Smart Data in Health – How we will exploit personal, clinical, and social “Big Health Data” for better outcomes

1
Smart Data in Health – How we will exploit
personal, clinical, and social “Big Health Data”
for better outcomes
Webinar given to Brain Health Alliance, June 30, 2015
Amit Sheth
Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
Wright State University, Dayton, Ohio
http://knoesis.org http://knoesis.org/amit/hcls
Special Thanks: Sujan Perera

Data Sources
Clinical
Diagnosis
Medications
Family History
Allergies
Patient History

Stakeholders
Medical Professionals Scientists Policy Makers
Patients

Makes Sense
Actionable or help decision support/making
7

Smart Data
Smart data makes sense out of Big data
It provides value from harnessing the
challenges posed by volume, velocity, variety
and veracity of big data, in-turn providing
actionable information and improve decision
making.

Healthcare Data Usage - Examples
• Support Research - Genomics and Beyond
The key to enable the personalized medicine and discoveries
• Transform Data to Information
Mine data for meaning and patterns/predictive analytics
• Support Self-Care
Mobile apps to keep track of your health status
• Support Providers - Improve Patient Care
Analyzing social and clinical data streams to create behavioral
health records
• Increase Awareness
Inform about epidemics, identifying counterfeit drugs, inform
about environmental issues

Few Success Stories
• IBM- Ontario’s Institute of Technology : predict the onset of nosocomial
infections 24 hours before symptoms appeared.
• University of Michigan Health System : reducing the need for blood
transfusions by 31 per cent and expenses by $200,000 a month.
• Kaiser Permanente : discovery of adverse drug effects and subsequent
withdrawal of the drug Vioxx from the market.
• Harvard Medical School : computer algorithms to analyze EHR data to
detect and categorize patients with diabetes for public health
surveillance.
• Seton Healthcare – IBM : bulging jugular vein is a strong—and easily
observed—predictor that a patient admitted for congestive heart failure
is likely to wind up back in the hospital.

Kno.e.sis Harness the Value
kHeath analyzes both active and passive observations of the patients to
generate the alarms that helps to improve health, fitness, and wellbeing
of the patient. It uses Semantic Sensor Web technology, Semantic
Perception, and Intelligence at the Edge to enable sophisticated analysis
of personal health observations.
kHealth
Data Sources
kHealth Wiki

The overall aim of PREDOSE is to develop techniques to facilitate
prescription drug abuse epidemiology, related to the illicit use of
pharmaceutical opioids. PREDOSE is designed to capture the knowledge,
attitudes and behaviors of prescription drug abusers through the
automatic extraction of semantic information from social media.
PREDOSE
Data Sources
PREDOSE Wiki

eDrugTrends is social media data analytics platform to monitor the
cannabis and synthetic cannabinoids usage. It uses Twitter and Web
forums data to: 1) Identify and compare trends in knowledge, attitudes,
and behaviors related to cannabis and synthetic cannabinoid, and 2)
Identify key influencers in cannabis and synthetic cannabinoid-related
discussions on Twitter.
eDrugTrends
Data Sources
eDrugTrends Wiki

This project seeks to understand and satisfy users’ need for keeping track
of new information in healthcare and well-being. The project harvest
collective intelligence to identify high quality, reliable and informative
healthcare content shared over social media based on following
analysis: Text Analysis, Semantic analysis, Reliability analysis, Popularity
Analysis.
Social Health Signals
Data Sources
Social Health Signals Wiki

Technology Stack
EMRSensor
Explicit/Implicit Entity Recognition Understanding Language NuancesNoise Filtering
Entity Disambiguation Sentiment Extraction
Spatial Information Extraction
Knowledge Extraction Semantic Perception
Time Series AnalysisPredictive Analytics Semantic Analysis
Social Network Analysis
Temporal Information Extraction
Data Integration

Kno.e.sis Strength
The research at Kno.e.sis fundamentally believe that the
‘knowledge about the world and the problem domain’
has critical role to play in solving the complex real world
problems. Hence, our technologies always exploit the
background knowledge available to overcome the unique
challenges posed by the problem at hand.
Computationally we seek to combine bottom
brain and top brain inspired computing.

