Challenges in Predicting Patient Pathways Dr Rajesh Ransing School of Engineering Professor Mike Gravenor School of Medicine Grand Challenges in Information Driven Health Care Workshop

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

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

Challenges in Predicting Patient Pathways

Challenges in Predicting Patient Pathways

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.

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

Disease s Anatom y Genes Physiolog y Diseases Physiology Anatomy Genes Diseases Medical Informatics Bioinformatics Novel relationships & Deeper insights

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

MetaCause: Swansea University Spin Out Objective Objective: 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:  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

Disease s Anatom y Genes Physiolog y Diseases Physiology Anatomy Genes Diseases MetaCause is proven for Aerospace Applications Novel relationships & Deeper insights

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

1). Validation Studies Data: Fitness and metabolic measures in children On-going population studies, SAIL linked Risk Outcomes: precursors of diabetic and cardiac conditions Fairly well defined and understood system

1). Validation Studies: Metabolic Syndrome Risk factors for metabolic syndrome FactorEffect SizeConfirmed with logistic regression Waistmoderate ✓ Hipsmoderate ✓ Skinfoldsmoderate ✓ HDL-Cmoderate ✓ Interactions Waist * HipsStrong ✓

1). Validation Studies: correlates of fitness Factors associated with fitness test scores (top 10) 1. BMI6. HOMA 2. Hips7. Waist 3. Skinfolds8. LDL-C 4. DBP9. Total Cholesterol 5. Triglycerides10. SBP Possible advantages 1. 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)

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

2). Whole Genome Studies: statistical approaches Standard methods : n separate individual  2 tests rank by p-value Determine 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?

2). Whole Genome Studies: comparison of methods ‘Significant’ with Statistical Tests ‘Significant’ with MetaCause YesNo Yes56 No Where do they differ and Why?

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-value Those 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?

PubMed Medical Informatics Patient Record s Disease Databas e → 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 Bioinformatics Genome Transcriptome Proteome Interactome Metabolome Physiome Regulome Variome Pathome Pharmacogenome Disease World OMIM ► Personalized Medicine ► Decision Support System ► Patient Pathways ► Diagnostic Test Selector ► Clinical Trials Design ► Hypothesis Generator….. Data Mining the Ultimate Goal…….