For Clinical Development - · PDF file2 Clinical Analytics and Data Provisioning Central...

1 © 2014 PerkinElmer

HUMAN HEALTH • ENVIRONMENTAL HEALTH

PerkinElmer Signals™ Perspectives

For Clinical Development

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Clinical Analytics and Data Provisioning

Central Repository

Well-governed File Share

SDTM

data Non-SDTM EDC data,

IVRS data, Safety, CTMS

Central

Labs

PerkinElmer SignalsTM Perspectives

A Enrichment &

Normalization

UNIFY PROVISION

Clinical Analytics

Catalog,

Merging & Versioning

Public Data,

Outcomes Data

Data Discovery & Identification

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Clinical Data Repository

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Unifying Study Data (SDTM)

Necessary Capabilities

◦ Find existing DataMarts to

reuse

◦ Search for and add existing

data files

◦ Add new data files ad hoc

◦ Automatically identify data

types

◦ Automatically create

relationships between data

sources

◦ Merge multiple versions of

data into a single source

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Versioned Study Data Workflow

Data

File

Storage

Version

Merging

Data

Normalization

Data

Mart

Data

Analysis

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Provision Data Marts for Analytics

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Data Analysis with Versioning

Rapidly change between

different timepoints of data

collection from during the study

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Merging Data Across Studies (SDTM & non SDTM)

Necessary Capabilities

◦ Search for data from other studies

◦ Automatically detect possible

relationships to data

◦ Provision to analytics

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Universal Adapter learns as it is used

Existing catalog,

column headers

Metadata

Content

Overlap

Synonym dictionaries,

ontologies

User defined joins

Datamart, DataSource

and column tags

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Study Data Workflow

Data

Mart

Data Analysis

Study 1 (SDTM)

Study 2 (non SDTM)

Extended Data Mart

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Get a 360° View of Available Data

A Enrichment

Classify

Provision Ingest

Semi

structured

Unstructured

JDBC

Flat file (ad-hoc or

fileshare

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Scientifically Aware Text Analysis A

• Text is enhanced through the application of scientifically aware dictionaries and ontologies.

• Context is understood (e.g. Hedgehog)

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Scientifically Aware Text Analysis A

• Pattern recognition can be applied for further semantic enrichment & data joins

Sample ID Test type:

wet grab Value Unit

V = 3.2 Pa.s (25ºC)

Value Unit Temperature

If “PKI-<number>”

then [SampleID] distance [Test Type] to [Unit] < 4 words

Can now join with additional data sources

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Key Takeaways

PKI clinical

app framework

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Thank You