Knowledge
World Knowledge
Medical Knowledge Linguistic Knowledge

Few Examples of Our Technologies

21
Sujan Perera, Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth, Suhas Nair, 'Semantics Driven Approach for Knowledge Acquisition from
EMRs', Special Issue on Data Mining in Bioinformatics, Biomedicine and Healthcare Informatics, Journal of Biomedical and Health Informatics
(To Appear)
Intuition: Knowledge is built by abstracting real world
facts, once built it should be able to explain the real world
Public Knowledge is not always Sufficient
Semantics Driven Approach for
Knowledge Acquisition from EMRs

Explanation
Module
Explained?
Yes
No
Hypothesis
Filtering
Hypothesis
Generation
Hypothesis
with High
Confidence
D
D D
DD
D
Patient Notes
UMLS
Knowledge Acquisition

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1. Annotate the EMR documents with given knowledgebase
2. Find unexplained symptoms
3. Generate hypothesis for unexplained symptoms
1. All disorders in document becomes candidates
4. Filter out candidate disorder with high confidence
1. Get disorders which has relationship with unexplained
symptom in given knowledgebase
2. Collect the “neighborhood” of the disorders
3. Get the intersection of “neighborhood” and candidate
disorders
Knowledge Acquisition - Algorithm

Implicit Entity Recognition
Bob Smith is a 61-year-old man referred by Dr. Davis for outpatient cardiac
catheterization because of a positive exercise tolerance test. Recently, he started to have
left shoulder twinges and tingling in his hands. A stress test done on 2013-06-02
revealed that the patient exercised for 6 1/2 minutes, stopped due to fatigue. However,
Mr. Smith is comfortably breathing in room air. He also showed accumulation of fluid in
his extremities. He does not have any chest pain.
Person Person
UMLS:
C0018795
UMLS:
C0008031
UMLS:
C0015672
Named Entity Recognition (gives type)
Co-reference Resolution
Negation Detection
Entity Linking
Temporal Information Extraction

Bob Smith is a 61-year-old man referred by Dr. Davis for outpatient cardiac
catheterization because of a positive exercise tolerance test. Recently, he started to have
left shoulder twinges and tingling in his hands. A stress test done on 2013-06-02
revealed that the patient exercised for 6 1/2 minutes, stopped due to fatigue. However,
Mr. Smith is comfortably breathing in room air. He also showed accumulation of fluid in
his extremities. He does not have any chest pain.
Shortness of breath - negated
edema
Shortness of breath : uncomfortable sensation of difficulty in breathing
Edema : excessive accumulation of fluid

Implicit Entity Recognition (IER) is the task of determining
whether a sentence, which does not contain the proper
name of an entity, nevertheless refers to the entity.
Sujan Perera, Pablo Mendes, Amit Sheth, Krishnaprasad Thirunarayan, Adarsh Alex, Christopher Heid, Greg Mott, 'Implicit Entity
Recognition in Clinical Documents', In proceedings of The Fourth Joint Conference on Lexical and Computational Semantics (*SEM), 2015

Sentence Entity
Her breathing is still uncomfortable. Shortness of breath
It is important to prevent shortness of breath and lower extremity swelling
from fluid accumulation.
Edema
She says she did not have any warning prior to losing consciousness and
remembers everything.
Syncope
His tip of the appendix was inflamed. Appendicitis
There is a 1.3 cm gallstone within the gallbladder neck which is not obstructing. Cholecystitis
Sujan Perera, Pablo Mendes, Amit Sheth, Krishnaprasad Thirunarayan, Adarsh Alex, Christopher Heid, Greg Mott, 'Implicit
Entity Recognition in Clinical Documents', In proceedings of The Fourth Joint Conference on Lexical and Computational
Semantics (*SEM), 2015

