www.swansea.ac.uk challenges in predicting patient pathways dr rajesh ransing school of engineering...
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Challenges in Predicting Patient
Pathways Dr Rajesh RansingSchool of Engineering
Professor Mike Gravenor School of Medicine
Grand Challenges in Information Driven Health Care Workshop
www.swansea.ac.uk
Challenges in Predicting Patient Pathways
Driving Force?
Earlier and better detection
Accurate and reliable decision making
Encouraging self-care i.e. taking patients in the decision making loop
Limited resources – Time and Money
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Challenges in Predicting Patient Pathways
Data Explosion
Google world (internet, search, instant answers)
Post Genomic era
We have too much data
Goal
Self-evolving, self-learning computers to digest data and extract useful information/knowledge
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Challenges in Predicting Patient Pathways
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Challenges in Predicting Patient Pathways
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Challenges in Predicting Patient Pathways
We can not deviate from the good old ways of Diagnosis.
Patients need professional consultation with doctors.
Early and accurate diagnosis is important
We need tools to aid their decision making process with minimum interference.
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Challenges in Predicting Patient Pathways
The right treatment for the right person at the
right time
Trial and Error
Personalized MedicineCurrent Practice
One size fits all One size fits all
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DiseasesDiseasesDiseases
DiseasesDiseasesDiseases
Diseases
Anatomy
Anatomy
Anatomy
Anatomy
Anatomy
Anatomy
Genes
Genes
Genes
Genes
Genes
Gen
es
Physiology Physiolog
y Physiology Physiolog
y Physiology Physiology
Diseases
PhysiologyAnatomy
Genes
Genes
GenesDiseases
Diseases
Medical Informatics
Bioinformatics
Novel relationships & Deeper insights
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Challenges in Predicting Patient Pathways
Interdisciplinary Approach Health Care Providers – Hospitals – IHC
Actual patient data
Collaboration with Computer Scientists, Engineers, Clinicians, Health Informatics colleagues, Patients, Nurses
Data Analysis and Machine Learning software tools
MetaCause – Machine Learning
GeneCIS – Clinical Data Capturing System
Autonomy – Meaning based symbolic processing
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MetaCause: Swansea University Spin Out
ObjectiveObjective: Develop Self-learning Process Optimisation and Diagnosis Software.
Financial Supporters:Financial Supporters: (~£1M, 10 Person Years)
Engineering and Physical Sciences Research Council (EPSRC)
KEF Collaborative Industrial Research Project (Welsh Assembly
Government)
Industrial Partners:Industrial Partners:
Consortium of 7 foundries and Cast Metal Federation
Rolls Royce Plc, Tritech Precision Components Ltd
Blaysons Olefins Ltd, Wall Colmonoy Ltd, MB Fine Arts Ltd
Kaye Presteigne Ltd, MA Edwards Ltd
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DiseasesDiseasesDiseases
DiseasesDiseasesDiseases
Diseases
Anatomy
Anatomy
Anatomy
Anatomy
Anatomy
Anatomy
Genes
Genes
Genes
Genes
Genes
Gen
es
Physiology Physiolog
y Physiology Physiolog
y Physiology Physiology
Diseases
PhysiologyAnatomy
Genes GenesDiseases
Diseases
MetaCause is proven for Aerospace Applications
Novel relationships & Deeper insights
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Mission Statement
Earlier and better detection
Identify high risk patient groups and monitor them
Recognise patterns in genetic/clinical data and medical history
Identify main effects/interactions to predict risk factors
Develop a self-evolving software
Accurate and reliable decision making
Combine risks together and aid decision making
Reduce overall cost for NHS
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1). Validation Studies
Data: Fitness and metabolic measures in childrenOn-going population studies, SAIL linked
Risk Outcomes: precursors of diabetic and cardiac conditions
Fairly well defined and understood system
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1). Validation Studies: Metabolic Syndrome
Risk factors for metabolic syndrome
Factor Effect Size Confirmed with logistic regression
Waist moderate ✓
Hips moderate ✓
Skinfolds moderate ✓
HDL-C moderate ✓
Interactions
Waist * Hips Strong ✓
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1). Validation Studies: correlates of fitness
Factors associated with fitness test scores (top 10)
1. BMI 6. HOMA
2. Hips 7. Waist
3. Skinfolds 8. LDL-C
4. DBP 9. Total Cholesterol
5. Triglycerides 10. SBP
Possible advantages1. Detection of interactions (automatic, very large number of interactions) expected and detected)2. Non-linear trends in quantitative variables(good at detecting threshold effects when linear model doesn’t fit very well)
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2). Whole Genome Studies
Data: 1434 Single Nucleotide Polymorphisms in DNA samples
Risk Outcomes: Diabetes (type I), case (n=895) control (n=817)
SNP effects not previously well known
Aim is to create short list of most important SNPs
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2). Whole Genome Studies: statistical approaches
Standard methods : n separate individual 2 testsrank by p-valueDetermine cut-off for significance after correcting for multiple testing
MetaCause: Consider all SNPs together (and interactions)
As expected both Methods identify strongest signal (1 SNP, odds ratio = 3.0, large sample size (few missing values))
What is the effect of method choice on ‘short list’ of candidate genes?
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2). Whole Genome Studies: comparison of methods
‘Significant’ with Statistical Tests
‘Significant’ with MetaCause
Yes No
Yes 5 6
No 15 1408
Where do they differ and Why?
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2). Main Categories of Misclassification (So far!)
1. p-value vs odds ratio (clinical vs statistical significance)Closer correlation between MetaCause and SNPs ranked by odds ratio than p-valueThose SNPs short listed by MetaCause but not statistically significant were found to
have large odds ratios 2. Consideration of interactions (automatically searched for in MetaCause)
interactions involving ‘non-significant’ SNPs. 3. Consideration of population size. Risky rare genotypes have less “impact“
at the population level.
Challenge: Need to clearly define study questions (and hence functions of risk to optimised):
Individual SNP effects or interactions?Individual or population risk?
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PubMed
Medical Informatics
Patient Records
Patient Records
Disease Database
Disease Database→Name→Synonyms→Related/Similar Diseases→Subtypes→Etiology →Predisposing Causes→Pathogenesis→Molecular Basis→Population Genetics→Clinical findings→System(s) involved→Lesions →Diagnosis→Prognosis→Treatment→Clinical Trials……
Clinical Trials
Clinical Trials
Bioinformatics
Genome
Transcriptome
Proteome
Interactome
Metabolome
Physiome
Regulome Variome
Pathome
Ph
arm
acog
enom
e
Disease
World
OMIM
►Personalized Medicine►Decision Support System►Patient Pathways►Diagnostic Test Selector►Clinical Trials Design►Hypothesis Generator…..
Data Mining
the Ultimate Goal…….
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