Healthcare innovations at Kno.e.sis sept2016

Healthcare Innovations at Kno.e.sis
Put Knoesis Banner
Amit Sheth
Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis):
an Ohio Center of Excellence in BioHealth Innovation
Wright State University, USA

Quick Intro to Kno.e.sis
• Ohio Center of Excellence in BioHealth innovation
• Highly multidisciplinary: Computer Science,
Cognitive Science, Clinical, Biomedical,
Community Health, Epidemiology,…
• Foundational research to Real-world (commercial
products, deployed applications, open source
tools, IP, start ups)
• Exceptional success for graduates
• WSU appears in top 10 academic institutions in
the world in WWW (for 10 yr impacts) due to our
work
2

Top organization in WWW: 10-yr Field Rating
3

• Social Media Big Data – Twitris, eDrugTrends
• Sensor/IoT Big Data – CityPulse, kHealth
• Healthcare Big Data – kHealth, EMR, Prediction
• Biomedical Big Data –SCOONER, (drug
repurposing)
• Big and Smart Data Certificate
Kno.e.sis private cloud: 864 CPU cores, 18TB RAM, 17TB SSD,
435TB disk
5

• 80% of doctors will eventually become obsolete: Vinod
Khosla, VC and founder of Sun Microsystems
• “The Doctor is (Always) In: Reinventing the Doctor-
Patient Relationship for the 21st Century” [Dr. J.
Shlain]. More data is generated under patient control
and outside clinical system. Patient empowerment,
reimbursement changes and AHA.
• #dHealth and #IoT are two hottest hashtags at CES and
SXSW
6
Healthcare is changing way too fast

7
Healthcare Innovation at Kno.e.sis
(with subset of applications)
Personalized Digital Health

8
• Prescription Drug Abuse / Toxicology (Social Media Analysis, R21 & R56)-completed
• Asthma in Children (Personalized Digital Health, NIH R01)
• Dementia (Personalized Digital Health, NIH K01)
• Marijuana Legalization (Social Media Analysis, R01)
• Healthcare Utilization – Depression (Social Media, R01)
• Musculoskeletal injury reduction (Clinical Notes analysis, SBIR)
• Computer Assisted Coding/Computerized Document Improvement (EMR,
commercially deployed)
• Healthcare Annotation/Text Analysis API (Clinical Notes/Text, R&D)
• Readmission of ADHF patients (Personalized Digital Health)
• Readmission of GI Patients (Personalized Digital Health)
• CV patient discharge outcome prediction (Predictive Health) - preliminary
• Diabetes Progression Prediction (Predictive Health) – preliminary
• NextGen Sequencing Data Semantic Annotation & Analysis for Cancer - preliminary
And several others…
Diseases/Health Apps we work with &/or target

The Patient of the Future
MIT Technology Review, 2012
http://www.technologyreview.com/featuredstory/426968/the-patient-of-the-future/ 10

11
kHealth:
Knowledge empowered personalized
digital mhealth
With applications to: Asthma, Dementia,
ADHF, GI, (other chronic disease)
Contact: Prof. Amit Sheth

13
Providing actionable information in a timely manner is
crucial to avoid information overload or fatigue
Sleep data
Community data
Personal
Schedule Activity data
Personal health
records
Data Overload for Patients/health aficionados

Current Trials/Evaluations
• Managing Asthma in Children [ongoing, R01]
• Dementia – adverse event prediction[ongoing,
K01]
• Reducing ADHF readmission
• Reducing readmission of GI surgery patients
• Excellent potential for chronic disease
management (COPD, Obesity, …)
14

15
1http://www.nhlbi.nih.gov/health/health-topics/topics/asthma/
2http://www.lung.org/lung-disease/asthma/resources/facts-and-figures/asthma-in-adults.html
3Akinbami et al. (2009). Status of childhood asthma in the United States, 1980–2007. Pediatrics,123(Supplement 3), S131-S145.
25
million
300
million
$50
billion
155,000
593,000
People in the U.S. are
diagnosed with asthma
(7 million are children)1.
People suffering from
asthma worldwide2.
Spent on asthma alone
in a year2
Hospital admissions in
20063
Emergency department
visits in 20063
Asthma

