Ontology-enabled Healthcare Applications Exploiting ...€¦ · 1 Ontology-enabled Healthcare...

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Ontology-enabled Healthcare Applications Exploiting Physical-Cyber-Social Big Data

Ontology Summit for the Health Care Track on Seman8c Integra8on, 7 April 2016

Amit Sheth Kno.e.sis – Ohio Center of Excellence in Knowledge-‐enabled Compu8ng:

An Ohio COE on BioHealth Innova8on Wright State University

Special thanks: Sujan Parera

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Kno.e.sis: Ohio Center of Excellence in Knowledge-enabled Computing

DoD & Industry •  Metabolomics & Proteomics •  Medical Info Decisions •  Human Detec8on on Synthe8c FMV

•  Sensor & Informa8on •  Material Genomics •  Cardiology Seman8c Analysis

NIH: Na;onal Inst. of Health •  kHealth -‐ Asthma •  eDrug Trends •  Depression on Social Media •  Drug Abuse Early Warning

NSF: Na;onal Science Founda;on •  Harassment on Social Media •  Ci8zen & Physical Sensing •  Twitris -‐ Collec8ve Intelligence •  Aerial Surveillance •  Visual Experience •  Web Robot Traffic

Kno.e.sis’ research in World Wide Web ranks Wright State University among the top 10 organiza8ons in the world based on 10-‐yr impact. Its total budget for currently ac8ve projects is $11,443,751, with $5,912,162 for new projects star8ng aaer July 2015. The significant majority of funds are highly compe88ve federal grants. World-‐class research is complemented by excep8onal student outcomes and commercializa8on with local economic impact.

As an Ohio COE on Bio Health Innova8on, Kno.e.sis conducts research leading to building intelligent systems for clinical, biomedical, policy, and epidemiological applica8ons.

Example clinical/healthcare applica8ons include major diseases such as asthma, depression, cardiology, demen8a and GI.

This is complemented by social and development challenges such as marijuana legaliza8on policy, harassment on social media, gender-‐based violence, and disaster coordina8on.

60+ Funded Students •  40 PhD •  16 MS •  5 BS

3

Collaborators

Projects @ Kno.e.sis

Image Credits: heps://www.boxchilli.com/wp-‐content/uploads/2014/07/socialmediamarke8ng.png , hep://rotwnews.com/wp-‐content/uploads/2014/04/DRUG-‐TRENDS-‐Talk.jpg, hep://slapheadmarke8ng.com/wp-‐content/uploads/2012/05/Forum-‐Marke8ng.jpg

eDrugTrends is social media data analy8cs plagorm to monitor the cannabis and synthe8c cannabinoids usage. It uses social media and Web forums data to: 1) Iden8fy and compare trends in knowledge, aitudes, and behaviors related to cannabis and synthe8c cannabinoid, and 2) Iden8fy key influencers in cannabis and synthe8c cannabinoid-‐related discussions on Twieer.

eDrugTrends

Data Sources

Project Wiki


Image Credits: heps://i.y8mg.com/vi/GOK1tKFFIQI/maxresdefault.jpg, hep://slapheadmarke8ng.com/wp-‐content/uploads/2012/05/Forum-‐Marke8ng.jpg, heps://www.boxchilli.com/wp-‐content/uploads/2014/07/socialmediamarke8ng.png, heps://encrypted-‐tbn1.gsta8c.com/images?q=tbn:ANd9GcSucmCuyWvX4dFHv5XvS3KzjvD11hC8HwK9N4004LnBZOGLOgf6, hep://www.crmchealth.org/sites/default/files/images/medical-‐records/Medical_Records_0.png?1314713869

Iden8fying combina8ons of online socio-‐behavioral factors and neighborhood environmental condi8ons that can enable detec8on of depressive behavior in communi8es and studying access and u8liza8on of healthcare services

Depression Behavior

Data Sources

Electronic Medical Records Public Surveys

Project Wiki


This project seeks to understand and sa8sfy users’ need for keeping track of new informa8on in healthcare and well-‐being. The project harvest collec8ve intelligence to iden8fy high quality, reliable and informa8ve healthcare content shared over social media based on following analysis: Text Analysis, Seman;c analysis, Reliability analysis, Popularity Analysis.

