Post on 23-May-2020
© Hitachi, Ltd. 2014. All rights reserved.
July 4, 2014
Toru HisamitsuChief Researcher
Life Science Research Center
Central Research Laboratory
Hitachi, Ltd.
Analytics of Clinical “Big Data”-Toward Applications for Pharmaceutical Industry-
The 13th Kitasato University - Harvard School of Public Health Symposium
© Hitachi, Ltd. 2014. All rights reserved.
1. Hitachi’s Concept for Healthcare IT
2. Hitachi’s Technologies and Their Pharmaceutical Industry
Applications
1
Contents
© Hitachi, Ltd. 2014. All rights reserved.
1. Hitachi’s Concept for Healthcare IT
2
© Hitachi, Ltd. 2014. All rights reserved.
1-1. Hitachi’s existing healthcare-related business
There has been rapid and significant change in healthcare around
world due to aging populations, increase in number of lifestyle-related
disease patients, and rising medical costs.
Hitachi has been focusing on healthcare business field within our
Social Innovation Business.
Clinical Inspection Device
Life Microscope
Diagnosis of mental health (advanced medical technology)Post Medical
Medical
Checkup system
“Harasuma” diet
Imaging/Radiation info. system
Proton Beam Therapy
Clinical Inspection system
CT/MRI/PET/Ultrasound diagnostics
Nursing care serviceFacility operation
Management solution fornursing care and welfare
Pre-Medical
Contributions towards realizing society where everyone lives healthy lives.
Open MRI
Hitachi is bringing together all its assets to provide innovative technologies,associated systems, solutions, and services.
3
EMR system
© Hitachi, Ltd. 2014. All rights reserved. 4
Provide comprehensive solution for care-cycle optimization by integrating andanalyzing data during care cycle.
Provide solutions for pharmaceutical/food companies using aggregated data.
Focus on prediction of medical conditions, which is essential for both disease prevention support and medical process optimization.
Develop core technologies using UK and JP PoC, and break into ACO market in US.
For home:
Personalized home care
For Checkup Provider:
Personalized prevention
Therapy
Prevention
Test
Diagnosis
care cycle
Home care
For Medical Provider/ Hospital & Clinic:
Optimize medical process and clinical resources to collaborate with checkup and home care
For insurerImprove total health status of insured For
pharmaceutical companies: Marketing Drug
development Drug efficacy
/Adverse event detection
For food companies Marketing Food efficacy
/Healthy food development
Primary use Secondary use
Goal
Strategy
1-2. Healthcare IT mission
PoC: Proof of Concept, ACO: Accountable Care Organization
© Hitachi, Ltd. 2014. All rights reserved.
1-3. Hitachi’s next generation healthcare service
5
Insurer, ACOPharmaceutical/food
companies
An
aly
ze
(co
mm
on m
odu
les)
Aggre
gation
Medical care
guideline
Medical care
data
Medical institution
Extraction of medical meta information
Contents house ware
Examination analysisStructured DB
Common analytic library・API
Clinical
Knowledge
Health
records
Healthcare providers
File Contents Storage
Metadata DB
Healthcare data analytics for value-based
healthcare
Secondary use of healthcare data for product
development S
erv
ice
(app
lica
tion
) Disease
prevention/
management
support
Population
health
management・・・ ・・・
Marketing
Medical cost
estimation
Adverse event
analysis
Secure database
for open innovation
【Secured healthcare cloud】
© Hitachi, Ltd. 2014. All rights reserved.
Hitachi General Hospital
Hitachinaka General Hospital
Taga Genera l Hospi ta l
Hitachi Healthcare Center
Tsuchiura Health Checkup Center
288 beds
148 beds
410 beds
Hospitals
Health Insurance Society
Insured Numbers: 270,000
Yokohama Research Laboratory (1,100)
Hitachi Research Laboratory (1,200)
Research Laboratories
Central Research Laboratory (900)
3 Hospitals and 2 Health Checkup Centers 3 Research Laboratories
and 1 Design Div. (Total: 200, Global Lab: US, UK, CN, SG)
Design Division (150)
6
1-4. Hitachi’s healthcare related assets
© Hitachi, Ltd. 2014. All rights reserved.
2. Hitachi’s Technologies and Their PharmaceuticalIndustry Applications
7
© Hitachi, Ltd. 2014. All rights reserved.
