Intel Health & Life Sciences | Make it Personal
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Today: Many disparate data types, streams…
Genomics/Analytics
Genomics
Clinical
Claims & transactions
Meds & labs
Patient experience
Personal data
Big Data is the Foundation of Precision Medicine
Future: Integrated computing and integrated data
Leading to better decisions
Improved patient experience
Healthier population outcomes
Reduced costs
Accelerate transition to personalized medicine
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Intel Confidential – Do Not ForwardIntel Health & Life Sciences | Make it Personal
Analytics in action:
Penn Medicine
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OBJECTIVE
Predict heart failure patients who are at risk of hospital re-admission within 30 or 90 days of discharge
CHALLENGE
Analyze large amounts of unstructured data in patient records across multiple hospitals in a network
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Predicting Heart Failure with Machine Learning
42,358 Raw MedicationsFrom EMRs
allopurinol, clindamycin, coumadin,
dextrose, docusate, fluconazo, gabapentin,
glargine, heparin, hydrocortisone, insulin,
lansoprazo, lantus, levothroid,
levothyroxine, lovenox, morphine,
neurontin, omeprazo, oxycodone,
pneumococcal, senna, sertraline,
subcutaneous, testosterone, therapy, valp, warfarin, zolof …….
23,663Standardized
Medication Names
Pain Management, Heart Disease,
Diabetes, Liver Failure, Respiratory, ……
20 Derived Indicators
Apply text processing & regular expressions
Apply “LDA” machine learning
More At-risk Patients, Identified Early On, Enables Better Care
Build Model of Indicators Predict Individual Risk Using Indicators & Machine Learning
0,9
0,95
1
1,05
1,1
1,15
1,2
Patient E H R Only
(baseline)
E H R + Meds
Before Admit
E H R + Meds
Before Discharge
15% Relative Predictive
Model Performance Improvement
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demo— —
demo
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Intel-led open source project
Accelerates the collaborative creation of cloud-native applications driven by Big Data Analytics
Eases the development of analytic models by data scientists and their use by developers
Optimized for performance and security
Trusted Analytics Platform (TAP)
Powers the journey from data’s potential to value www.trustedanalytics.org
Intel Health & Life Sciences | Make it Personal Intel Confidential – Do Not ForwardFrom 12 WGS in 35 hours, to 96 WGS in 11 hours
Intel Health & Life Sciences | Make it Personal Intel Confidential – Do Not Forward
Intel Collaborative Cancer Cloud (CCC)
Q
Q
Lab #2
OHSU
Lab #1
Learn more about our work with OHSU:OHSU’s ExacloudCollaborative Analytics for Personalized Cancer Care
Learn more about precision medicine and genomic research:www.intel.com/healthcare/optimizecodehttps://www.whitehouse.gov/precision-medicine
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Big data Unicancer ConSoRe: analysis of clinical records for cancer care
*Other names and brands may be claimed as the property of others.
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Query the UNICANCER EMRs, as they’ve accumulated records over many years with
extensive doctor annotations.
Use natural language processing and the power of big data analytics for this purpose.
Propose a user-friendly interface for physicians.
Add potential other sources of data (next steps).
Obtain instant cohorts of real patients allowing better decisions and easier clinical trial
recruitment.
By definition, there is not very much literature on rare diseases.
With precision medicine, many more rare diseases will be discovered.
Actual patient cases are few, and clinical trials must be built over various healthcare sites. This takes time and is
costly.
Rare cancers demand rapid treatment decisions and cannot wait for lengthy clinical trials.
Fast response to media health threats is difficult.
SolutionGeneral challenge
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ConSoRe: using natural language processing to identify patients for clinical trials
*Other names and brands may be claimed as the property of others.
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We recently had a metastatic breast cancer research project where it took
30 people reviewing patient records for six months to assemble a cohort of
patients who had been treated in one of the 20 French cancer centers. We believe ConSoRe will help us do that
within a matter of hours or days.Pierre Heudel
Oncologist
Centre Léon Bérard
Query the UNICANCER EMRs.
Use natural language processing and the power of big data analytics.
Obtain instant cohorts of real patients allowing better decisions and better response to media
health threats.
32% of trial costs are attributed to recruiting participants