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PATHOLOGY DATA MINING
The discipline of pathology
has electronically stored its collective experience
and we now have the ...
World Resources

A/Prof Ken Sikaris 15th October 2013
History of Sonic Healthcare
1987 Sonic Technology Australia Ltd
Barry Patterson, Mining Engineer
1987 Douglas Laboratories...
Sonic Healthcare

A/Prof Ken Sikaris 15th October 2013
Sonic Healthcare vs. Gold Mining

A/Prof Ken Sikaris 15th October 2013
The Growing ‘Market’ of Pathology

A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
Melbourne Pathology
10,000 patients / day
Avg 15 tests / patients
>30 million tests / year

A/Prof Ken Sikaris 15th Octobe...
A/Prof Ken Sikaris 15th October 2013
Medical Learning 1st Phase: Masters

Hippocrates
400BC

A/Prof Ken Sikaris 15th October 2013

Galen
150AD

Avicenna
980AD
...
Medical Learning 2nd Phase: Journals

NEJM
1812

A/Prof Ken Sikaris 15th October 2013

Lancet
1823

BMJ
1840

JAMA
1883

A...
Medical Learning: 3rd Phase
Databases

Framingham
Heart
Study
1948

National
Health
And
Nutrition
Examination
Survey
1971
...
“Database” articles on Medline

A/Prof Ken Sikaris 15th October 2013
Computer Foundations of Knowledge Acquisition
• Newell, A.; Simon, HA. Computer science as empirical
inquiry: symbols and ...
Pathology: The study and diagnosis of
disease.
Masters
Evidence
Based
Literature
Database
Patterns

Pathology
Conclusions
...
Pathology: The study and diagnosis of
disease.
Masters
Evidence
Based
Literature
Database
Patterns

Pathology
Conclusions
...
Pathology: The study and diagnosis of
disease.
Masters
Evidence
Based
Literature
Database
Patterns

Pathology
Conclusions
...
Knowledge discovery from databases
Fayyad U, Piatetsky-Shapiro G, Smyth P,
From Data Mining to Knowledge Discovery in Data...
A/Prof Ken Sikaris 15th October 2013
• 1. Heterogeneity of Data
– Volume & Complexity – MRI, CBC
– Physicians Interpretation – English, Synonyms
– Sensitivity ...
A/Prof Ken Sikaris 15th October 2013
• 2. Ethical / Legal / Social
– Data Ownership – Who can sell? (Not for sale.)
– Fear of Lawsuits – Unnecessary tests?
– P...
• 3. Statistical Philosophy
– Ambush – expected not found – but new one is!
• Training set and testing set
– Superset stat...
8.7% of clinical
data may be
Unusable
A/Prof Ken Sikaris 15th October 2013
• 3. Statistical Philosophy
– Ambush – expected not found – but new one is!
• Training set and testing set
– Superset stat...
Scientific method
• Discovery Driven Data Mining
Observation

• Linkage / Cluster analysis
• Finding similar segments
• Fi...
Data Searching vs Data Mining
• Data Searching
– You know what you are looking for
• and where it is:
–SQL searches:
» How...
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
Mortality (%)

Sodium & Inpatient Mortality
100
90
80
70
60
50
40
30
20
10
0
110

120

130

140

Sodium
Lowest
A/Prof Ken ...
Potassium & Inpatient Mortality
Mortality (%)

100
80
60
40
20
0
1

2

3

4

5

Potassium
Lowest
A/Prof Ken Sikaris 15th O...
Cholesterol and Inpatient Mortality
INPATIENT MORTALITY %

60
50
40
30
20
10
0
0

2

4

6
CHOLESTEROL

A/Prof Ken Sikaris ...
• 4. Special Status of Medicine
– Life and death
• Not luxury, pleasure or convenience product
– Long apprenticeship
– Med...
• ‘Markets’ for Laboratory Information
–
–
–
–
–
–

Individual Test Results
Profile of Test Results
Rare Tests
Critical Re...
Data Elements in Pathology Data Mining
Patient Demographics
Gender
DOB

Patient Status
Physiological
Childhood,Pregnancy
P...
Folate deficiency trends
Mets & Sikaris, et al. MJA 2002; 176

(Since 1995)