2-1. Technologies mapped on service stack
8
Insurer, ACOPharmaceutical/food
companies
An
aly
ze
(co
mm
on m
odu
les)
Aggre
gation
Medical care
guideline
Medical care
data
Medical institution
Extraction of medical meta information
Contents house ware
Examination analysisStructured DB
Common analytic library・API
Clinical
Knowledge
Health
records
Healthcare providers
File Contents Storage
Metadata DB
Healthcare data analytics for value-based
healthcare
Secondary use of healthcare data for product
development S
erv
ice
(app
lica
tion
) Disease
prevention/
management
support
Population
health
management・・・ ・・・
Marketing
Medical cost
estimation
Adverse event
analysis
Secure database
for open innovation
Marketing
Medical cost
estimation
Adverse event
analysis
Secure database
for open innovation
Disease progression analysis
Security/Privacy protectionGraph-based clinical repository construction Medical Text Analysis
Medical cost simulation
2-2
2-3
2-4
【Secured healthcare cloud】
© Hitachi, Ltd. 2014. All rights reserved.
Life habit data
Health data
Omics data
Clinical data
Ingest・Cleansing・Curation Analytic
Database
Research A
Research C
Research B
・Cohort Extraction・k-anonymization・Privacy preserved search/analysis
Medical Knowledge
【Issues】・Missing data (Lab data, Disease Name, etc.)・Data Fragmentation・Privacy Protection
① Clinical data is messy→ It is necessary to reduce cost for data preparation process
② Clinical data is sensitive→ It is necessary to guarantee data security while keeping ease of
data handling
Graph-based clinicalrepository construction
Medical Text Analysis
Security / Privacy protection
9
2-2. Technologies for managing clinical data
Staging Database
© Hitachi, Ltd. 2014. All rights reserved.
• Clinical guideline
• Disease-drug relation
• Disease-test relation, etc.
2-2-1. Graph-based clinical repository construction
10
Constructing repository of clinical events extracted from hospital information systems (EMR, PACS, LIS, etc).
By adding clinical knowledge as semantic links between clinical events, repository enables 90% reduction of human labor to make data mart.
Clinical Semantic Linkage
Graph-based
representation
Cirrhosis
Drug Test
Test Operation
PatientA
Problem
Treat
◆ Add semantic links between clinical events automatically extracted from hospital information systems
Drug
Disease
focused on
Added Links
Liver Cancer
Extracted from
medical textsEMR
PACS
LIS
Finance
© Hitachi, Ltd. 2014. All rights reserved.
2-2-2. Medical text analysis (1/2)
Unstructured medical texts (such as discharge summaries) contain a lot of
medical information that cannot be directly utilized by computer.
Analyzing medical texts transforms unstructured texts into XML.
This enables knowledge extraction from large volume of medical texts
(including publicly available DBs).
Output is structured XML.
Machine friendly
Easy to analyze
Input is text.
Flexible
Human friendly
Prednisolone 30 mg
daily was
commenced, but her
clinical picture did
not improve. Dose
was increased to 50
mg daily, .......
<event>
<type>Action</type>
<modifier>commence</modifier>
<action>.prescription.</action>
<what>Prednisolone</what>
<quantity>30 mg</quantity>
</event>
<event>
<type>State-Change</type>
<change>improve</change>
<wrt>clinical picture</wrt>
</event>
Discharge
summary
(pdf)
Layout Analysis Structuring
Hospital course
11
© Hitachi, Ltd. 2014. All rights reserved.
体温が38度から40度まで上昇した(Body temperature increased from 38℃ to
40 ℃)
<event><polarity>0</polarity><attitude>confirm</attitude>
<change>.up.</action><action>上昇(increase)</change><attribute>体温(body temperature)</what><is>40度(40 degrees)</is><was>38度(38 degrees)</was>
</event>
Structuring (XML)
Text documents
XML
Morphological analysis
Predicate argument analysis
Event structure analysis
Priority rule for argument search
Predicate argument
structure data
Aiming to extract information such as time, date, symptoms and treatments in unstructured data from text documents
■ Structuring clinical document (ex. discharge summary)
■Analysis speed: 0.45 sec/documentDischarge summaries of Hitachi general hospital for 2 years(approximately 19,000) can be analyzed in 2.4 hours.
■Accuracy: 80 - 85% (precision: 90%, recall (giving answers): 90%)Internal medicine cases give higher score than surgery.
2-2-2. Medical text analysis (2/2)
12
© Hitachi, Ltd. 2014. All rights reserved.
k-anonymization: Remove some details of cells so that no. of rows with same values becomes more than k.
