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David Hanauer

October 22, 2013

Information Retrieval and Natural Language Processing

Disclosure

I have no conflicts of interest and no disclosures to report

What is NLP?

The process of converting unstructured language into a computable, structured form so that a deep understanding of the meaning

embedded in the text can be extracted.

What is NLP?

NLP is more than just identifying words and phrases. It deals (or should deal) with

ambiguity, negation, co-references, etc.

• NLP initially involves breaking down text into sentences, phrases, parts of speech, and actual words. Even this can be complex!

Dr. Jones wanted to take a ride along Rodeo Dr. Jones was 12.5 miles away. He was driving to the rodeo and drinking an orange Jones soda and looking at the orange sunset.

NLP Initial steps

• Syntax (structure) and Semantics (meaning) are both important

Time flies like an arrow

Language can be complex

Mead C, 2006

• Syntax (structure) and Semantics (meaning) are both important

Time flies like an arrowFruit flies like a banana

Language can be complex

Mead C, 2006

• Syntax (structure) and Semantics (meaning) are both important

Time flies like an arrowFruit flies like a banana

Language can be complex

Mead C, 2006

• “The next United States presidential election is to be held on Tuesday, November 6, 2012.”

NLP Initial steps

http://tomato.banatao.berkeley.edu:8080/parser/parser.html

Mapping to concepts is often required

Processing 00000000.tx.1: lung cancer.Phrase: "lung cancer." Meta Candidates (8): 1000 Lung Cancer (Malignant neoplasm 1000 Lung Cancer (Carcinoma of lung) [Neoplastic Process] 861 Cancer (Malignant Neoplasms) [Neoplastic Process] 861 Lung [Body Part, Organ, or Organ Component 861 Cancer (Cancer Genus) [Invertebrate] 861 Lung (Entire lung) [Body Part, Organ, or Organ Component] 861 Cancer (Specialty Type - cancer) [Biomedical Occupation or Discipline] 768 Pneumonia [Disease or Syndrome]

Meta Mapping (1000): 1000 Lung Cancer (Carcinoma of lung) [Neoplastic Process]Meta Mapping (1000): 1000 Lung Cancer (Malignant neoplasm of lung) [Neoplastic Process]

Machine learning is often used

• Hand annotated examples used to “train” a system so it can “learn” from the examples.

• Patterns can then be detected in new examples and a probability of meaning can be assigned.

• Involves discerning between various possibilities.

Machine learning

http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=MachineLearning&doc=exercises/ex8/ex8.html

Machine learning

Machine learning

Machine learning

Machine learning

• Natural language does follow some ‘rules’ but it can be very free form.

• There are many ways to express the same or similar concepts

Why is this so complex?

• “Forrest illustrated the difficulties with respect to standards by citing a one-day record from Children’s Hospital in Philadelphia in which clinicians entering data into electronic medical records (EMRs) used 278 ways to describe fever for 465 patients, 123 different ways to express ear pain in 213 patients, and 99 different ways to describe red ears.”

Why is this so complex?

Rubinsetein YR, Contemp Clin Trials. 2010 September ; 31(5): 394–404

…and this creates problems

Many clinical notes are dictated…

• What was dictated:

– “He has no nasal flaring”

• What was transcribed:

– “He has no nasopharynx”

Transcription Errors

• What was dictated:

– “given a prescription for an albuterol MDI”

• What was transcribed:

– “given a prescription for an albumin MDI”

Transcription Errors

• What was dictated:

– “has had a runny nose since September”

• What was transcribed:

– “has had a funny nose since September"

Transcription Errors

Runny Nose Funny Nose

Transcription Errors

• What was dictated:

– ????????

• What was transcribed:

– “Prior to discharge from the emergency department she was offered some Motrin, however she deferred atrial tachycardia.”

Transcription Errors

• ibuprofen– ibuprofin– Ibuprophen– ibuprophin

Medical terms are difficult to spell

• diarrhea– diarrheae– dirreahea– diarheea– diahhrea– diahrrea– diarhhea– diarreah– diarehha– diarrea– diahrea– diarhea

Medical terms are difficult to spell

14 yo here with dad.

2 days frontal HA and fever. No ST, No cough, No RN, mild abd pain,

sl dizzy today. Perfectly well when he has has motrin.