Semantic Perception
Making sense of sensor data with

SSN
Ontology
2 Interpreted data
(deductive)
[in OWL]
e.g., threshold
1 Annotated Data
[in RDF]
e.g., label
0 Raw Data
[in TEXT]
e.g., number
Levels of Abstraction
3 Interpreted data
(abductive)
[in OWL]
e.g., diagnosis
Intellego
“150”
Systolic blood pressure of 150 mmHg
Elevated
Blood
Pressure
Hyperthyroidism
……
31

* based on Neisser’s cognitive model of perception
Observe
Property
Perceive
Feature
Explanation
Discrimination
1
2
Translating low-level signals
into high-level knowledge
Focusing attention on those
aspects of the environment that
provide useful information
Prior Knowledge
32
Perception Cycle*

W3C Semantic Sensor
Network (SSN) Ontology Bi-partite Graph
33
Prior Knowledge on the Web

Observe
Property
Perceive
Feature
Explanation
1
Translating low-level signals
into high-level knowledge
Explanation is the act of choosing the objects or events that best account for a
set of observations; often referred to as hypothesis building
34
Explanation

Inference to the best explanation
• In general, explanation is an abductive problem; and
hard to compute
Finding the sweet spot between abduction and OWL
• Single-feature assumption* enables use of OWL-DL
deductive reasoner
* An explanation must be a single feature which accounts for
all observed properties
Explanation is the act of choosing the objects or events that best account for a set of
observations; often referred to as hypothesis building
35
Explanation

elevated blood pressure
clammy skin
palpitations
Hypertension
Hyperthyroidism
Pulmonary Edema
Observed Property Explanatory Feature
36
Explanation

Discrimination is the act of finding those properties that, if observed, would help distinguish
between multiple explanatory features
Observe
Property
Perceive
Feature
Explanation
Discrimination
2
Focusing attention on those
aspects of the environment that
provide useful information
37
Discrimination

elevated blood pressure
clammy skin
palpitations
Hypertension
Hyperthyroidism
Pulmonary Edema
Discriminating Property Explanatory Feature
38
Discrimination

Few Real World Problems that
Kno.e.sis Solves

Through physical monitoring and
analysis, our cellphones could act as
an early warning system to detect
serious health conditions, and
provide actionable information
canary in a coal mine
Empowering individuals for their own health
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kHealth

What?
• kHealth is a knowledge-based approach/application
for patient-centric health-care that exploits:
(a) Web based tools and social media,
(b) Mobile phone technology and wireless sensors,
(c) For synthesizing personalized actions from heterogeneous
health data
(i) For disease prevention and treatment
(ii) For health, fitness and well-being
41
kHealth

kHealth – Applications & Impact
Condition Number of patients Total cost per year
Asthma 25 million 50 billion
ADHF 5 million 34 billion
Parkinson’s disease 1 million 25 billion

Sensordrone
(Carbon monoxide,
temperature, humidity)
Node Sensor
(exhaled Nitric
Oxide)
43
Sensors
Android Device
(w/ kHealth App)
Total cost: ~ $500
*Along with two sensors in the kit, the application uses a variety of population level signals from the web:
Pollen level Air Quality
Temperature & Humidity
kHealth – Asthma Patient Kit

Personal level
Signals
Public level
Signals
Population level
Signals
Domain
Knowledge
Risk Model
Events from
Social Streams
Take Medication before
going to work
Avoid going out in the
evening due to high pollen
levels
Contact doctor
Analysis
Personalized
Actionable
Information
Data Acquisition &
aggregation
44
Health Signal Processing Architecture

45
Risk assessment
model
Semantic
Perception
Personal level
Signals
Public level
Signals
Domain
Knowledge
Population level
Signals
GREEN -- Well Controlled
YELLOW – Not well controlled
Red -- poor controlled
How controlled is my asthma?
Patient Health Score (Diagnostic)

46
Risk assessment
model
Semantic
Perception
Personal level
Signals
Public level
Signals
Domain
Knowledge
Population level
Signals
Patient health
Score
How vulnerable* is my control level today?
*considering changing environmental conditions and current control level
Patient Vulnerability Score (Prognostic)

