Biomedical Informatics and Clinical NLP in Translational

Science Research

Piet C. de Groen, M.D.

Overview - Examples

Patient-specific research – N=1 study Understanding a disease Finding the right MD, diagnosis and

treatment

Renal Transplant patientMay, 2005

Hepatobiliary Clinic Consultation Abnormal liver tests – using Lipitor™ Diarrhea and weight loss

Challenge Very complex medical history Nobody understands the case HUGE history with hundreds of notes

Patient January 16, 2006

Total weight of printed pages presented for review:5 lbs.

Patient January 16, 2006

Total number of X-rays presented for review:16,902

Questions

What is exactly the patient’s problem?– Are liver tests and weight loss due to Lipitor?– When did she use Lipitor?– What was the weight on what date?

Impossible to review all notes!– Which notes are relevant to current symptoms?– Which have notes have weights and drug

information?

What I need

I need to see trends over time– Weight– Lipitor use

– Effects of Lipitor on lipids and liver tests

But I cannot see trends over time– EMR does not have structured data for weight or

Lipitor use– EMR only allows for display of laboratory test results

in very large tables or simple graphs

Data Warehouse to the Rescue!

Demographics– MC # = xx-xxx-xxx

Clinical Notes– Patient Vitals

Weight exists

Result– 243 notes

43 had weight

Weight in kg

40

45

50

55

60

65

70

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Start DialysisTransplantNew Problem

What happened to Cholesterol?

She was on Lipitor, but:– When was it discontinued?– Did it do anything to her lipid levels?

NLP to the rescue!

Sort 33 identified Clinical Notes on date First note is from 1997

– Lipitor is highlighted in the note– …Dr. X recommended discontinuation of Pravachol

and initiation of Lipitor … have written a prescription for Lipitor …

Last note is from 2005– … Lipitor was discontinued in 2004 …– March 2004 note confirms discontinuation

Warehouse to the Rescue!

Demographics– MC # = xx-xxx-xxx

Tests– Cholesterol exists

Clinical Notes– “Lipitor”

Result– 22 cholesterol levels– 243 notes: 33 mentioned “Lipitor”

Cholesterol in mg/dL

0

50

100

150

200

250

300

350

1993 1995 1998 2001 2004 2006

Lipitor

Recommendations

72 hour stool fat on 100 gram fat diet– 689 gram, 23 gram fat/day (2-7 Normal)

EGD/EUS with biopsies and aspirate– Esophagitis - ? Candida – biopsy negative– Duodenal diverticula, normal pancreas– Duodenal biopsy normal– Aerobes > 100,000 Gram negative bacillus cfu/mL– Anaerobes > 10,000 Bacteroides Fragilis cfu/mL– Yeast 1,000-10,000 cfu/mL

Small Bowel X-ray– Numerous diverticula

Understanding a disease

Hepatocellular Cancer in Obesity

Spring 2006Based on simple queries of MCLSS

• For NASH the ICD-9 code 473.8 was used; this code may include other diagnoses, but the vast majority is NASH

• For Primary Liver Cancer the ICD-9 codes 155.0 and 155.1 were used

• For Obesity ICD-9 code 278.0 was used, or Diagnosis section Clinical Notes

• BMI was retrieved from Clinical Notes; maximum value during life time was used

Primary Liver CancerNASH Cases with BMI>30

0

5

10

15

20

25

30

35

40

1992 1994 1996 1998 2000 2002 2004 2006

Males

FemalesCases

Cancers with Increasing Incidence2012 report US: 1999 through 2008

CA: A Cancer Journal for CliniciansVolume 62, Issue 2, pages 118-128, 4 JAN 2012 DOI: 10.3322/caac.20141http://onlinelibrary.wiley.com/doi/10.3322/caac.20141/full#fig2

Finding the right MD, diagnosis and treatment

Interval Colorectal Cancer

Time LineExample of Interval Colorectal Cancer

Pathology

Endoscopy

Diagnoses

Time Line

1 2 3 4 5

Year

Benign Colon Colon Cancer Non-Colon Disease

< 3 years

Co

lon

Can

cer

1993

-200

6(Pathology data)

4,203,857 specimens

238,177 specimens

Part description = “COL/RECT” AND Valid MCN

19,259 specimens

13,477 specimens(10,136 patients)

(Endoscopy data)325,370 Procedures

2,692 patients

4,743 procedures (date, other features)

Missed Lesions (Anatomic location, tumor size, other

characteristics)

Diagnosis_code = One of 50 identified cancer diagnosis codes

Unique? (One specimen may have multiple diagnosis codes)

Patients with CC diagnosis and C procedure

Extract all C procedures, the date and other features

Compare the CC diagnosis and C dates

Remove Patients with Research Authorization = ‘No’

Co

lon

osc

op

y 19

92-

2004

MethodsPathology = Colorectal Cancer

Negative History

Year

1 32 4 5Truly Missed

No lesions at colonoscopy

Probably Missed 1 32 4 5

1 32 4 5Seen, removed

Lesions at colonoscopy

1 32 4 5Seen, not removed

Colorectal Cancer History

1 32 4 5Recurrent, 2nd, 3rd

cancer not prevented

Results Summary

• Truly missed case– 90 days to 3 years

• Probably missed case– 3 to 5 years

• A lesion was seen– removed <5 years– not removed <5 years

• Local recurrence or 2nd, 3rd cancer

82

95

8

>44

54 >283

©Ralph A. Clevenger

Tumor Growth Curves

Truly MissedProbably MissedSeen & RemovedRecurrent, 2nd, 3rd

Time Interval (days)

Tu

mo

r S

ize

(m

m)

t = 3 yrs

0 200 400 600 800 1000 1200 1400 1600 18000

5

10

20

30

40

50

60

70

80

90

100 3 Months Doubling Time

Number Not

Detected

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 460

5

10

15

20

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 460

50

100Number

Seen

Numbers for each Endoscopist

Truly MissedProbably MissedSeen & RemovedRecurrent, 2nd, 3rd

% Not Detected

Miss Rate for each Endoscopist

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 460

5

10

15

20

25 Truly MissedProbably MissedSeen & Removed

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 460

1

2

3

4

5

6

7

8

0

1

2

3

4

5

6

7

8

Detection of cancers in previously seen patients (self)

Detection of cancers in patients seen by colleagues (others)

Endoscopist

Overview - Examples

Patient-specific research – N=1 study Understanding a disease Finding the right MD, diagnosis and

treatment