Asthma is a multifactorial disease with health signals spanning personal,
public health, and population levels.
16
Real-time health signals from personal level (e.g., Wheezometer, NO in breath,
accelerometer, microphone), public health (e.g., CDC, Hospital EMR), and
population level (e.g., pollen level, CO2) arriving continuously in fine grained
samples potentially with missing information and uneven sampling frequencies.
Variety Volume
VeracityVelocity
Value
Can we detect the asthma severity level?
Can we characterize asthma control level?
What risk factors influence asthma control?
What is the contribution of each risk factor?semantics
Understanding relationships between
health signals and asthma attacks
for providing actionable information
WHY Big Data to Smart Data?
Healthcare example

Sensordrone
(Carbon monoxide,
temperature, humidity)
Node Sensor
(exhaled Nitric Oxide)
17
Sensors
Android Device
(w/ kHealth App)
Total cost: ~ $500
kHealth Kit for the application for Asthma management
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

18
kHealth to Manage ADHF
(Acute Decompensated Heart Failure)

19
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
ChectTightness
PollenLevel
Pollution
Activity
Wheezing
ChectTightness
PollenLevel
Pollution
Activity
RiskCategory
<PollenLevel, ChectTightness, Pollution,
Activity, Wheezing, RiskCategory>
<2, 1, 1,3, 1, RiskCategory>
.
.
.
Expert
Knowledge
Background
Knowledge
tweet 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

20
Social streams has been used to extract
many near real-time events
Twitter provides access to rich signals but is noisy,
informal, uncontrolled capitalization, redundant,
and lacks context
We formalize the event extraction from tweets as
a sequence labeling problem
How do we know the event phrases and who creates
the training set? (manual creation is ruled out)
Now you know why you’re miserable! Very High Alert
for B-ALLERGEN Ragweed I-ALLERGEN pollen. B-FACILITY Oklahoma
I-FACILITY Allergy I-FACILITY Clinic says it’s an extreme exposure situation
Idea: Background knowledge used to create the training set
e.g., typing information becomes the label for a concept
Health Signal Extraction Challenges

Asthma Control => Daily Medication
Choices for starting
therapy
Not Well Controlled Poor Controlled
Severity Level of
Asthma
(Recommended Action) (Recommended Action) (Recommended Action)
Intermittent Asthma SABA prn - -
Mild Persistent Asthma Low dose ICS Medium ICS Medium ICS
Moderate Persistent
Asthma
Medium dose ICS alone
Or with
LABA/montelukast
Medium ICS +
LABA/Montelukast
Or High dose ICS
Medium ICS +
LABA/Montelukast
Or High dose ICS*
Severe Persistent Asthma High dose ICS with
LABA/montelukast
Needs specialist care Needs specialist care
ICS= inhaled corticosteroid, LABA = inhaled long-acting beta2-agonist, SABA= inhaled short-acting beta2-agonist ;
*consider referral to specialist
Asthma Control
and Actionable Information
Sensors and their observations
for understanding asthma
21
Personal, Public Health, and Population Level
Signals for Monitoring Asthma

22
At Discharge
Health Score Non-compliance Poor economic
status
No living
assistance
Vulnerability
Score
Well Controlled Low
Well Controlled Very low
Not Well
Controlled
High
Not Well
Controlled
Medium
Poor Controlled Very High
Poor Controlled High
Estimation of readmission vulnerability based on the personal health score
Personal Health Score and Vulnerability Score

How is Jack
doing
today?
How is
Mary’s
stress level
today?
Any signs of
abnormal
behavior
today?
Data Information Knowledge (Actionable
Information)
Wisdom
Wandering Depression Apathy
Aggression
Night-time
Disturbance
Agnosia
Toileting Paranoia
Stress Depression Tearful
Difficulty
sleeping
Tired Anxiety Irritability Overreaction
PwD
Symptoms
Cg
Symptom
s
t0 t
1
…
tn

26
PREDOSE:
Social media analysis driven
epidemiology
Application: Prescription drug abuse and beyond
Contact: Delroy Cameron

27
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: Prescription Drug abuse Online
Surveillance and Epidemiology

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: Prescription Drug abuse Online
Surveillance and Epidemiology
28

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
PREDOSE: Bringing Epidemiologists and Computer
Scientist together
29

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30

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
experience sucked
feel pretty damn good
didn’t do shit
feel great
Sentiment Extraction
bad headache
+ve
-ve
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
31

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
32

34
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
Loperamide-Withdrawal Discovery

