qiagram: a novel interface for scientists to mine and understand big data with natural-language...

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QIAGRAM: A NOVEL INTERFACE TO MINE AND UNDERSTAND LARGE DATA SETS WITH NATURAL LANGUAGE QUERIES

Making data useful by making data smarter

Matthew Clark, Ph. D. BioFortis

February 6, 2013


Life sciences is a fast becoming a data problem

Beyond the obvious issues of scale and

reproducibility, the complexity and

diversity of …data poses the greatest

challenge to unlocking knowledge and

scientific discovery.*

Higdon et al (2012) Unraveling The Complexities Of Life Sciences Data: DOI: 10.1089/big.2012.1505


Big Data Challenges

VolumeLarge amounts of data

Veracity The credibility/quality, how trusted is the data

VelocityNeed for rapid analysis

Value Actionable outcomes for an organization

VarietyMany disparate types


A multitude of ‘omics and more

genomics proteomics cellomics metabalomics lipidomics transcriptomics

High Throughput technologiesNGS, imaging, mass-spectrometry, high

capacity flow, arrays

Collecting data at a prodigious rate – not always clear on how to use

Other DataHealthcare data (EMR), demographics,

Adverse events, clinical trials


Potential is huge…

• Targeted trials• Adverse events from pharmacy/clinical data• Segment patients based on profiles/responses• Biomarker discovery• Observational/outcome studies• “Virtual” clinical trials• In-silico discovery

Enormous promise…. Enormous challenges


Big data challenges - general

Source: The Economist, 2011, Big Data, Harnessing a Game changing asset


Barriers to extracting value

Source: The Economist, 2011, Big Data, Harnessing a Game changing asset


Big Data in life sciences/healthcare

• Multiple disparate data sources• Lack of integration patient-molecular-clinical-

assay-payer• “Swiss cheese” problem• Data cleansing/verification/credibility• Standards for data interchange• Privacy concerns• Lack good tools for cross-domain analytics


Changing paradigms

• Hypotheses driven– Traditional, test the hypotheses, scientific method

• what’s the mechanism of action of this drug

• Discovery driven– More open, questioning, enumerates elements to

drive hypotheses• What data do I have, what’s interesting

• Hybrid– Discovery driven + Hypotheses

• Human Genome Project


Data Exploration

Often neglected, but now key to getting value from life science big data


What is Data Exploration?

• Occurs before in-depth statistical/analytics• Explore and probe the data• Determine “what’s interesting”, “what’s relevant”• Generate hypotheses• Ensure data is there to support hypotheses


Asking questions of the data

Very complex query that touches many of the 5 V’s of Big Data


Asking questions of the dataMultiple data

sources

Domain expertiseMore data sources

More data sources

Requires considerable IT resources to program this query


Data Exploration challenges

• Programming is hard– Mostly SQL, SAS

• Lack of a shared language to support collaboration– Multidisciplinary data

requires domain experts

• Meaningful access to data– Sensitive to

regulatory/compliance

“The hands-on analytics time to write the SAS code and specify clearly what you need for each hypothesis is very time-consuming,” Felix Freuh, CEO, Medco*

*Miller, K. Big Data Analytics, Biomedical Computational Review, Winter 2011/2012


Clinical and molecular data

The Problem

Data Managers, overwhelmed by

researchers questions on complex data sources

Researchers with many questions across

disciplines

“Weeks to months to NEVER”“Lost in Translation”

SELECT DISTINCT PATIENT_ID, SAMPLE_ID, SAMPLE_NAME

FROM SAMPLE_INVENTORY S INNER JOIN PATIENTS P ON S.PATIENT_ID = P.PATIENT_ID

INNER JOIN DIAGNOSIS D ON S.PATIENT_ID = D.PATIENT_ID

INNER JOIN MEDICATIONS M ON S.PATIENT_ID = M.PATIENT_ID

INNER JOIN BIOMARKERS B ON S.PATIENT_ID = B.PATIENT_ID

WHERED.DIAGNOSIS_NAME = ‘LUNG CANCER’ AND

M.MEDICATION_GENERIC_NAME = ‘CETUXIMAB’ ANDB.BIOMARKER_NAME = ‘EGFR’ AND

B.OBSERVATION = 1ORDER BY PATIENT_ID, SAMPLE_NAME

No common language for

questions


Overcoming the challenges

• Deep Collaboration– Easy access to dynamic data– Intuitive tools– Secure holistic view of data– Collaboration


Big Data - Deep Collaboration

Single researcher in a silo often can go deep into the data, but maybe limited by their domain expertise

Small groups of researchers may be able to collaborate on asking questions but can’t go very deep with the tools they have today

QIAGRAM

Deep Collaboration is when multiple groups of researchers can collaborate in asking questions deeper into the layers of data. Shared domain knowledge allows deeper insights


Clinical and molecular data

Qiagram – Collaborative Scientific Intelligence

Researchers and data managers can collaborate

on creating queriesQiagram acts as a

shared, visual language for queries

More efficient and effective query creationTransparent to all stakeholders


MINING AERS DATAExamples


Small Data Can Become Big Data

1000 Drugs

1000 Drug Categories

1000 Adverse Events

109 Possible Combinations


Introduction to Qiagram


Answer the Question –Which Sources Have Data on Cholestasis?