47
Population Level
Personal
Wheeze – Yes
Do you have tightness of chest? –Yes
ObservationsPhysical-Cyber-Social System Health Signal Extraction Health Signal Understanding
<Wheezing=Yes, time, location>
<ChectTightness=Yes, time, location>
<PollenLevel=Medium, time, location>
<Pollution=Yes, time, location>
<Activity=High, time, location>
Wheezing
ChestTightness
PollenLevel
Pollution
Activity
Wheezing
ChestTightness
PollenLevel
Pollution
Activity
RiskCategory
<PollenLevel, ChectTightness, Pollution,
Activity, Wheezing, RiskCategory>
<2, 1, 1,3, 1, RiskCategory>
.
.
.
Expert
Knowledge
Background
Knowledge
Tweets reporting pollution level
and asthma attacks
Acceleration readings from
on-phone sensors
Sensor and personal
observations
Signals from personal, personal
spaces, and community spaces
Risk Category assigned by
doctors
Qualify
Quantify
Enrich
Outdoor pollen and pollution
Public Health
Well Controlled - continue
Not Well Controlled – contact nurse
Poor Controlled – contact doctor
Health Signal Extraction to Understanding

D. Cameron, G. A. Smith, R. Daniulaityte, A. P. Sheth, D. Dave, L. Chen, G. Anand, R. Carlson, K. Z. Watkins, R. Falck. PREDOSE: A Semantic Web
Platform for Drug Abuse Epidemiology using Social Media. Journal of Biomedical Informatics. July 2013 (in press)
Kno.e.sis - Ohio Center of Excellence in Knowledge-enabled Computing
CITAR - Center for Interventions Treatment and Addictions Research
http://wiki.knoesis.org/index.php/PREDOSE
Bridging the gap between researcher and policy
makers
Early identification of emerging
patterns and trends in abuse
PREDOSE

In 2008, there were 14,800 prescription painkiller
deaths*
*http://www.cdc.gov/homeandrecreationalsafety/rxbrief/
• Drug Overdose Problem in US
• 100 people die everyday from drug overdoses
• 36,000 drug overdose deaths in 2008
• Close to half were due to prescription drugs
Gil Kerlikowske
Director, ONDCP
Launched May 2011
PREDOSE

PREDOSE
Early Identification and
Detection of Trends
Access hard-to-reach
Populations
Large Data Sample Sizes
Group Therapy: http://www.thefix.com/content/treatment-options-prison90683
Interviews
Online Surveys
Automatic Data
Collection
Not Scalable
Manual Effort
Sample Biases
Epidemiologist
Qualitative Coding
Problems
Computer Scientist
Automate Information
Extraction & Content Analysis

Web
Crawler
Informal Text DatabaseWeb Forums
2
4
5
8
Data Cleaning
Stage 1. Data Collection
3
Stage 2. Automatic Coding
Stage 3. Data Analysis and Interpretation
1
6
Qualitative and Quantitative Analysis
of Drug User Knowledge, Attitudes
and Behaviors
+ =
Semantic Web Database
Information Extraction Module
Temporal Analysis for Trend Detection
10
Triples/RDF Database
Entity
Identification
Sentiment
Extraction
Relationship
Extraction
Triple Extraction
7
Opioid, Cannabinoid,
Side Effect, Feeling
[Buprenorphine has_slang_term bupe]
[Suboxone subClassOf Buprenorphine]
[Suboxone_Injection CAUSES Nausea]
Drug Abuse Ontology
(Schema)
9
PREDOSE Web Application
9