A/Prof Ken Sikaris 15th October 2013

Brown e...
LUNG

PROSTATE

A/Prof Ken Sikaris 15th October 2013

COLON

BREAST
•Sikaris K.A., “Combining Clinical Biochemistry and
Haematology Databases to define Predictive Values
for Ferritin.” Clin ...
2.36

2.31

2.35

2.3

2.34

2.29

2.33

2.28

2.32

2.27

2.31

2.26

2.3

2.25

2.29

2.24
2.23
100

0

10

20

30

40

...
A/Prof Ken Sikaris
15th

60

0

October 2013

50
50
65

40 17545
13 179
3 24
1
7 05074
3 71
9
6

93

0 29

42

22 6
68
27
...
Sonic Reference Intervals 2009

A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
Alkaline Phosphatase in Childhood

A/Prof Ken Sikaris 15th October 2013
Triglycerides in Childhood

A/Prof Ken Sikaris 15th October 2013
Cholesterol in Pregnancy

A/Prof Ken Sikaris 15th October 2013
Triglycerides in Pregnancy

A/Prof Ken Sikaris 15th October 2013
Hb in Pregnancy

A/Prof Ken Sikaris 15th October 2013
Lymphocytes in Pregnancy

A/Prof Ken Sikaris 15th October 2013
Neutrophils in Pregnancy

A/Prof Ken Sikaris 15th October 2013
Platelets in Pregnancy

A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
Brain

Mental
Heart
Dermatitis
Arthritis

Diabetes

Urinary

Drugs/Liver/HIV
Respiratory

A/Prof Ken Sikaris 15th October ...
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
Multidisciplinary teams

Funding

Benchmark other ‘markets’

Demonstrations for opinion leaders

Public-Private Partnershi...
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
A/Prof Ken Sikaris 15th October 2013
SUMMARY
• Pathology/Medical Databases
–
–
–
–

Unique technical issues
Complex legal, ethical, confidentiality ownership i...
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Ken Sikaris - Melbourne Pathology - Previous Discovery in Pathology Data Mining

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Associate Professor Ken Sikaris, Director of Clinical Support Systems, Sonic Healthcare and Principle Fellow in the Department of Pathology, Melbourne University presented "Previous Discovery in Pathology Data Mining" at the National Pathology Forum 2013.

This annual conference provides a platform for the public and private sectors to come together and discuss all the latest issues affecting the pathology sector in Australia. For more information, please visit the conference website: http://www.informa.com.au/pathologyforum

Published in: Health & Medicine, Technology
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Transcript of "Ken Sikaris - Melbourne Pathology - Previous Discovery in Pathology Data Mining"