No illnesses in years

T 98.2

Wt 112

TM perfect. Throat nl Chest clear. Turbs pink with exudate.

Imp URI. Course reviewed

• Limited or no structure• Multiple abbreviations, many non-standard

Clinical notes aren’t always “natural” language

14 yo here with dad.

2 days frontal HA and fever. No ST, No cough, No RN, mild abd pain,

sl dizzy today. Perfectly well when he has has motrin.

No illnesses in years

T 98.2

Wt 112

TM perfect. Throat nl Chest clear. Turbs pink with exudate.

Imp URI. Course reviewed

Non-standard abbreviation

Missing punctuation

Missing units (pounds or kg?)

wheezing, coughing, running nose

Mortin - 10:30 AM

pain in troat had toni. removed

• Brief intake note from medical assistant

Clinical notes aren’t always “natural” language

wheezing, coughing, running nose

Mortin - 10:30 AM

pain in troat had toni. removed

Spelling error

This says, “pain in throat, had tonsils removed.”

Clinical notes aren’t always “natural” language

The patient is seen today in follow-up for: 1. Status-post aortic valve repair. 2. Status-post single vessel coronary artery bypass. 3. Hypertension. 4. Hyperlipidemia. 5. Atrial fibrillation.The patient underwent mitral valve repair surgery and one vessel bypass six weeks ago. He is doing well. He is active. He has no dyspnea or angina or palpitations. He saw his cardiologist last week and everything is going well. He does note some ongoing fatigue. He also has had difficulty maintaining his weight.

• Discordant information is often present

Free text notes are complex

The Maternal Past Medical and Pregnancy History: Healthy prior to the pregnancy. Course was complicated uncomplicated. Normal fetalanatomic survey. Medications during the pregnancyincluded PNV.

• Discordant information is often present

Free text notes are complex

He is approved to play Sports without restrictions,but no form filled out.  School form was filled out.

• Discordant information is often present

Free text notes are complex

History of Present Illness: The patient is an 86-year-oldAfrican- American female with severe Alzheimer's disease,and a history of multiple falls, who presents to the Emergency Department after a witnessed fall from her bed, and thus she injured her left wrist....Physical Exam: General: Elderly white female appearing somewhat confused, in no apparent distress. Temperature 97.9. Heart rate 77. Respiratory rate 18. Blood pressure 106/53.

• Discordant information is often present

Free text notes are complex

1997She has no history of hypertension or hyperlipidemia.

1998Cardiac risk factors include a history of tobacco abuse, having quit in October, as well as a history of hypertensionand hypercholesterolemia…

2002She denies hypertension, diabetes, or hypercholesterolemia.

2004Weight was 191.8, blood pressure with the M.A. was 142/98and I got 128/84 when I rechecked it, and pulse was 62.

• Discordant information can exist over time as well

Free text notes are complex

Family Medicine Doctor (June 10, 2007):Social History: She has never smoked, gets occasionalexercise, and drinks occasional alcohol. No history ofstreet drug use.

Emergency Medicine Doctor (June 14, 2007):SOCIAL HISTORY: The patient occasionally smokes.Denies any alcohol or drug use.

• Discordant information can exist over time as well (even just a few days)

Free text notes are complex

DOCTOR A (Day 1):ASSESSMENT: <Name> is a three week old newborn with ahistory of hyperbilirubinemia, who presents today after anapparent life threatening event.

DOCTOR B (Day 3):Assessment: <Name> is a three week old newborn with ahistory of hyperbilirubinemia, who presents today after anapparent life threatening event.

DOCTOR B (Day 4):Assessment: <Name> is a three week old newborn with ahistory of hyperbilirubinemia, who presents today after anapparent life threatening event.

Copy-paste is also a problem

DOCTOR C (Day 5):Assessment: <Name> is a three-week old newborn with ahistory of hyperbilirubinemia, who presents today after anapparent life threatening event characterized by cyanosis,extremity and hand stiffening, apnea, and unresponsivenessfor 45 seconds-1 minute.

