Plan for several itera=ons

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The NLP Process

Crea=ng your training / test sets

Designing your “gold standard”

•  Several variables = several measures of accuracy •  What if tumor staging F – measure is .97 but co-‐

morbidi=es is .6? •  Effects must be accounted for in study design

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The NLP Process

Extrac=ng key variables

NLP algorithm development

•  Are you reinven=ng the wheel?* •  Is it important that it scale

•  Other projects? •  Beyond your ins=tu=on?

*PraD, AW. 1969“Automated processing of medical English” Interna=onal Conference on Computa=onal Linguis=cs, Sweden

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Current state, future progress, available

resources

Leonard D’Avolio

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State of the Science

NLP is not “off the shelf” • Opportunity to reduce effort

Several approaches can yield similar performance

• i2b2 challenge First increase in open source components

• Weka, MMTx, Stanford parser

• Lots of ‘glue code’ Now increase in open source frameworks

• GATE, UIMA

End-‐to-‐end informa=on retrieval using open source frameworks

•  ARC 94

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Progression of Field—More Resources

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“Closed” Concept Mapping Systems •  MedLEE •  Knowledge Map •  MVCS

Open Components •  Stanford Parser •  IBM Parser •  OpenNLP •  Weka (ML) •  MALLET (ML) •  UMLS •  NegEx (nega=on)

Open Frameworks •  UIMA •  GATE

Open Concept Mapping Systems •  MetaMap •  HITEx (GATE) •  Topaz (GATE) •  cTAKES (UIMA) •  MedKAT (UIMA)

Open Corpora •  Cincinna= •  PiDsburgh NLP Repository •  i2b2 •  MIMIC 1 & 2

Open IR Systems •  ARC (UIMA + MALLET)

Tools Registries •  RDS •  ORBIT •  Eagle-‐I Hosted Environments •  iDASH •  VINCI

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Future of NLP

Informa=on quality – context is key •  Error propagates in pipelines •  Informa=on not captured for our secondary uses •  Scrap idealized test sets

Greater code reuse •  Less glue code •  Will allow focus on improving specific components

Increase in open source data sets & shared task challenges Drive adop=on of NLP

•  More data driving greater demand / new uses •  Reduce current dependency on system developers

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Current Process

D’Avolio et. al. “Evalua=on of a generalizable approach to informa=on retrieval using the Automated Retrieval Console.” 2011. 17(4)

What it should be

D’Avolio et. al. “Evalua=on of a generalizable approach to informa=on retrieval using the Automated Retrieval Console.” 2011. 17(4)

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Best approach to NLP?

VS.

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Best approach to NLP?

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Worst approach to NLP?

Resources WEKA: hDp://www.cs.waikato.ac.nz/ml/weka/

MALLET: hDp://mallet.cs.umass.edu/

MetaMap: hDp://mmtx.nlm.nih.gov/

UMLS: hDp://www.nlm.nih.gov/research/umls/

OpenNLP: hDp://opennlp.sourceforge.net/

HITEx (hosted by i2b2): hDps://www.i2b2.org/resrcs/hive.html

cTAKES: hDps://cabig-‐kc.nci.nih.gov/Vocab/KC/index.php/OHNLP_Documenta=on_and_Downloads

UIMA: hDp://incubator.apache.org/uima/

GATE: hDp://gate.ac.uk/

ARC: hDp://research.maveric.org/mig/arc.html

Resources (cont) Topaz: hDp://www.dbmi.piD.edu/blulab/resources.asp#Topaz

NegEx: hDp://code.google.com/p/negex/

ConText: hDp://www.dbmi.piD.edu/chapman/ConText.html

Cincinna= Pediatric Corpus: hDp://www.computa=onalmedicine.org/project/cpc.php

PiDsburgh NLP Repository: hDp://www.dbmi.piD.edu/blulab/nlprepository.html

MIT MIMIC Repository (structured and unstructured):

hDp://mimic.mit.edu/mimic-‐ii-‐database.html

ORBIT Project: hDp://orbit.nlm.nih.gov/

iDASH: hDp://iDash.ucsd.edu

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Contact informa&on

Leonard D’Avolio, PhD Associate Center Director for Biomedical

Informa=cs MAVERIC, VA Boston Healthcare System [email protected]

Wendy Chapman, PhD Division of Biomedical Informa=cs University of CA, San Diego [email protected]

Dina Demner-‐Fushman, MD, PhD Na=onal Library of Medicine [email protected]

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An(Introduc=on(to(( Clinical(Natural(( … Ques&ons!addressed!in!this!½!day!tutorial!!! (((•...

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