  1. 1. PATHOLOGY DATA MINING The discipline of pathology has electronically stored its collective experience and we now have the tools to tackle big data. A/Prof Ken Sikaris BSc(Hons), MBBS, FRCPA, FAACB, FFSc, GAICD Clinical Support Systems Director, Sonic Healthcare Chemical Pathologis, Melbourne Pathology A/Prof Ken Sikaris 15th October 2013
  2. 2. World Resources A/Prof Ken Sikaris 15th October 2013
  3. 3. History of Sonic Healthcare 1987 Sonic Technology Australia Ltd Barry Patterson, Mining Engineer 1987 Douglas Laboratories (Syd) Colin Goldschmidt, Pathologist 1992 Clinpath (Adel) Michael Boyd 1993 Macquarie Pathology (Syd) 1994 Clinipath (Perth) 1994 Sonic Healthcare 1994 Pathlab (Adelaide) 1996 Hanly Moir, Barratt & Smith (Syd) 1997 Lifescreen 1998 Silex split off 1999 Southern (Wollongong), ADL (Syd) 1999 SGS (Melb Path, SNP) A/Prof Ken Sikaris 15th October 2013 2000 Hitech (Melb) 2000 Foundation (GP) 2000 Radiology (Vic, Qld, NSW) 2002 TDL (London) 2004 Schottdorf (Germany) 2004 IPN (GP) 2005 CPL (Texas) 2006 USA (Oklahoma, Florida) 2007 USA, Germany, Switzerland 2008 USA, Germany, Switzerland 2009 USA 2010 USA, Belgium, Prime (GP) 2011 USA, Belgium, Allied (GP) 2012 Germany, Healthscope (WA) 2013 Germany
  4. 4. Sonic Healthcare A/Prof Ken Sikaris 15th October 2013
  5. 5. Sonic Healthcare vs. Gold Mining A/Prof Ken Sikaris 15th October 2013
  6. 6. The Growing ‘Market’ of Pathology A/Prof Ken Sikaris 15th October 2013
  7. 7. A/Prof Ken Sikaris 15th October 2013
  8. 8. A/Prof Ken Sikaris 15th October 2013
  9. 9. Melbourne Pathology 10,000 patients / day Avg 15 tests / patients >30 million tests / year A/Prof Ken Sikaris 15th October 2013
  10. 10. A/Prof Ken Sikaris 15th October 2013
  11. 11. Medical Learning 1st Phase: Masters Hippocrates 400BC A/Prof Ken Sikaris 15th October 2013 Galen 150AD Avicenna 980AD Paracelsus 1520AD Osler 1880AD
  12. 12. Medical Learning 2nd Phase: Journals NEJM 1812 A/Prof Ken Sikaris 15th October 2013 Lancet 1823 BMJ 1840 JAMA 1883 Arch Int Med 1908
  13. 13. Medical Learning: 3rd Phase Databases Framingham Heart Study 1948 National Health And Nutrition Examination Survey 1971 Canadian Community Health Survey 2000 Busselton Health Study 1966 AusDiab 1999 n=450 n=177 n=79 ABS 2013 Journal Articles n=4,486 n=17,227 A/Prof Ken Sikaris 15th October 2013 n=….
  14. 14. “Database” articles on Medline A/Prof Ken Sikaris 15th October 2013
  15. 15. Computer Foundations of Knowledge Acquisition • Newell, A.; Simon, HA. Computer science as empirical inquiry: symbols and search. In: Haugeland, J., editor. Mind Design. MIT Press/Bradfor Books; Cambridge: 1981. p. 3566. • Compton P, Jansen R. “A philosophical basis for knowledge acquisition.” Knowledge Acquisition 1990;2(3):241–257. • Symbols & Relationships A/Prof Ken Sikaris 15th October 2013
  16. 16. Pathology: The study and diagnosis of disease. Masters Evidence Based Literature Database Patterns Pathology Conclusions Stored Knowledge Knowledge Experience A/Prof Ken Sikaris 15th October 2013 Experience
  17. 17. Pathology: The study and diagnosis of disease. Masters Evidence Based Literature Database Patterns Pathology Conclusions Stored Knowledge Knowledge Experience A/Prof Ken Sikaris 15th October 2013 Experience
  18. 18. Pathology: The study and diagnosis of disease. Masters Evidence Based Literature Database Patterns Pathology Conclusions Stored Knowledge Knowledge Experience A/Prof Ken Sikaris 15th October 2013 Experience
  19. 19. Knowledge discovery from databases Fayyad U, Piatetsky-Shapiro G, Smyth P, From Data Mining to Knowledge Discovery in Databases Artificial Intelligence Magazine 1996;Fall:37-53 A/Prof Ken Sikaris 15th October 2013
  20. 20. A/Prof Ken Sikaris 15th October 2013
