ken sikaris - melbourne pathology - previous discovery in pathology data mining

A/Prof Ken Sikaris 15th October 2013

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


World Resources


History of Sonic Healthcare

1987 Douglas Laboratories (Syd)

Colin Goldschmidt, Pathologist 1992 Clinpath (Adel)

Michael Boyd

1993 Macquarie Pathology (Syd)

1994 Clinipath (Perth)

Barry Patterson, Mining Engineer

1987 Sonic Technology Australia Ltd

1994 Sonic Healthcare

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

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)


Sonic Healthcare


Sonic Healthcare vs. Gold Mining


The Growing ‘Market’ of Pathology


10,000 patients / day

Avg 15 tests / patients

>30 million tests / year

Melbourne Pathology


Medical Learning 1st Phase: Masters

Hippocrates

400BC

Galen

150AD

Avicenna

980AD

Paracelsus

1520AD

Osler

1880AD


Medical Learning 2nd Phase: Journals

NEJM

1812

BMJ

1840

JAMA

1883

Lancet

1823

Arch Int Med

1908


Medical Learning: 3rd Phase Databases

Framingham

Heart

Study

1948

n=4,486

National

Health

And

Nutrition

Examination

Survey

1971

n=17,227

AusDiab

1999

n=79

Canadian

Community

Health

Survey

2000

n=450

Busselton

Health

Study

1966

n=177

ABS

2013

n=….

Journal Articles


“Database” articles on Medline


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. 35-

66.

• Compton P, Jansen R. “A philosophical basis for

knowledge acquisition.” Knowledge Acquisition

1990;2(3):241–257.

• Symbols & Relationships


Pathology: The study and diagnosis of

disease.

Masters

Evidence

Based

Literature

Database

Patterns

Pathology

Conclusions

Stored

Knowledge Experience Knowledge

Experience


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


• 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


• 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


• 3. Statistical Philosophy

– Ambush – expected not found – but new one is!

• Training set and testing set

– Superset statistics

• Qualitative, changes with time, missing data


8.7% of clinical

data may be

Unusable


• 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


Scientific method

• Verification Driven Data Search

• Graphs, tables, descriptions

Hypothesis

Observation

Experiment

• Discovery Driven Data Mining

• Linkage / Cluster analysis

• Finding similar segments

• Finding deviations in a segment


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.


110 120 130 140 150 160

Sodium

0

10

20

30

40

50

60

70

80

90

100

Mo

rta

lity

(%

)

Lowest Highest

Sodium & Inpatient Mortality


1 2 3 4 5 6 7

Potassium

0

20

40

60

80

100

Mo

rtali

ty (

%)

Lowest Highest

Potassium & Inpatient Mortality


Cholesterol and Inpatient Mortality

0 2 4 6 8 10 12

CHOLESTEROL

0

10

20

30

40

50

60

INP

AT

IEN

T M

OR

TA

LIT

Y %


• 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’


• ‘Markets’ for Laboratory Information

– Individual Test Results – Reference Intervals

– Profile of Test Results – Diagnostic algorithms

– Rare Tests – Accumulated experience

– Critical Results – For emergency planning

– Follow Up tests – To guide usefulness

– Trended results – For treatment guidance


Data Elements in Pathology Data Mining

Patient Demographics

Gender

DOB

Patient Status

Physiological

Childhood,Pregnancy

Pathological

Analyte Results

Numerical

Numbers / Ordinal

Qualitative

Groupings / Text

Temporal Data

Date of Test, Date of repeat

Time of day, Season,

Gestation, Admission


Folate deficiency trends

Mets & Sikaris, et al. MJA 2002; 176

(Since 1995)

Brown et al. MJA 2011; 194 (2): 65-67.

(Since Sept 2009)


LUNG COLON

PROSTATE BREAST


10 100 100020 30 40040

Ferritin

80

85

90

95

Media

n M

CV

YW

OW

M

ALL MEN

OLD WOMEN

YOUNG WOMEN

•Sikaris K.A., “Combining Clinical Biochemistry and Haematology Databases to define Predictive Values for Ferritin.” Clin Biochem Rev 1997;18:81.


0 10 20 30 40 50 60 70 80 90 1000

Vit D

2.28

2.29

2.3

2.31

2.32

2.33

2.34

2.35

2.36

2.37

Post

menopausa

l C

orr

ecte

d C

alc

ium

2.23

2.24

2.25

2.26

2.27

2.28

2.29

2.3

2.31

2.32

Pre

menopausal C

orr

ecte

d C

alc

ium

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


442

622

1002

1293

1584 15

83

1811

1952

1976

2079

2036

1950

1824

1709

1516

1371

1119

870

770

597

454

407310246

179

151

133

98

426

930

1196

1391

1691

1828

1649

1391

90561

5

384

196

136

950

1589

2268

2754

2907

3463

3605

3589

3940

4213

4058

4204

3915

3476

3220

2896

2307 19

3215

171153

949

678

590

464319

265

201

0 50 10065

Vitamin D

50

60

70

80

90

100P

rem

enopausal

Geom

etr

ic M

ean A

LP

3

4

5

6

7

8

All

Geom

etr

ic M

ean P

TH

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


Sonic Reference Intervals 2009


Alkaline Phosphatase in Childhood


Triglycerides in Childhood


Cholesterol in Pregnancy


Triglycerides in Pregnancy


Hb in Pregnancy


Lymphocytes in Pregnancy


Neutrophils in Pregnancy


Platelets in Pregnancy


Brain

Mental Heart Dermatitis

Arthritis

Diabetes

Urinary

Drugs/Liver/HIV Respiratory


Public-Private Partnerships

Funding

Demonstrations for opinion leaders

Coding scheme standards

Multidisciplinary teams

Benchmark other ‘markets’


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

ken sikaris - melbourne pathology - previous discovery in pathology data mining

Health & Medicine

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