Joining Data Sources is Simple


Combining Data Sources

• SNOMED contains hierarchy of drug and medical terms– ~12M records

• AERS contains reports of adverse events– ~70M records

• MedDRA contains hierarchy of adverse event terms– 150k records


SNOMED Ontology


MedDRA Hierarchy

low level term pref term hlt pref termhlgt pref term soc term

abdominal migraine migraine migraine headaches headaches nervous system disordersacute migraine migraine migraine headaches headaches nervous system disordersband-like headache tension headache headaches nec headaches nervous system disordersbasilar migraine basilar migraine migraine headaches headaches nervous system disorderscephalalgia headache headaches nec headaches nervous system disorderscephalalgia or cephalgia headache headaches nec headaches nervous system disorderscephalgia headache headaches nec headaches nervous system disorders


AERS

• All drug-related adverse events reported to FDA since 2000

• Tables for Drugs, demographics, indications, therapy, reactions, outcomes


Filter AERS Drugs by SNOMED Categories

AERS Drug list

Results in all analgesics in AERS, with associated case #s


Count the Various MedDRA high-level group terms reported for all drugs in AERS from

the SNOMED “antibiotic” category

AERS Drug list

SNOMED Categories

MedDRA Hierarchy Mapping


Top Ten Antibiotic Adverse Event High-Level Group Terms

Count hlgt pref term - MedDRA browser692,058 general system disorders nec522,739 epidermal and dermal conditions491,409 neurological disorders nec385,375 joint disorders297,433 gastrointestinal signs and symptoms290,760 respiratory disorders nec278,723 allergic conditions230,134 cardiac disorder signs and symptoms208,985 injuries nec197,446 gastrointestinal motility and defaecation conditions


Data Quality

• With more date, more chance for inconsistencies.

• Need easy ways to dynamically check the data, identify errant records

• Example: AERS data


Query to Locate Patients with Treatment Dates After Death Dates


Example Results

isr - drugs drug age death_dtstart_dt -

therapies

days after death - therapies + demographics

4016857 darbepoetin 59 9/5/2002 8/12/3003 365,5834006065 interferon I 46 8/23/2002 1/27/2991 361,0176013473 naloxone 54 6/15/1953 6/15/2008 20,0896038344 combivent 50 12/10/1958 12/8/2008 18,2616038344 levofloxacin 50 12/10/1958 12/8/2008 18,2616105245 dexamethasone 49 1/27/1959 7/15/2008 18,0676105245 bortezomib 49 1/27/1959 7/12/2008 18,0646252126 enfuvirtide 50 8/29/1956 7/8/2005 17,8456252126 efavirenz 50 8/29/1956 5/10/2002 16,6906252126 didanosine 50 8/29/1956 5/10/2002 16,690

Over 2,000 results


Answer Questions At the Speed of Thought

• Many “purpose built” systems answer pre-defined questions.

• However, in data exploration we need the ability to explore new questions


Collaborative Experience

• Team of physicians, informaticians, safety experts collaboratively explored questions based on large amounts of clinical (SDTM) data –

– Did subjects who were pre-treated with certain drug classes have the most change in cardiac function?

– What was in common with the subjects that were outliers in cardiac function change?

• Team defined baselines, changes, etc


CPRD

• The Clinical Practice Research Datalink (CPRD) is the new English NHS observational data and interventional research service, jointly funded by the NHS National Institute for Health Research (NIHR) and the Medicines and Healthcare products Regulatory Agency (MHRA).

• 6 large fact tables with 1 B to 2 B rows

• Example query– Identify patients with coronary artery disease who

have taken aspirin, then study readmission rates.


Thomson Reuters' MarketScan

• Several characteristics set MarketScan databases apart from other research databases. The core databases, Commercial, Medicare Supplemental, and Medicaid, are huge – over 170 million patients since 1995.

• Over 25 Fact tables 100 M up to 1.5 B rows• Example

– Identify cancer patients, looking at opiate treatment and study duration of the escalation


Premier Research Services

• Patient level data is available from more than 600 hospitals, 45 million records and 310 million hospital visits

• 5 large Fact tables from 100 M to over 4 B rows


Cerner Health Facts Database

• 8 Large Fact tables most in the 10's of millions of records

• Example– Looking for type II diabetes patients, study infection

rates of these patients based on hospital types.


Launching in March, 2013, cloud based Qiagram offering with AERS and TCGA data

qiagram: a novel interface for scientists to mine and understand big data with natural-language...

Documents

data useful

data problembeyond

diversity of data

layers of data

data explorationoften

big data analytics

highhealthcare data

large data sets