Challenges in predicting patient pathways
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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

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Challenges in Predicting Patient Pathways

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Challenges in predicting patient pathways

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 pathways1

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 pathways2

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 pathways3

Challenges in Predicting Patient Pathways


Challenges in predicting patient pathways4

Challenges in Predicting Patient Pathways


Challenges in predicting patient pathways5

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 pathways6

Challenges in Predicting Patient Pathways

Current Practice

Personalized Medicine

One size fits all

The right treatment for the right person at the right time

Trial and Error


Challenges in predicting patient pathways

Genes

Diseases

Diseases

Diseases

Physiology

Diseases

Physiology

Genes

Genes

Anatomy

Diseases

Physiology

Anatomy

Diseases

Physiology

Anatomy

Diseases

Physiology

Anatomy

Diseases

Physiology

Anatomy

Diseases

Physiology

Anatomy

Diseases

Anatomy

Genes

Genes

Genes

Genes

Genes

Genes

Novel relationships & Deeper insights

Medical Informatics

Bioinformatics


Challenges in predicting patient pathways7

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

MetaCause: Swansea University Spin Out

Objective: Develop Self-learning Process Optimisation and Diagnosis Software.

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


Challenges in predicting patient pathways

Diseases

Physiology

Diseases

Physiology

Anatomy

Diseases

Physiology

Anatomy

Diseases

Physiology

Anatomy

Diseases

Physiology

Anatomy

Diseases

Physiology

Anatomy

Diseases

Anatomy

Genes

Genes

Genes

Genes

Genes

Genes

Novel relationships & Deeper insights

MetaCause is proven for Aerospace Applications

Diseases

Diseases

Genes

Genes

Anatomy

Diseases

Physiology


Mission statement

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

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


Challenges in predicting patient pathways

1). Validation Studies: Metabolic Syndrome


Challenges in predicting patient pathways

1). Validation Studies: correlates of fitness

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

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

2). Whole Genome Studies: statistical approaches

Standard methods :

n separate individual C2 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

2). Whole Genome Studies: comparison of methods

Where do they differ and Why?


2 main categories of misclassification so far

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?


Challenges in predicting patient pathways

the Ultimate Goal…….

Disease World

Medical Informatics

Bioinformatics

Genome

Variome

Transcriptome

Regulome

Disease Database

  • Personalized Medicine

  • Decision Support System

  • Patient Pathways

  • Diagnostic Test Selector

  • Clinical Trials Design

  • Hypothesis Generator…..

Proteome

  • 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……

Interactome

Patient Records

Pharmacogenome

Metabolome

Physiome

Pathome

Clinical Trials

Data Mining

PubMed

OMIM


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