35
EMR and clinical text analysis:
Intelligence from clinical data
Contact: Sujan Parera

• Active Semantic EMR: high quality, low error, faster
completion of patient records
• Predicting patient outcomes and advice discharge decisions
based on both structured (billing) data and clinical text
(unstructured data)
• Deep understanding of clinical text for Computer Assisted
Coding for ICD9 and ICD10 and Computerized Document
Improvement (commercial products from ezDI)
36

Explanation
Module
Explained?
Yes
No
Hypothesis
Filtering
Hypothesis
Generation
Hypothesis
with High
Confidence
D
D D
DD
D
Patient Notes
UMLS
Semantic Driven Approach for Knowledge Acquisition
from EMRs

Deep clinical text analysis using semantics enhanced NLP has
enabled our industry partner ezDI to develop exciting commercial
products: ezCDI (Computerized Document Improvement) and
ezCAC (Computer Assisted ICD9/ICD10 Coding)
See: http://ezdi.us
Semantics enhanced NLP
38

cTAKES
ezNLP
ezKB
<problem value="Asthma" cui="C0004096"/>
<med value="Losartan" code="52175:RXNORM" />
<med value="Spiriva" code="274535:RXNORM" />
<procedure value="EKG" cui="C1623258" />
ezFIND ezMeasure ezCDIezCAC
www.ezdi.us
ezHealth Platform
42

43
Social Health
Signals
Contact: Ashutosh Jadhav

• Everyday millions of health related tweets shared
• Most of these tweets are highly personal and contextual
• Only around 12% posts are informative*
• Keyword-based search doesn't help
• User has to manually identify informative tweets
How to automate the identification of informative content?
44
Problem: Identifying Signals from Noise

Present high quality, reliable and informative health related
information shared over social media by understanding
45
Who
who shared the information?
social network user People Analysis
share what
what content is shared? social
media post Content Analysis
when when the post is generated? Temporal Analysis
in what context what is the topic of the message? Semantic Analysis
on which
channel
To which website, the social
media post is pointing? Reliability Analysis
with what social
effect
how many retweets, facebook
like/share, comments for the
post?
Popularity Analysis
Social Health Signals

46
Search and
Explore
Top health
news
Faceted search (by
health topics)
Social Health Signals

kHealth - Asthma
Principal Investigators: Amit P. Sheth
Co-Investigators: Krishnaprasad Thirunarayan , Maninder Kalra
Other Faculty: Tanvi Banerjee
Students: Utkarshini Jaimini, ….
Ohio Center of Excellence in Knowledge-Enabled Computing
Grant Number: 1 R01 HD087132-01
Project Title: KHealth: Semantic Multisensory Mobile Approach to
Personalized Asthma Care
Timeline: 07/01/2016 – 06/30/2019
Award Amount: $938,725

kHealth - Dementia
Principal Investigators: Tanvi Banerjee
Mentors: Amit Sheth, Larry Lawhorne
Students: ….
Grant Number: 1K01LM012439-01
Project Title: Managing Dementia through Multisensory Smart Phone
Approach to Support Aging in Place
Timeline: 09/01/2016 – 08/30/2019
Award Amount: $509,909

Context-Aware Harassment
Detection on Social Media
Principal Investigators: Prof. Amit P. Sheth
Co-Investigators: Valerie Shalin, Krishnaprasad Thirunarayan
Other Faculty: Debra Steele-Johnson, Dr. Jack L. Dustin
PhD Students: Lu Chen, Wenbo Wang, Monireh Ebrahimi, Kathleen Renee Wylds
MS Students: Pranav Karan, Rajeshwari Kandakatla
Collaboration with Beavercreek High School
 NSF Award#: CNS 1513721
 TWC SBE: Medium: Context-Aware Harassment Detection on Social Media
 Timeline: 01 Sep. 2015 - 31 Aug. 2018
 Award Amount: $925,104 + $16,000 (REU)

eDrug Trends
Principal Investigators: Prof. Amit P. Sheth, Prof. Raminta Daniulaityte
Co-Investigators: Robert Carlson, Krishnaprasad Thirunarayan, Ramzi Nahhas,
Silvia Martins (Columbia), Edward W. Boyer (U. Mass.)
PhD Students: Farahnaz Golroo, Sanjaya Wijeratne, Lu Chen, Adarsh Alex
MS Student: Adarsh Alex
Postdoctoral Researcher: Francois Lamy
Software Engineer: Gary Smith
 NIH Award#: 5 R01 DA039454-02
 Trending: Social media analysis to monitor cannabis and synthetic cannabinoid use
 Timeline: 15 Sep. 2014 - 14 Sep. 2018
 Award Amount: $1,689,019 + $162,505