Doctor C (Day 6):ADMISSION HISTORY: <Name> is a three week-old newborn witha history of hyperbilirubinemia, who presents today fromclinic after her mother witnessed an episode of cyanosisand apnea yesterday evening. <Name>'s mother explains that<Name> fell asleep after feeding at 5:30PM yesterday.

Copy-paste is also a problem

“This project determined the prevalence of inaccurate clinical documentation resulting from the use of computer-based documentation systems that allow carry-forward of prior information. The study found a failure rate of 8% in a random sample of all electronic notes and a failure rate of 26% in a random sample of notes generated by reusing prior clinical notes.”

Inaccurate Documentation

• “melanoma; however, the findings are subthreshold”

• “I favor atypical combined nevus rather than melanoma”

• “Unequivocal findings of melanoma are not identified.”

Ambiguous wording

Hedge phrases – Probabilities for “Probable”

Hedge phrases

Hedge phrases

Hedge phrases

• Even determining if something is a hedge phrase can be challenging…

• He was here in May.• He may have melanoma.• He may call back at any time with questions.

Hedge phrases

Q: Should hedge phrases be taken into consideration with NLP or IR tasks?

Hedge phrases

Q: Should hedge phrases be taken into consideration with NLP or IR tasks?

A: Probably

Hedge phrases

Language can be ambiguous

Language can be ambiguous

moped

Language can be ambiguous

moped

Language can be ambiguous

moped

Language can be ambiguous

moped mopped

Image removed - copyright

• Carcinoma, Cancer, Ca, Tumor, Neoplasm

• Zithromax, Z-pax, Azithromycin, Zmax

• White Count, Leukocyte Count, WBC

Multiple Synonyms

• CA– Calcium– Cancer– California

• MI– Myocardial Infarction– Michigan

• T1– TNM cancer staging– Bone of thoracic spine– MRI weighting

Ambiguous Abbreviations

• Did all of the patients in clinic with ALL arrive on time?

• The renal patient with a high BUN can eat the bun but not the burger.

• He had FROM of his hips bilaterally and could walk from the chair to the door.

Case-sensitivity

AOR

INIT

HE IS

DOT GASALL

PAT BUS

ANDARE

TENPAN

SLAPCHOP

RICE TIPSPOEMS

PANDAS

Case-sensitivity

artery

Operating room

inches

intrathecal

HeliumIncentive spirometry

directly obeserved therapy

group A strep

acute lymphoblastic leukemia

paroxysmal atrial tachycardia

buspirone

Axillary node dissection

active resistance exercise

toxic epidermal necrolysis

polyarteritis nodosa

serum leucine aminopeptidasecyclophosphamide, hydroxydoxorubicin, Oncovin, prednisone

rest, ice, compression, elevation

transvenous Intrahepatic Portosystemic Shunt

polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy and skin

pediatric autoimmune neuropsychiatric disorders associated with Streptococcus

Case-sensitivity

• DID ALL TEN PANDAS EAT TUNA FISH AND RICE?

– Dissociative identity disorder– Acute lymphoblastic leukemia– Toxic epidermal necrolysis– Pediatric autoimmune neuropsychiatric disorder associated with streptococcus– Ectopic atrial tachycardia– Transurethral needle ablation– Fluorescence in-situ hybridization– Axillary node dissection– Rest, Ice, Compression, Elevation

Case-sensitivity

According to her EHR she went to the OR in OR or MI for an AND of her ER pos and HER 2 pos breast ca and to the ER in CA for her pos MI.

Case-sensitivity

ACCORDING TO HER EHR SHE WENT TO THE OR IN OR OR MI FOR AN AND OF HER ER POS AND HER 2 POS BREAST CA AND TO THE ER IN CA FOR HER POS MI.

Case-sensitivity

According to her EHR she went to the OR in OR or MI for an AND of her ER pos and HER 2 pos breast ca and to the ER in CA for her pos MI.

Case-sensitivityOperatingRoom

Auxillary NodeDissection

Oregon Michigan

possible cancer EmergencyRoom

California

Electronic Health Record

positive

MyocardialInfarction

positiveEstrogen receptor

humanepidermalGrowthfactorreceptor 2

positive

Why do we need de-id?