I was sent home with 5 x 2 mg Suboxones. I also got a bunch of phenobarbital (I
took all 180 mg and it didn't do shit except make me a walking zombie for 2 days).
I waited 24 hours after my last 2 mg dose of Suboxone and tried injecting 4 mg
of the bupe. It gave me a bad headache, for hours, and I almost vomited. I could
feel the bupe working but overall the experience sucked.
Of course, junkie that I am, I decided to repeat the experiment. Today, after
waiting 48 hours after my last bunk 4 mg injection, I injected 2 mg. There wasn't
really any rush to speak of, but after 5 minutes I started to feel pretty damn good.
So I injected another 1 mg. That was about half an hour ago. I feel great now.
Codes Triples (subject-predicate-object)
Suboxone used by injection, negative experience Suboxone injection-causes-Cephalalgia
Suboxone used by injection, amount Suboxone injection-dosage amount-2mg
Suboxone used by injection, positive experience Suboxone injection-has_side_effect-Euphoria
Triples
DOSAGE PRONOUN
INTERVAL Route of Admin.
RELATIONSHIPS SENTIMENTS
DIVERSE DATA TYPES
ENTITIES
Buprenorphine
subClassOf
bupe
Entity Identification
has_slang_term
SuboxoneSubutex
subClassOf
bupey
has_slang_term
Drug Abuse Ontology (DAO)
83 Classes
37 Properties
33:1 Buprenorphine
24:1 Loperamide
feel pretty damn good
feel great
Sentiment Extraction
+ve
experience sucked
didn’t do shit
-ve
bad headache

Ontology Lexicon Lexico-ontology Rule-based Grammar
ENTITIES
TRIPLES
EMOTION
INTENSITY
PRONOUN
SENTIMENT
DRUG-FORM
ROUTE OF ADM
SIDEEFFECT
DOSAGE
FREQUENCY
INTERVAL
Suboxone, Kratom, Herion,
Suboxone-CAUSE-Cephalalgia
disgusted, amazed, irritated
more than, a, few of
I, me, mine, my
Im glad, turn out bad, weird
ointment, tablet, pill, film
smoke, inject, snort, sniff
Itching, blisters, flushing,
shaking hands, difficulty
breathing
DOSAGE: <AMT><UNIT>
(e.g. 5mg, 2-3 tabs)
FREQ: <AMT><FREQ_IND><PERIOD>
(e.g. 5 times a week)
INTERVAL: <PERIOD_IND><PERIOD>
(e.g. several years)
PREDOSE: Smarter Data through Shared Context
and Data Integration

55
dose of 16 mg per day. For example, web forum participants shared the following opinions:
“Back in the day when I would run out of pills early I would take 8-10 Lopermide tabs and
get some pretty good relief from w/d.”
“If you take a shitload of loperamide like 10-20 pills at once in withdrawal, you’ll get relief
from some of the physical symptoms. Im not sure exactly how it works, but it’s definitely
MORE than just relieving the GI symptoms. Im guessing if you just bombard your blood
with it, SOME of it has to make it through? Not sure.”
“Normally around 100 milligrams of loperamide will get me out of withdrawals.”
“Loperamide alone is enough to keep me well without being miserable, IF I megadose.”
“This loperamide has saved my life during w/ds.... and made me even more careless
with my monthly meds.”
Loperamide is used to self-medicate to from Opioid Withdrawal symptoms
with it, SOME of it has to make it through? Not sure.”
“Normally around 100 milligrams of loperamide will get me out of withdrawals.”
“Loperamide alone is enough to keep me well without being miserable, IF I megadose.”
“This loperamide has saved my life during w/ds.... and made me even more careless
with my monthly meds.”
“But I just wanted to tell you that loperamide WILL WORK. I take 105 mg of
methadone/day, and recently have been running out early due to a renewed interest in
IVing that shit. 200mg of lope 100 pills will make me almost 100 again. It brings the
sickness down to the level of, say, a minor flu. Sleep returns, restlessness dissipates.
Sometimes a mild opiation is felt.”
“So you just stick with it. Don’t go and score big with your next paycheck. Overcome the
need to make everything numb. Learn to live with normality for a while. It’ll all seem
worthwhile soon enough. Go for a walk. Get out of the house. Go grab some loperamide
from the store, the desperate junky’s methadone.”
The most commonly discussed side effects of loperamide use were constipation, dehydration
and other types of gastrointestinal discomforts. Some also reported mild withdrawal symptoms
from using loperamide for an extended period of time.
“Loperamide is good for a day or two but the problem is on loperamide I lose all desire to
eat OR drink, or do anything really.”
“I used to sing the praises of loperamide....and still do, as a short term standby until you
can score. Long term maintenance, it really wears you out. Starts to “feel” toxic though I
PREDOSE: Loperamide-Withdrawal Discovery

http://www.internetlivestats.com/internet-users/
Around 3 Billions (40%) of the
world population
Around 300 Million (87 %) of
the US population

