Panel: FROM SMALL TO BIG TO RICH DATA: Dealing with new sources of data in

Biomedicine

Precision and Participatory Medicine

Fernando J. Martin-Sanchez Professor and Chair of Health Informatics

Melbourne Medical School &

Director, Health and Biomedical Informatics Centre (HABIC)

•  “Rich” or “Smart Data” means information that actually makes sense.

•  “Fast Data” means information enabling real-time decision-making.

•  “Small data” connects people with timely, meaningful insights (derived from big data and/or “local” sources), organized and packaged – often visually – to be accessible, understandable, and actionable for everyday tasks. Digital traces of individual’s lives.

Health Informatics

Precision Medicine

Participatory health

Quantified Self

Social Media Exposome

Genome

Phenome

Big Data Digital Health

Research areas

Precision Medicine

Genome regulation

Microbiome

Epigenome

Exposome

Inter and intra individual

genetic variation

Phenome levels

Marc Rubin, Nature 2015

Precision medicine

•  Precision Medicine is an approach to discover and develop medicines, vaccines or routes of intervention (behavior, nutrition, etc.) that enable disease prevention and deliver superior therapeutic outcomes for patients, by integrating “Big Data”, clinical, molecular (multi-omics including epigenetics), environmental and behavioral information to understand the biological basis of disease.

•  This effort leads to better selection of disease targets and identification of patient populations that demonstrate improved clinical outcomes to novel preventive and therapeutic approaches.

C.M. Christensen et al.. The innovator’s prescription a disruptive solution for health care. McGraw-Hill, 2008

Personalised medicine

•  Improving therapy •  Looking for the right drug for

the right people

•  Companion diagnostics to stratify patients

•  Use of genomics data

•  Static - “Snapshot”

Precision medicine

•  Improving Diagnosis •  Looking for the right drug for

the right disease •  New taxonomy of disease and

disease reclassification

•  New/refined diagnostics methods •  Use of molecular (-omics) and

other (i.e. exposome) data sources •  Dynamic stratification - Modelling

patient journeys

Personalised vs Precision Medicine

Participatory Health

Participatory Health

Participatory Health mobile

Social networks

sensors

games

Internet of things

self tracking devices

PCEHR

à Patients informed and involved in decision making, prevention and learning

Health Informatics and Participatory health

I.  Personal genome services (23andMe) II.  Personal diagnostic testing III.  Personal medical image management IV.  Personal sensing and monitoring (QS) V.  Personal health records VI.  Patient reading doctor’s notes (OpenNotes) VII.  Patient initiating clinical trials (PLM) VIII.  Patient reporting outcomes (PROMIS) IX.  Patient sharing data (Social Media) X.  Shared decision making

Collecting data

Exchanging and using information

Participatory health

Genotype - Genome Expotype - Exposome

Phenotype -Phenome

Biomarkers (DNA sequence, Epigenetics)

Environmental risk factors (pollution, radiation, toxic agents, …)

Anatomy, Physiological, biochemical parameters (cholesterol, temperature, glucose, heart rate…)

Social media / Integrated personal health record / Personal Health Systems

Availability of new sensors for data collection

Convergence between precision and participatory medicine

Exposome Informatics

Exposome Resources

ENCODE UN IPCC GHG QIIME CDC NHANES EPA HPVIS EPA NCBI DDBJ WormBase VectorBase NIOSH NOES EPA CHAD EPA NHAPS EPA IUR EPA TRI Household Products DB Cosmetic Voluntary Reg. DB SGD NDAR Protein Data Bank GenBank EU ESIS Actor EPA ToxRefDB EPA Pesticide Usage Data ATSDR Tox Profiles FlyBase CMIP3 PRISM CTD GO

ToxCastDB ExpoCastDB BioRefDB DEA NFLIS DevToxDB ECOTOX DB CESAR DOE Indoor Air NHEXAS Tox21 IRIS HPVIS ChemSpider PubChem CTEPP EPA NATA EPA AIRS/AFS SEER VDW TCGA BAM MCAPS GEO/SRA Ensembl Factorbook CGHub