– Reduce risk

– Increase HIPAA compliance

– A lot of people look at medical records who are not clinicians and don’t need to know who the patient is

– Maintain trust of our patients

De-Identification (NLP example)

18 HIPAA identifiers

• names• geographic subdivisions smaller

than a state• dates (with exceptions)• telephone numbers• FAX numbers• electronic mail addresses• Social Security numbers• medical record numbers• health plan beneficiary numbers• account numbers

• certificate/license #s• vehicle identifiers , license plates,

etc.• device identifiers and serial

numbers• web URLs• IP addresses• Finger prints, voice prints• full face photos and comparable

images• any unique identifying number,

characteristic or code

John Doe is a 2 year-old boy diagnosed with a malignant diffuse intrinsic pontine glioma in February 2004 who has now completed radiation therapy. Friends for Michael, a foundation that the family independently discovered, contacted me on Friday (09/05/05) to verify patient's diagnosis.

Free text de-identification

John Doe is a 2 year-old boy diagnosed with a malignant diffuse intrinsic pontine glioma in February 2004 who has now completed radiation therapy. Friends for Michael, a foundation that the family independently discovered, contacted me on Friday (09/05/05) to verify patient's diagnosis.

Free text de-identification

His final path showed a 12.5 x 7 x 7cm squamous cell carcinoma with 4/23 lymph nodes involved. Bronchoscopy culture from 4/19 numerous Strep. Milleri and H. parainfluenzae. In addition, respiratory culture from 4/23 grew pansensitive pseudomonas aeurginosa.

Free text de-identification

His final path showed a 12.5 x 7 x 7cm squamous cell carcinoma with 4/23 lymph nodes involved. Bronchoscopy culture from 4/19 numerous Strep. Milleri and H. parainfluenzae. In addition, respiratory culture from 4/23 grew pansensitive pseudomonas aeurginosa.

Free text de-identification

Statistical model built using “features” of the text thought to be important to discriminate protected health information (PHI) from non-PHI.

How modern de-id systems work

1. Morphological– Capitalization– Neighboring words– Punctuation

2. Syntactic– Parts of speech

3. Semantic– Dictionary terms (names, cities, hospitals)

Common features used in de-id

MITRE Identification Scrubber Toolkit (MIST)

MITRE Identification Scrubber Toolkit (MIST)

Brief live demo (if possible)…

What else are people doing with NLP?

• Natural Language Processing to Identify Venous Thromboembolic Events (Reichley RM 2007)

• Automated Identification of Postoperative Complications Within an Electronic Medical Record Using Natural Language Processing (Murff HJ 2011)

• Automatic Identification of Critical Follow-Up Recommendation Sentences in Radiology Reports (Yetisgen-Yildiz M 2011)

• Mayo Clinic NLP System for patient smoking status identification (Savova GK 2008)

Will NLP still be important in 10 years?

Maybe we should just code everything initially…

Will NLP still be important in 10 years?

“[T]here has been an emphasis on deploying computer-based documentation systems that prioritize direct structured documentation. Research has demonstrated that healthcare providers value different factors when writing clinical notes, such as narrative expressivity, amenability to the existing workflow, and usability.”

NLP is difficult to implement

Wendy Chapman iDASH talk, May 20, 2011

EMERSE

Electronic Medical Record Search Engine

• Not NLP (in the modern sense), but usable by non-technical people

• Let’s humans do the “processing”, based on search engine technology to help users find what they need

• Low barrier for adoption

EMERSE

Brief live demo (if possible)…

• “Banana” by nicubunu is in the Public Domain. • “Wings” by dear_theophilus is in the Public Domain. • “Ill” by William Brawley is under a Creative Commons license CC BY 2.0.

https://www.flickr.com/photos/williambrawley/4195919691• “Clown chili peppers” by Rick Dikeman is under a Creative Commons license CC BY-SA 3.0.

https://commons.wikimedia.org/wiki/File:Clown_chili_peppers.jpg• “Blue scooter” by pearish is in the Public Domain. • “Frown” by Arvin61r58 is in the Public Domain. • “Moe Biggie” by Pete Simon is under a Creative Commons license CC BY 2.0.• “Panda” by Machovka is in the Public Domain. • “Tekka maki sushi” by johnny_automatic is in the Public Domain. • “My Wife and My Mother-in-Law” by William Ely Hill is in the Public Domain.

Image Attributions