Simple Anonymization: From data, remove all or some details by which individuals could be identified.
e.g. name, address, and phone number
Address Age Gender
Tokyo, Japan 28 Male
New York, USA 27 Female
Okinawa, Japan 112 Male
Singapore, Singapore 33 Female
Name Phone number
John Smith 0332581111
Mary Williams 0332581111
Robert Jones 0423231111
Linda Taylor 0458603093
Records with rare data still
remain, which might result
in identification of
individuals.
Re-identification risk
remains!
After k-anonymization,
possibility of identifying
individual becomes at most
1/k
Address Age Gender
Tokyo, Japan 28 Male
NY, USA 27 Female
Wash., USA 25 Female
Okinawa, Japan 21 Male
LA, USA 25 Female
Address Age Gender
Japan Twenties Male
USA Twenties Female
USA Twenties Female
Japan Twenties Male
USA Twenties Female
e.g.
k = 2
• Technical challenges in k-anonymization are (1) how to reduce information loss accompanying
anonymization, and (2) how to reduce trial and error in operation.
• Hitachi has techniques for solving these problems.
What’s k-anonymization?
2-2-3. Privacy protection / k-Anonymization (1/2)
13
© Hitachi, Ltd. 2014. All rights reserved.
2-2-3. Privacy protection / k-Anonymization (2/2)
14
NameGender Age Cancer
F 40’s Stomach
F 40’s Liver
M 50’s or
more
Colon
M 30’s Colon
An
on
ym
izatio
n
Ge
ne
ratio
no
f Da
taH
iera
rch
y
Info
. Loss
Ca
lcu
latio
n
Pe
rso
na
lIn
fo.
An
on
ym
ized
In
fo.
4-Anonymization
Liver or Colon or …
*
Rectum
4
2
1
Colon
1
Colon or …
4
8
2
Automatic Generation
Name Gender Age Cancer
F 45 Stomach
F 40 Liver
M 98 Rectum
M 32 Colon
Mask
Can be identified from unmasked
data.
Complicate individual identification by specifying number (k) of persons who can be identified from their
unmasked data.
Conventional Methodk-Anonymization Technology
Ma
sk
Difficulty of identification can be controlled by
changing k.
k-anonymization technology to control re-identification risk of personal data for privacy-aware data usage.
Compared with previous methods, our method reduces information loss by more than 30% due to k-anonymization by using (1)automatic generalization of data hierarchy and (2) evaluation of information loss.
(1) (2)
© Hitachi, Ltd. 2014. All rights reserved.
B55BE115 EC4FA6BC
C1 = 128E(Abe) E1 = 6E7A(M)
C2 = 84D2(Ito) E2 = D0D5(M)
C3 = 24F9(Abe) E3 = EC2B(F)
・・・ ・・・
User(Data Owner)
Search word m Q(m)
[Ci,Di,Ei]
Data encrypted by searchable encryption
Preliminary: User encrypts his data and stores in cloud
cloud(Service Provider)
Highly secure query
For same words,
different queries1. Send encrypted query
2. Return found data
Name
High efficiency
Additional time cost by
encryption is negligible
C33A0B12
D1 = B22A(Tokyo)
D2 = 1AC3(Chiba)
D3 = 4T7A(Tokyo)
・・・
Search for 10,000 records Time ( client / transmission(intranet) / server )
Plaintext 8.2 ms (2.3/4.9/1.0 [ms])
Encrypted 16.3 ms (2.8/5.5/8.0 [ms])
Highly secure encryptionFor same plaintexts, different ciphertexts
Address Gender
Private Search
Check (Q(m),Ci)
=Y/N
2-2-3. Privacy protection / Searchable encryption
Searchable Encryption Scheme“Encryption”, “Decryption” + “Searching with data remaining encrypted ”
Hitachi Scheme High Efficiency: using symmetric-key encryption scheme that enables
high-speed processing
High Security: ensure that completely different ciphertexts are produced, even for same source data
15This work was supported by Ministry of Internal Affairs and Communications, Japan.
© Hitachi, Ltd. 2014. All rights reserved.
We have been constructing “disease progression model” using health checkup and claim data.
• Number of health checkups: 200,000• Number of claims: 1,600,000
Example of disease progression model
Obesity
Weight gain Eating
habits
Diabetes
Complications
High blood sugar
Exercise
XXX Patients over XX YearsXXX Patients over XX Years
100,000 Patients over 2 Years
Patient Information
Age
Gender
Weight
Height
Body fat
HbA1c
FPG
Exercise habits
Smoking habits
Etc…
Condition Status
Impaired Glucose Tolerance?
