BIG DATA SCIENTIFIC AND COMMERCIAL APPLICATIONS (ITNPD4) LECTURE: DATA SCIENCE IN MEDICINE

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1 BIG DATA SCIENTIFIC AND COMMERCIAL APPLICATIONS (ITNPD4) LECTURE: DATA SCIENCE IN MEDICINE Gabriela Ochoa OVERVIEW Module My Contributions Description: with guest lectures from science and industry, this course presents a set of case studies of Big Data in action. Aim: To enable students to see what Big Data techniques are used. Data science in Medicine: 1 lecture, 1 speaker: Dr Marilyn Lennon Complex Network Analysis and Visualisation: 2 lectures and 1 tutorial, 1 speaker: Dr Pawel Widera Data Journalism (invited speaker: Dr Eddy Borges) 1

of Big Data in action. Aim: To enable students to see what Big Data techniques are used.

2 OUTLINE Epidemiology Prediction and causality Asking causal questions How to obtain medical data Observational studies Examples of data visualisation and infographics in medicine and healthcare Ebola virus disease Antibiotic resistance Case study of data science diagnostic: early cancer detection 3 WHAT IS EPIDEMIOLOGY? The study of how often diseases occur in different groups of people and why Data-Driven/Quantitative discipline based on probability, statistics, causal reasoning and sound research methods. Clinicians are concerned with the health of an individual; epidemiologists are concerned with the collective health of the people in a community Epidemiological information is used to: Plan and evaluate strategies to prevent illness Guide the management of patients in whom disease has already developed 4 2

The study of how often diseases occur in different groups of people and why Data-Driven/Quantitative discipline based on probability, statistics, causal reasoning and sound research methods.

3 PREDICTION AND CAUSALITY Many models and examples in Data Science focus on the fundamental problem of prediction Example: build a model to predict whether or not a person would be likely to prefer a certain item (a movie or a book) There may be thousands of features that go into the model (feature selection may be needed to narrow them down) The model is optimised to get the highest accuracy The meaning or interpretation of the features, (especially if there are thousands of them) is not a concern. 5 PREDICTION AND CAUSALITY A different set of real-world problems are situations where understanding causality is important Determine whether a certain type of behaviour causes a certain outcome Example: determine whether a certain treatment (or drug) helps to cure a disease The goal is not to optimize for predictive accuracy, but rather to be able to isolate causes Sometimes the same statistical methods (logistic regression, linear regression) are used 6 3

meaning or interpretation of the features, (especially if there are thousands of them) is not a concern.

4 ASKING CASUAL QUESTION Correlation doesn t imply causation Big challenge: establishing the causal relationship between two variables. When does one thing cause another? It is more difficult than it sounds! Natural form of a causal question: What is the effect of on? Examples What is the effect of advertising on customer behaviour? What is the effect of smoking on lung cancer? What is the effect of drug on time until viral failure?, and the general case What is the effect of treatment on outcome? 7 HOW TO OBTAIN MEDICAL DATA? When researchers have specific questions but no data to answer them. They need to collect data! Two main ways to collect data 1. Design of experiments: The gold standard: randomised clinical trials. A group of people receive treatment, others in control group. It works well, but it is not always feasible for ethical reasons. 2. Observational study: Data collected from patients as they undergo routine medical care. Goal: elucidate cause-and-effect relationships in which it is not feasible to use controlled experimentation 8 4

What is the effect of smoking on lung cancer? What is the effect of drug on time until viral failure?, and the general case What is the effect of treatment on outcome? 7 HOW TO OBTAIN MEDICAL DATA?

