Text Mining in Animal Health Surveillance

1. Text Mining in Animal Health Surveillance John Berezowski Clarissa Snyder Lindsay Mclarty Food Safety Division Alberta Agriculture Food And Rural Development

2. Text Mining In Public Health • Knowledge management • Classification of journal articles to manage and search of databases • Classification of hospital records to allow data mining of hospital databases to discover knowledge • Classification of medical records for real time surveillance • Free text emergency room chief complaints classified into syndromes eg GI or Influenza like

3. Purpose • Canada-Alberta BSE Surveillance Program: • CABSESP • Alberta Veterinarians participate in BSE surveillance • Submit cattle samples for BSE testing • Dead or euthanized • Examine cattle prior to sampling • Provide data about farmers and animals tested • Purpose: maximize information about cattle tested • Especially why cattle were: sick/dead/sampled • Assist CFIA to identify ‘Clinical Suspects”

4. Purpose • Large sample (July 04 - July 06) • 35,720 Alberta cattle tested by AAFRD • Another 25,000(+/-) tested by the CFIA • 9,117 farms • 141 veterinary clinics (293 veterinarians) • Purpose: evaluate utility of BSE submission form data for other surveillance purposes

5. Submission Form Data • Farmer ID, date, location, number on farm • Purebred (y/n), breed, age, sex, BCS, PM (y/n) • Diseased, Distressed, Down, Dead, Neuro • Clinical signs in free text format • Presumptive diagnosis in free text format

6. Example Submission • Clinical Signs: Cow was in dry lot. Went off feed, coughing and labored breathing • Presumptive Diagnosis: PM findings- traumatic pericarditis and abscess from hardware between reticulum and diaphragm • Need tools (Text Mining) to extract information from free text fields

7. Text Mining: Definition • Based on data mining definitions • Knowledge discovery in text • Semi or automated discovery of trends and patterns across large volumes of text • Computer applications that aim to aid in making sense of large volumes of text

8. Text Mining: Our Context • Classify cattle with respect to certain concepts: • Etiologies: Johne’s, AIP, hepatic lipidosis, LDA, IBR, unknown, etc. • Descriptors: acute, chronic, emaciated, lame, autolyzed, blind, ataxic, etc. • Clinical Presentation:Syndromes: respiratory, GI, repro etc • Use classifications to better describe the cattle sampled and look for associations or trends within the samples

9. Named Entity Recognition • Identify terms in text -Term = textual representation of a concept • Classify terms -Noun vs verb vs adjective,preposition, etc. -Etiology vs descriptors: animal (pregnant) vs clinical sign (chronic) • Map terms to concepts in an ontology -Associate each term with one or more concepts Bleeding Concept of hemorrhage Bled Hemorrhage

10. Problems With Our Data • No suitable ontology • What’s an ontology? • A model that links concept labels to their textual representations and defines or describes the relationships between concepts • Machine readable descriptions of concepts and their relationships • Examples: Dictionaries, SNOMED-SNOVET

11. Problems With Our Data • Terms are formal (vet/med) + unusual “Nephritis”, “peritonitis”, “cancer eye”, “lump jaw”, “corkscrew claw”, ‘downer”, “fatty liver”, “hardware”, “found dead” • Specific to food animal practitioners.

12. Problems With Our Data • Term Variation • A single concept is expressed in a number of different ways (synonyms) • Probability of two experts using the same term to refer to the same concept is lessthan 20%1 • Arthritis: arthritis, arthritic, osteoarthritis, polyarthritis, septic-arthritis • 1Grefenstette G. 1994

13. Problems With Our Data • Term Ambiguity • The same term is used to refer to multiple concepts • Multiple meanings for the same term • Boated= nutritional (feedlot, pasture), or bloated abdomen (perforated ulcer) • Prolapse = vagina, uterus, rectum, vaginal fat, intestinal

14. Problems With Our Data • No sentence structure • “Old age, arthritis, no teeth” • “Stifle, bilateral, degenerative, arthritis” • ‘Pelvic injury, post calving, crippled “ • “Down, tumor on R shoulder, losing condition”

15. Build Our Ontology • From the text fields on the submission forms • Designed to meet our classification needs • Identify Potential “Clinical Suspects” • Classify BSE submissions into clinical syndromes

