of the TRANSFoRm Project Przemyslaw Kazienko, Tomasz Kajdanowicz - - - PowerPoint PPT Presentation

Translational Research and Patient Safety in Europe

Data Mining Primary Care Data as part

• f the TRANSFoRm Project

Przemyslaw Kazienko, Tomasz Kajdanowicz - Wroclaw University of Technology, Poland Roxana Danger Mercaderes, Vasa Curcin – Imperial College London Jean-Karl Soler – Mediterranean Institute for Primary Care, Malta Derek Corrigan – Royal College of Surgeons in Ireland Brendan Delaney – Kings College London

This project is partially funded by the European Commission under the 7th Framework Programme. Grant Agreement No. 247787 Translational Research and Patient Safety in Europe (TRANSFoRm)

TRANSFoRm

EGPRN – Malta – October 2013

Translational Research and Patient Safety in Europe

Why data mine?

• Large repositories of potentially useful coded Primary Care

data exist such as the TRANSHIS project

• Can be used to derive empirical evidence, sensitive to

different populations, to support the goal of practicing “evidence based medicine”

• In the context of TRANSFoRm, can provide the data

necessary to populate actionable models of evidence from dynamic sources, rather than static literature based sources

Translational Research and Patient Safety in Europe

Data mining steps

TRANSHIS 1 - Derive Association Rules 2 - Calculate Quality Measures 3 - Filter based on High Quality Rules 4 - Clinical Review 5 - Evidence Transfer to Models

Translational Research and Patient Safety in Europe

Episode of Care

How to data mine TRANSHIS?

Encounter 1 Diagnostic Cues

RFEs RFEs RFEs

Diagnosis 1 Diagnosis 2 Diagnosis n

Encounter 2 Diagnostic Cues Encounter n Diagnostic Cues

.....

Time Patient Clinician

Translational Research and Patient Safety in Europe

Step 1 - Association Rules Structure

• RFEs, Diagnostic Cues, Demographic Features --------> Diagnosis

Antecedent Variables --------- > Consequent Variable

e.g. Abdominal Pain, Dysuria, Fever, Female  Urinary Tract Infection ICPC2 Coded = D06, U01, A03, F -> U70

– Apriori Algorithm implemented using tool called KNIME

Translational Research and Patient Safety in Europe

Step 2 - General Quality Measures

• Itemsets characterization

– Support – no of cases containing rule antecedents

• Rules characterization

– Lift – how much more likely antecedents and consequent

• ccur together than if statistically independent

– Confidence – probability of consequent occurring given the antecedent

Translational Research and Patient Safety in Europe

Step 2 -Bayesian Quality Measures

• Consequent (disease) interest

– Prior probability – prevalence of disease

• Variables characterization

– Likelihood ratios (+/-)

Translational Research and Patient Safety in Europe

Step 3, 4 -Web rule review tool

• Goal: Analyse data mining results from

TRANSHIS to identify clinically significant rules Features:

– Full rule view – Sorting rules – Multivariate filtering by quality measures – Annotation of interesting findings – Transfer of rules to evidence ontology service

Translational Research and Patient Safety in Europe

Translational Research and Patient Safety in Europe

TransHIS analysis example

Rules x => U71, (Cystitis/urinary infection, other)

• U01 – Dysuria
• U02 – Urinary Frequency
• U06 – Haematuria
• A03 – Fever
• D06 – Abdominal pain localised
• Important cues compare favourably with

literature – e.g. JAMA reviews

Translational Research and Patient Safety in Europe

Rule transfer to Evidence Model

Translational Research and Patient Safety in Europe

Rule transfer to Ontology

Translational Research and Patient Safety in Europe

RFE - Dysuria U01

RFE -Frequency U02 Urinary Tract Infection U71 Quantification - Support x Confidence y Lift z Demographic - Malta Female Symptom - Fever A03

hasDifferentialDiagnosis hasDemographic hasSymptom hasRFE

Evidence Representation

isQuantificationOf RFE - Abdominal Pain D06

Translational Research and Patient Safety in Europe

Conclusions

• Feasible to identify clinically meaningful evidence based on

coded primary care repositories of data

• Primary Care context is crucial - dependent on how common the

condition occurs in primary care e.g. UTI vs Ectopic Pregnancy

• May become more feasible for uncommon cases when data

from more countries are aggregated together to give larger volumes of data for uncommon cases

• The TRANSFoRm evidence models can be used to represent and

query this data for decision support

Translational Research and Patient Safety in Europe

11 Derek Corrigan: derekcorrigan@rcsi.ie www.hrbcentreprimarycare.ie www.transformproject.eu