Biomedical Informatics

Martin-Sanchez F et al. J Am Med Inform Assoc. 2014

Biomedical Informatics

Large scale studies with healthy individuals

Health Data Exploration

´Snyderome´

100 People Wellness

Researcher

Hospital data

GP, labs, pharmacies data

Researcher-entered data

IMIA Working Group

•  Data Mining and Big Data Analytics •  New leadership

– Chair – Fernando Martin-Sanchez (U. of Melbourne, Australia)

– Vice-Chair – Lucia Sacchi (U. of Pavia, Italy)

The international Journal of Big Data and Analytics in Healthcare

•  Healthcare organizations have and will continue to generate large amounts of very complex, variable and heterogeneous data, termed “Big Data”, at all levels and in many formats. Hence, there is an increasing need to better understand, process and turn into actionable knowledge in order to foster new discoveries in research and improve quality, safety and efficiency of care.

•  To facilitate adoption of big data in the healthcare domain, it is essential to address the main issues associated with the size, complexity, and multidimensional nature of many datasets. This makes data analysis extremely challenging. Substantial research is needed to develop new methods and software tools for analyzing and visualising such large, complex, and multidimensional datasets. User-friendly data workflow platforms and analytic tools are also needed to facilitate the analysis of Big Data. While these challenges are complex, they are addressable.

•  Therefore, this new journal offers a forum to discuss the state-of-the-art research in biomedical and clinical big data, for providing practical solutions to a broad research community ranging from computer scientists to clinical practitioners, biomedical researchers and experts in Health Informatics.

The international Journal of Big Data and Analytics in Healthcare

•  Vision: Improving clinical care, biomedical research, public health and consumer health increasingly relies on an efficient processing of Big Data assets to generate actionable knowledge

•  Mission: The International Journal of Big Data and Analytics in Healthcare (IJBDAH) publishes high-quality, scholarly research papers, position papers, and case studies covering: hardware platforms and architectures, development of software methods, techniques and tools, applications and governance and adoption strategies for the use of Big Data in Healthcare and Clinical Research.

•  Objectives: • Sharing knowledge and experiences about big data and analytics in healthcare • Providing multidisciplinary big data practical solutions for present and future challenges in healthcare contexts. • Fostering the emergence of big data and data science as a relevant discipline in biomedicine

Call for papers

•  The Editors-in-Chief of the International Journal of Big Data and Analytics in Healthcare (IJBDAH) invites authors to submit manuscripts for consideration in this scholarly journal. The following describes the guidelines for submission to IJBDAH.

•  Submission Researchers and practitioners are invited to submit their original empirical research articles 3,000–5,000 words in length. Interested authors must consult the journal’s guidelines for manuscript submissions at http://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission.

•  All submitted articles will be reviewed on a double-blind review basis by no fewer than 3 members of the journal’s Editorial Review Board and 1 Associate Editor. Final decision regarding acceptance/revision/rejection will be based on the reviews received from the reviewers and at the sole discretion of the Editor-in-Chief.

•  All manuscripts must be submitted through the E-Editorial Discovery™ online submission manager: http://www.igi-global.com/submission/submit-manuscript

•  www.igi-global.com • 

Call for papers

•  Coverage IJBDAH is interested in publishing research addressing various architecture, methods, applications, and adoption of Big Data & Analytics in healthcare and research contexts. It targets a broad audience, ranging from computer scientists to clinical practitioners, with a special focus on health informatics experts. Topics to be discussed in this journal include (but are not limited to) the following:

•  Hardware platforms and architectures for Big Data –  Cloud computing –  Data storage –  Security –  Non relational database management system

•  Methods, techniques, processes and software tools for new sources of Big Data –  Data integration and data linkage –  Predictive modelling –  Process discovery –  Natural language processing and Text mining –  Data mining, web mining and machine learning –  Biomedical image and signal processing –  Mobile and Social media analytics –  Personal monitoring, self quantification and –  Visualisation –  Decision support

•  Applications of Big Data in Healthcare, biomedical research, public health and consumer health –  Precision and personalised medicine –  Participatory medicine and consumer health –  Population and global health –  Chronic disease management –  Biomedical and clinical research –  Rational drug design and pharmacovigilance

•  Governance and adoption strategies of Big Data approaches –  Ethics –  Training and education –  Change management –  Access, Usability and acceptability –  Policy issues –  Privacy and confidentiality –  Patient and clinician engagement –  Emergence of new professional roles and careers

© Copyright The University of Melbourne 2015

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