Impaired FPG?
High blood pressure?
Have diabetes?
Taking Oral Medication?
Using Insulin injections?
Using Insulin pump?
Diabetes foot disease?
Renal failure?
Etc…
Treatment Received
Blood test
Lifestyle intervention
Blood pressure medication
Oral diabetes medication
Glucose test
Insulin pens
Insulin pump
Foot amputation
Dialysis
Etc…
Visualized in 2D space, length of green lines indicate
relational strength of values
Source Data
• Circles denote health or disease status related to diabetes.
• Arrows denote direction of disease progression.
2-3-1. Disease progression modeling
16
© Hitachi, Ltd. 2014. All rights reserved.
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
5.5 5.7 5.9 6.1 6.3 6.5 6.7 6.9 7.1 7.3
1人当たりの
1
0
年間の
累積医療費抑制額(
円)
初期のHbA1c(%)
HbA1cが0.2%改善した場合10年後
9年後
8年後
7年後
6年後
5年後
4年後
3年後
2年後
1年後
Elapsed years
2000
2600
3300
4000
4600
1300
600
0
Initial HbA1c [%]
Cum
ula
tive s
avin
gs o
f 10 y
ears
[£/p
ers
on
]
Highly cost-effective
Effect of health guidance
in case of 0.2% HbA1c improvement
For 10 years
£3500
Medical cost simulation
in case of guidance for specific person
highly cost-effective
Intervention
5k
0
10k
Control
We have been developing medical cost simulation usingdisease progression model.
Simulation is used not only to estimate medical cost reduction, but to extract subjects for whom health promotion services are effective.
Cum
ula
tive C
ost
[£/p
ers
on
]
2-3-2. Medical cost simulation
17
© Hitachi, Ltd. 2014. All rights reserved.
2-4. Pharmaceutical industry applications
Analysis of medical needs for planning R&D of new drugs
Planning sales strategy by precise prediction of drug demand
Evaluation/comparison of effects and medical costs of drugs
Estimation of actual rate of adverse events of drug Early detection of adverse events and analysis of
contributing factors for minimizing risk
Promotion of open innovation by providing secure data sharing environment
Medical cost estimation
Adverse event analysis
Secure databasefor open
innovation
Marketing
18
© Hitachi, Ltd. 2014. All rights reserved. 19
Appendix
© Hitachi, Ltd. 2014. All rights reserved. 20
Utilizing IT for diabetes prevention and contribution to medical expenditure reduction
Enhance Quality of Life by Integrated Healthcare Platform
※1 National Health Service Greater Manchester ※2 General Practitioner
NHS GM and Hitachi Ltd. are promoting healthcare service
project utilizing IT (from Oct. 2013).
※1
Secure Integrated HealthcarePlatform Architecture
Adopt IT-based treatment for lifestyle-related diseases
GPGP GP
Research Institute
Hospital
Platform
Analytics DatabasePrivacy Enhancing
TechnologyAnalytics Application
70kg
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10日目 20日目 30日目0日目5/18( 木 )
改善実施度
改善実施度
イベントイベント
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ParticipantSelf-care
Operator
・Visualization・Life guidance
Advice
Glucose levelWeight
Exercise HistoryData analysis
& guide
※2
A1-1. UK NHS project
© Hitachi, Ltd. 2014. All rights reserved. 21
Adapting data analysis technology developed by Hitachi to disease prevention
programme in Salford, England
Adapting medical cost simulation technology, and developing cost effective
care plan
Issues- Manpower data management- Advice quality is variable
A1-2. Diabetes prevention
Providing cost effective care plan to people at high risk for Diabetes
Current lifestyle-related disease prevention programme
Enhanced lifestyle-related disease prevention programme
Issues- Recorded in paper- Invisible Effect
Validating Improvement of QoL (Quality of Life)by prevention of Diabetes and Reducing Care Cost through PoC
High riskPeople
Telecarer
Interview by form / phone
Advice based on each Telecarer’s decision
70kg
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改善実施度改善
実施度
イベントイベント
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High riskPeople
Telecarer
Effective and Efficient Advicebased on patient’s disease status
Blood SugarWeight
Daily Activityetc
Self-checkAdvice based onData analytics
- Visualize daily effect- Effective advice