  21. 21. • 1. Heterogeneity of Data – Volume & Complexity – MRI, CBC – Physicians Interpretation – English, Synonyms – Sensitivity & Specificity – All diagnoses imprecise – Mathematical Models – Gaussian(?), Qualitative – Canonical Forms – Different expressions (units) • Liver secondaries, metastatic liver disease • SNOMED, Synoptic reporting A/Prof Ken Sikaris 15th October 2013
  22. 22. A/Prof Ken Sikaris 15th October 2013
  23. 23. • 2. Ethical / Legal / Social – Data Ownership – Who can sell? (Not for sale.) – Fear of Lawsuits – Unnecessary tests? – Privacy and Security – Concealed identifiers – Expected benefits – How big? Rare diseases? – Administrative – Contractual agreements, Audits A/Prof Ken Sikaris 15th October 2013
  24. 24. • 3. Statistical Philosophy – Ambush – expected not found – but new one is! • Training set and testing set – Superset statistics • Qualitative, changes with time, missing data A/Prof Ken Sikaris 15th October 2013
  25. 25. 8.7% of clinical data may be Unusable A/Prof Ken Sikaris 15th October 2013
  26. 26. • 3. Statistical Philosophy – Ambush – expected not found – but new one is! • Training set and testing set – Superset statistics • Qualitative, changes with time, missing data – Established Procedures • Scientific Method A/Prof Ken Sikaris 15th October 2013
  27. 27. Scientific method • Discovery Driven Data Mining Observation • Linkage / Cluster analysis • Finding similar segments • Finding deviations in a segment Hypothesis • Verification Driven Data Search • Graphs, tables, descriptions Experiment A/Prof Ken Sikaris 15th October 2013
  28. 28. Data Searching vs Data Mining • Data Searching – You know what you are looking for • and where it is: –SQL searches: » How often does hyponatraemia cause death? • Data Mining – You know you are looking for (mortality) • but don’t assume you know where it is. A/Prof Ken Sikaris 15th October 2013
  29. 29. A/Prof Ken Sikaris 15th October 2013
  30. 30. A/Prof Ken Sikaris 15th October 2013
  31. 31. A/Prof Ken Sikaris 15th October 2013
  32. 32. Mortality (%) Sodium & Inpatient Mortality 100 90 80 70 60 50 40 30 20 10 0 110 120 130 140 Sodium Lowest A/Prof Ken Sikaris 15th October 2013 Highest 150 160
  33. 33. Potassium & Inpatient Mortality Mortality (%) 100 80 60 40 20 0 1 2 3 4 5 Potassium Lowest A/Prof Ken Sikaris 15th October 2013 Highest 6 7
  34. 34. Cholesterol and Inpatient Mortality INPATIENT MORTALITY % 60 50 40 30 20 10 0 0 2 4 6 CHOLESTEROL A/Prof Ken Sikaris 15th October 2013 8 10 12
  35. 35. • 4. Special Status of Medicine – Life and death • Not luxury, pleasure or convenience product – Long apprenticeship – Medical Research • Community responsibility • Scientific truths can be used for ‘good’ or ‘evil’ A/Prof Ken Sikaris 15th October 2013
  36. 36. • ‘Markets’ for Laboratory Information – – – – – – Individual Test Results Profile of Test Results Rare Tests Critical Results Follow Up tests Trended results A/Prof Ken Sikaris 15th October 2013 – Reference Intervals – Diagnostic algorithms – Accumulated experience – For emergency planning – To guide usefulness – For treatment guidance
  37. 37. Data Elements in Pathology Data Mining Patient Demographics Gender DOB Patient Status Physiological Childhood,Pregnancy Pathological Temporal Data Date of Test, Date of repeat Time of day, Season, Gestation, Admission Analyte Results Numerical Numbers / Ordinal Qualitative Groupings / Text A/Prof Ken Sikaris 15th October 2013
  38. 38. Folate deficiency trends Mets & Sikaris, et al. MJA 2002; 176 (Since 1995) A/Prof Ken Sikaris 15th October 2013 Brown et al. MJA 2011; 194 (2): 65-67. (Since Sept 2009)
  39. 39. LUNG PROSTATE A/Prof Ken Sikaris 15th October 2013 COLON BREAST