Social and Physical Sensing Enabled Decision Support for Disaster
Management and Response
Principal Investigators: Prof. Amit P. Sheth, Prof. Srinivasan Parthasarathy (OSU)
Co-Principal Investigators: Densheng Liu (OSU), Ethan Kubatko (OSU), Valerie Shalin,
Krishnaprasad Thirunarayan
PhD Students: Sarasi Lalithsena, Pavan Kapanipathi, Hussein Olimat
MS Student: Siva Kumar
Postdoctoral Researcher: Tanvi Banerjee
 NSF Award#: EAR 1520870
 Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster
Management and Response
 Timeline: 01 Jul. 2015 - 31 Jul. 2019
 Award Amount: $1,975,000 (WSU: $787,500)

Modeling Social Behavior for
Healthcare Utilization in Depression
Principal Investigators: Prof. Amit P. Sheth, Prof. Jyotishman Pathak (Cornell)
Co-Investigators: Krishnaprasad Thirunarayan, Tanvi Banerjee, William V. Bobo (Mayo Clinic),
Nilay D Shah (Mayo Clinic), Lila J Rutten (Mayo Clinic), Jennifer B McCormick (Mayo Clinic),
Gyorgy Simon (Mayo Clinic)
Other Faculty: Debra Steele-Johnson, Jack Dustin
PhD Students: Ashutosh Jadhav, Amir Hossein Yazdavar, Hussein Al-Olimat
Master Student: Surendra Marupudi
Visiting Scholar: SoonJye Kho
 NIH Award#: 1 R01 MH105384-01A1
 Modeling Social Behavior for Healthcare Utilization in Depression
 Timeline: 1 Jul. 2015 - 30 Jun. 2019
 Award Amount: $1,934,525 (WSU: $505,600)

Additional Funded Projects (when Kno.e.sis faculty is a PI/jointPI*)
● NMR-Based Urinary Metabolomics in Rats Exposed to Burn Pit Emissions and
Respirable Sand, $241K, Reo, Raymer
● PFI: AIR-TT: Market-driven Innovations and Scaling up of Twitris - A System for
Collective Social Intelligence; 200K, Sheth, Mackay
● CRII: CSR: Towards Understanding and Mitigating the Impact of Web Robot Traffic
on Web Systems; 174K, Doran
● Medical Information Decision Assistance and Support; 25K, Prasad, Sheth
● Choose Ohio First: Growing the STEMM Pipeline in the Dayton Region
FY2016/FY2017; Raymer
● Westwood Partnership to Prevent Juvenile Repeat Violent Offenders; $200K,
Sheth, Doran, Dustin
● Semantic Web-based Data Exchange and Interoperability for OEM-Supplier
Collaboration; 89K, Prasad, Sheth
● NIDA National Early Warning System Network (iN3): An Innovative Approach;
299K, Carlson, Sheth, Boyer, Daniulaityte, Nahas
● CUTE: Instructional Laboratories for Cloud Computing Education; 200K, Chen,
Wang, Mateti
● SemMat: Federated Semantic Services Platform for Materials Science and
Engineering; 315K, Sheth, Prasad, Srinivasan
● Materials Database Knowledge Discovery and Data Mining; 190K, Sheth, Prasad,
Srinivasan
* Grants with Kno.e.sis faculty as
coPI or investigator not included

• Predicting post-discharge outcome through
healthcare big data studies
• Predicting chronic disease prevention and
possible intervention options (starting with
Diabetes)
• Stress, obesity/lifestyle disease, chronic diseases
• Food and diet in the health context
• Keeping elderly at home as long as possible
• Clinical research – developing blood test for
esophageal cancer detection
55
On the drawing board/early stage

• Kno.e.sis is a truly multidisciplinary, pan-University
Center of Excellence were world class
technology/computing expertise come together with
clinical research and applications in health, fitness &
wellbeing
• Major theme: personalized digital health, patient
empowerment, informed patients, epidemiology
• More is covered in my talk on Semantic Data enabling
Personalized Digital Health
56
Take Away

58
http://knoesis.org
http://knoesis.org/vision
http://knoesis.org/amit/hcls
Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
Wright State University, Dayton, Ohio, USA

Healthcare innovations at Kno.e.sis sept2016

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