• Online health resources
– Easily accessible
– Helps to obtain medical information quickly, conveniently
– Can help non-experts to make more informed decisions
– Play a vital role in improving health literacy

• With the growing availability of online health resources,
consumers are increasingly using the Internet to seek health
related information
• Most queries are initiated in search engines
According to a 2013 Pew
Survey*, one in three
American adults has gone
online to find information
about a specific medical
condition.
*Fox S, Duggan M. Pew Internet & American Life Project. 2013. Health online 2013

Social Health Signals - Motivation
• Analyzing health search log
– Helps to understand population level health information
needs
– How users formulate search queries (“expression of
information need”)
– availability of potentially larger, cohorts of real users and
their behaviors, e.g. querying behaviors
• Such knowledge can be applied
– to improve the health search experience
– to develop next-generation knowledge and content
delivery systems

Social Health Signals - Studies
• Online information seeking: Personal computer vs Smart
devices
• What information about the cardiovascular disease do people
search
vs.

• Comparative analysis of online health information seeking for
chronic diseases
• Analyzing temporal patterns of the online health seeking
Cardiovascular
Diseases
Arthritis
Cancer
Diabetes

• Analyzing online information seeking for “Food and Diet” in
the context of health

• Identification of users intent for health information
seeking
• Using background knowledge based to develop a rule
based classification approach
– Using UMLS MetaMap and based on UMLS concepts and
semantic types
– To categorize CVD search queries into 14 “consumer
oriented” health categories
Research Problem

eDrugTrends
• eDrugTrends is a software platform developed to
semi-automate the processing and visualization of
thematic, sentiment, spatio-temporal, and social
network dimensions on cannabis and synthetic
cannabinoid use.
• This built on top of our existing analytics platforms
Twitris and PREDOSE.

eDrugTrends - Significance
• eDrugTrends advance the the field’s
technological and methodological capabilities
to harness social media for drug abuse
surveillance research.
• eDrugTrends informs the field on new trends
regarding the use of cannabis and synthetic
cannabinoid usage.

eDrugTrends – Preliminary Study
• We studied the differences in volume of hash
oil (form of cannabis) related tweets among
varying cannabis legalization policies.
• We studies the attitudes about the use of
hash oil products.

eDrugTrends – Data Set
• ~18,000 Tweets in early October from ~14,500
users.
• 20% contains identifiable state level
geolocations.
• Examples
If you smoke spice and you live in a Weed Legal state.... You are trash
Tried my first dab Tuesday night. Best sleep I've had in a while. Too bad dabs
are too expensive for me.
I used to smoke k2 all the time when my bestfriend was on papers‚ then I
almost died n never touched it again.

eDrugTrends – Early Findings
• Tweets related to hash oil are highest in the
states that have passed medical and
recreational usage of cannabis.
• The users have high positive attitude towards
the cannabis usage in such states.
• These finding will help to develop intervention
and policy responses.

Thank You
Visit Us @
www.knoesis.org
with additional background at http://knoesis.org/amit/hcls

Ohio Center of Excellence in Knowledge-enabled Computing -
An Ohio Center of Excellence in BioHealth Innovation
Wright State University

Amit Sheth’s
PHD students
Ashutosh Jadhav
Hemant
Purohit
Vinh
Nguyen
Lu Chen
Pavan
Kapanipathi
Pramod
Anantharam
Sujan
Perera
Alan Smith
Pramod Koneru
Maryam Panahiazar
Sarasi Lalithsena
Cory Henson
Kalpa
Gunaratna
Delroy
Cameron
Sanjaya
Wijeratne
Wenbo
Wang
Kno.e.sis in 2012 = ~100 researchers (15 faculty, ~50 PhD students)

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