  40. 40. •Sikaris K.A., “Combining Clinical Biochemistry and Haematology Databases to define Predictive Values for Ferritin.” Clin Biochem Rev 1997;18:81. 95 OLD WOMEN Median MCV ALL MEN 90 YOUNG WOMEN 85 80 10 20 30 40 100 Ferritin A/Prof Ken Sikaris 15th October 2013 400 1000 YW OW M
  41. 41. 2.36 2.31 2.35 2.3 2.34 2.29 2.33 2.28 2.32 2.27 2.31 2.26 2.3 2.25 2.29 2.24 2.23 100 0 10 20 30 40 50 60 70 80 90 Premenopausal Corrected Calcium 2.32 2.28 Postmenopausal Corrected Calcium 2.37 Vit D A/Prof Ken Sikaris 15th October 2013 Lu ZX, Dahanayaka K, Lambrianou J, Ratniake S, Sikaris KA, “How much Vitamin D is sufficient? An evidence based approach.” Clin Biochem Rev 2007; 28:S29
  42. 42. A/Prof Ken Sikaris 15th 60 0 October 2013 50 50 65 40 17545 13 179 3 24 1 7 05074 3 71 9 6 93 0 29 42 22 6 68 27 54 07 34 63 11 9 36 05 35 89 13 91 39 40 42 13 16 91 40 58 42 04 18 28 39 15 34 76 16 49 32 20 28 96 13 91 23 07 19 32 90 15 16 119 8 65 3 1 941245731 59 366 4 5 10 58 71 9 66340 9 6 5 Geometric Mean PTH All 93 80 84 15 83 18 11 19 52 19 76 20 79 20 36 19 50 18 24 17 09 15 16 13 71 11 19 87 0 15 02 2 15 89 90 12 10 62 70 44 2 Geometric Mean ALP Premenopausal 95 0 98 100 8 7 4 3 100 Vitamin D Lu ZX, Dahanayaka K, Lambrianou J, Ratniake S, Sikaris KA, “How much Vitamin D is sufficient? An evidence based approach.” Clin Biochem Rev 2007; 28:S29
  43. 43. Sonic Reference Intervals 2009 A/Prof Ken Sikaris 15th October 2013
  44. 44. A/Prof Ken Sikaris 15th October 2013
  45. 45. Alkaline Phosphatase in Childhood A/Prof Ken Sikaris 15th October 2013
  46. 46. Triglycerides in Childhood A/Prof Ken Sikaris 15th October 2013
  47. 47. Cholesterol in Pregnancy A/Prof Ken Sikaris 15th October 2013
  48. 48. Triglycerides in Pregnancy A/Prof Ken Sikaris 15th October 2013
  49. 49. Hb in Pregnancy A/Prof Ken Sikaris 15th October 2013
  50. 50. Lymphocytes in Pregnancy A/Prof Ken Sikaris 15th October 2013
  51. 51. Neutrophils in Pregnancy A/Prof Ken Sikaris 15th October 2013
  52. 52. Platelets in Pregnancy A/Prof Ken Sikaris 15th October 2013
  53. 53. A/Prof Ken Sikaris 15th October 2013
  54. 54. A/Prof Ken Sikaris 15th October 2013
  55. 55. A/Prof Ken Sikaris 15th October 2013
  56. 56. A/Prof Ken Sikaris 15th October 2013
  57. 57. A/Prof Ken Sikaris 15th October 2013
  58. 58. A/Prof Ken Sikaris 15th October 2013
  59. 59. A/Prof Ken Sikaris 15th October 2013
  60. 60. A/Prof Ken Sikaris 15th October 2013
  61. 61. Brain Mental Heart Dermatitis Arthritis Diabetes Urinary Drugs/Liver/HIV Respiratory A/Prof Ken Sikaris 15th October 2013
  62. 62. A/Prof Ken Sikaris 15th October 2013
  63. 63. A/Prof Ken Sikaris 15th October 2013
  64. 64. A/Prof Ken Sikaris 15th October 2013
  65. 65. A/Prof Ken Sikaris 15th October 2013
  66. 66. A/Prof Ken Sikaris 15th October 2013
  67. 67. A/Prof Ken Sikaris 15th October 2013
  68. 68. A/Prof Ken Sikaris 15th October 2013
  69. 69. A/Prof Ken Sikaris 15th October 2013
  70. 70. A/Prof Ken Sikaris 15th October 2013
  71. 71. Multidisciplinary teams Funding Benchmark other ‘markets’ Demonstrations for opinion leaders Public-Private Partnerships Coding scheme standards A/Prof Ken Sikaris 15th October 2013
  72. 72. A/Prof Ken Sikaris 15th October 2013
  73. 73. A/Prof Ken Sikaris 15th October 2013
  74. 74. A/Prof Ken Sikaris 15th October 2013
  75. 75. A/Prof Ken Sikaris 15th October 2013
  76. 76. A/Prof Ken Sikaris 15th October 2013
  77. 77. SUMMARY • Pathology/Medical Databases – – – – Unique technical issues Complex legal, ethical, confidentiality ownership issues New paradigms in scientific/statistical analysis Special status: Life/death, medicine, community values • Data mining and pathology databases – A repository of established knowledge – A source for new knowledge – A framework for clinical decision making A/Prof Ken Sikaris 15th October 2013
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