5 OBSERVATIONAL STUDIES Most data science activity revolves around observational data. Examples of tests you can t run with design of experiments studies, for ethical reasons: smoking and heart disease (you can t randomly assign someone to smoke) vitamin C and cancer survival aspirin and mortality cocaine and birthweight diet and mortality 9 BIG DATA AND EPIDEMIOLOGY Prof. David Madigan, Chair of Statistics, Columbia University, New York City. The Observational Health Data Sciences and Informatics OHDSI: ( Madigan et al. examined 50 studies, each corresponding to a drugoutcome pair (e.g antibiotics with Gastrointestinal (GI) bleeding) 5,000 analysis for every pair on 9 different databases (ranging from 4,000,000 to 80,000,000 patients) Example: Drug: ACE inhibitors (a medication often used to treat high blood pressure) Outcome: swelling of the heart In this case, for one DB the drug triples the risk of heart swelling; but for another DB, it seems to have a six-fold increase of risk. Good example! As it is consistent. 10 5

and mortality cocaine and birthweight diet and mortality 9 BIG DATA AND EPIDEMIOLOGY Prof. David Madigan, Chair of Statistics, Columbia University, New York City.

6 For 20 of the 50 pairs, you can go from statistically significant in one direction to the other direction depending on the database you pick. In other words, you can get whatever you want! 11 WHAT IS INFOGRAPHICS? A data rich visual representation of information, or knowledge Objective: to present information quickly and clearly simplify a complicated subject or turn an otherwise boring subject into a captivating experience. Should be visually engaging Examples: What is infographics? The online population boom 12 6

A data rich visual representation of information, or knowledge Objective: to present information quickly and clearly simplify a

7 EBOLA VIRUS DISEASE A severe, often fatal illness in humans. Caused by infection with one of the Ebola virus strains. First appeared in 1976 near the Ebola river in the Republic of Congo 2014 outbreak: 27,000+ cases, 11,000+ deaths Likelihood of catching the disease in the UK is very low unless you've travelled to a known infected area Symptoms, Contagious: see Infographic. Pictures: Pictures.html 13 CDC Organization. US one of the major operating components of the Department of Health and Human Services

in the UK is very low unless you've travelled to a known infected area Symptoms, Contagious: see Infographic. Pictures: http://www.md-health.

8 THE MICROBE-SCOPE From Information is Beautiful, David McCandles Infectious diseases in context How Ebola compares to other infectious diseases? Horizontal axis: Contagiousness Average reproduction number (rate, ratio) Ro the number of additional cases generated by a single case of the disease. how likely an infectious disease might spread through a population if nothing is done to contain the outbreak. Vertical axis: Deadliness average case fatality rate - the % of infectees who die

Horizontal axis: Contagiousness Average reproduction number (rate, ratio) Ro the number of additional cases generated by a single

We're at a tipping point: an increasing number of germs no longer respond to the drugs designed to kill them. http://www.cdc.

9 ANTIBIOTIC/ANTIMICROBIAL RESISTANCE Is the ability of microbes to resist the effects of drugs that is, the germs are not killed, and their growth is not stopped. As people use antibiotics more and more often, bacteria develop resistance to the drugs. We're at a tipping point: an increasing number of germs no longer respond to the drugs designed to kill them ANTIBIOTIC RESISTANCE What you need to know Europe s fight against antimicrobial resistance Where antibiotic resistance is worst around the world 18 9

ANTIMICROBIAL RESISTANCE Deaths attributable to AMR every year compared to other major causes of death The Review on Antimicrobial Resistance Chaired by Jim O Neill December 2014 19 THE FUTURE OF

10 ANTIMICROBIAL RESISTANCE Deaths attributable to AMR every year compared to other major causes of death The Review on Antimicrobial Resistance Chaired by Jim O Neill December THE FUTURE OF EARLY CANCER DETECTION? TED talk: Jorge Soto MirOculus website- A simple, non-invasive, open-source test that looks for early signs of multiple forms of cancer. microrna detection platform for molecular data gathering, analysis and interpretation. MicroRNAs Small RNA molecules Can be found in the blood Reflect a person s health status Can be used as biomarkers for various diseases, including cancer

com/ A simple, non-invasive, open-source test that looks for early signs of multiple forms of cancer.

Ebola: Teaching Points for Nurse Educators

Ebola: Teaching Points for Nurse Educators Heightened media attention on emerging disease outbreaks such as Ebola may raise concerns among students. During outbreaks such as Ebola, nursing faculty are