16. Clinical Suspect Refractory To Treatment Alive Yes Yes Progressive Behavior Change Progressive Neuro Signs OR Clinical Suspect Yes Over 30 Months Rule Outs No Yes [Alive]AND[(Refractory to tx)AND(Progressive Behavior ChangeORProgressive Neuro Change)AND(No Rules Outs)AND(Over 30 months of Age)] Clinical Suspect=

17. Clinical Suspect Refractory To Treatment Alive Yes Yes Progressive Behavior Change Progressive Neuro Signs OR Clinical Suspect Yes Over 30 Months Rule Outs No Yes [Alive]AND[(Refractory to tx)AND(Progressive Behavior ChangeORProgressive Neuro Change)AND(No Rules Outs)AND(Over 30 months of Age)] Clinical Suspect=

18. Ontology • Chronic (refractory to Tx) • Neurologic • Behavioral • Rule outs • Lame Skin/Ocular/Mammary • Cardiovascular Sudden Death • GI Infectious Dz • Repro Edema/Swelling/Neoplasia • Respiratory Trauma • Urologic Anorexia/Wt loss

19. Method • Text Mining Software • “WordStat” and “SimStat” (Provalis Research, Quebec City, PQ) • Spell checked text fields • Identified all words in the text fields • 292,537 words in total, 7,266 unique • Manually sorted words into ontology categories

20. Chronic • ADVANCED DOWNHIL* • CHONIC DURATION • CHRINIC AWHILE • CHRONCI POOR_DOER • CRONIC DECLIN* • D*BILIT* EMACIAT* • DAYS_AGO

21. Neurological • Ataxia • Neurological • Paresis/Paralysis • Hyperesthesia • Hypermetria • Locomotor deficits

22. Neurological • Ataxia • *ATAX*, AT*XIA, AT*XIC, ATACHIA, ATAXIA, TAXIA, etc • CNS • CN*, MENINGITIS, MENINGOMA , etc • Neurological • CONVULS*, HEAD_PRESS*, HEPATOENCEPHALOPATHY, N*URO*, NEUR*, etc • Paresis/Paralysis • PARLAYSIS, PARLYSIS, PARYALYZED, PARAPARESIS, PAREISIS, PARES*, PARETIC, etc

23. Behavioral • Behavioral • Hyperexcitable

24. Behavioral • Behavioral • *EHAV*, APPREHENS*, AVOID*, BALKING, BAWLING, BELIGER*, BELLIGER, BELLOW*, BIZARRE, COMPULSIVELY, CRAZY, DELIROUS etc • Hyperexcitable • ANXIETY, ANXIOUS, CHARG*, CHASE*, EXCITEABLE, HYPERALERT, HYPEREXC*, HYPEREXCITABLE, HYPERSENSITIV*, IRRITA*, etc.

25. Example Submission • Clinical Signs: Cow was in dry lot. Went off feed, coughing and labored breathing • Presumptive Diagnosis: PM findings- traumatic pericarditis and abscess from hardware between reticulum and diaphragm

26. Classifying Submissions • Cow was in dry lot. Went off feed, coughing and labored breathing Anorexia Respiratory

27. Classifying Submissions • PM findings- traumatic pericarditis and abscess from hardware between reticulum and diaphragm GI Cardiovascular Trauma

28. Classified Submissions N = 35,721

29. Clinical Suspects

30. Clinical Suspect Examples

31. Veterinary Practice Surveillance • Veterinary Practice Surveillance (VPS) • Cattle practitioners submit data about about cattle to AAFRD daily via a restricted access website • Practitioners classify sick cattle by commodity (cow-calf, dairy etc), age and syndrome (12) • Large sample • 26,016 Submissions (Aug 05 – Dec 06) • 5,081 farms • 31 veterinary clinics

32. Submissions per day Sept 2005 to July 2006

33. Respiratory Syndrome VPS = Cattle greater than 30 months of age

34. Clostridium hemolyticum VPS = 75 cases, BSE = 157 cases

35. Utility ? • Classifying/identifying “High Risk” • Generalize with caution (no prevalence) • Sampling bias • Misclassification • For each classification estimate: • Se and Sp of veterinarians • Se and Sp of text classifier

36. Utility ? • But: • Large sample • Disease importance or trends over time and space • Clostridium hemolyticum • Events: syndromic, unknown, emerging • Establish normal patterns to identify unusual events • Respond/investigate • Access for targeted surveillance

37. Questions? • Our Team: • Clarissa Snyder • Lindsay McLarty • John Berezowski • Contact us: john.berezowski@gov.ab.ca