Social Health Signals

Data Sources

Image Credits: heps://www.boxchilli.com/wp-‐content/uploads/2014/07/socialmediamarke8ng.png, heps://encrypted-‐tbn2.gsta8c.com/images?q=tbn:ANd9GcS6qI3Z_Y0Uh0sPNCgy0J_0d66-‐5NsCwK3VqWsIkAKRmqjTSXK0uA

Project Wiki

kHeath analyzes both ac8ve and passive observa8ons of the pa8ents to generate the alarms that helps to improve health, fitness, and wellbeing of the pa8ent. It uses Seman8c Sensor Web technology, Seman8c Percep8on, and Intelligence at the Edge to enable sophis8cated analysis of personal health observa8ons.

kHealth


Data Sources

image credits: heps://www.boxchilli.com/wp-‐content/uploads/2014/07/socialmediamarke8ng.png, heps://www.cooking-‐hacks.com/media/cooking/images/documenta8on/e_health_v2/e_health_sensors_small.png, hep://www.co.freeborn.mn.us/ImageRepository/Document?documentID=483

Public Health APIs

Project Wiki


Monitor the health status of the military personnel in training period through self-‐reported fitness notes and con8nuous monitoring with body sensors. The collected data is used to assess the health status of the person and suggest exercise regimen change or treatment plans if needed.

MIDAS

Data Sources

image credits: heps://www.cooking-‐hacks.com/media/cooking/images/documenta8on/e_health_v2/e_health_sensors_small.png, heps://encrypted-‐tbn3.gsta8c.com/images?q=tbn:ANd9GcROFoUIaLPDWcvcCmoi1-‐sl8Bl3CPUtZooX5HHPuDiQKGI7oFZfuQ

Self-‐reported Data

Project Wiki


PREDOSE developed techniques to facilitate prescrip8on drug abuse epidemiology, related to the illicit use of pharmaceu8cal opioids. PREDOSE is designed to capture the knowledge, aitudes and behaviors of prescrip8on drug abusers through the automa8c extrac8on of seman8c informa8on from social media.

PREDOSE

Data Sources

image credits: heps://www.boxchilli.com/wp-‐content/uploads/2014/07/socialmediamarke8ng.png, hep://slapheadmarke8ng.com/wp-‐content/uploads/2012/05/Forum-‐Marke8ng.jpg, heps://encrypted-‐tbn3.gsta8c.com/images?q=tbn:ANd9GcTrMsVTVc6RJrWZtst5ZTILWoD83HO0DPbj3I89YSqMiNRdwI7S

Project Wiki


The scien8fic analysis of the parasite Trypanosoma cruzi (T. cruzi), the principal causa8ve agent of human Chagas disease, is the driving biological applica8on of this project. We developed and deployed a novel ontology-‐driven seman8c problem-‐solving environment (SPSE) for T.cruzi

SPSE – T.cruzi

Data Sources

image credits: heps://encrypted-‐tbn2.gsta8c.com/images?q=tbn:ANd9GcRB-‐YQT3LWMXm9vfv3IdclcMDjP-‐_ChizcFMw53OAkptnHdaUAn6w, hep://www.clfs.umd.edu/biology/machadolab/images/trypanosoma.jpg, hep://web.eecs.umich.edu/~dkoutra/courses/W16_484/

Lab Data Public Databases

Project Wiki

Ontologies Developed at Kno.e.sis •  Drug Abuse Ontology – 83 classes, 37 proper8es •  Depression Insight Ontology – ongoing work •  Healthcare Ontology/ezDI Knowledge Graph – proprietary •  Human Performance and Cogni8on “Ontology” – 2 million en88es, 3 million facts (HPCO)

•  Ontology for Parasite Lifecycle – 360 classes, 12 proper8es (BioPortal) •  Parasite Experiment Ontology – 142 classes, 40 proper8es (BioPortal) •  Provenir Ontology -‐ 88 classes, 23 proper8es (Provenir) – a key input to W3C provenance work

Explana8on Module

Explained?

Yes

No Hypothesis Filtering

Hypothesis Genera8on

Hypothesis with High Confidence

D

D D

DD

D

Pa8ent Notes

UMLS

Knowledgebase Enrichment

Sujan Perera, Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth, and Sahas Nair. "Seman8cs driven approach for knowledge acquisi8on from EMRs." Biomedical and Health Informa2cs, IEEE Journal of 18, no. 2 (2014): 515-‐524.

Knowledgebase Enrichment

●  Knowledge in a given knowledge base may not always sufficient ●  Acquiring required knowledge in some domains is a tedious task ●  Data available for a par8cular domain may contain required knowledge ●  Par8al knowledge about the domain can be used to efficiently acquire

domain knowledge from data that can fill exis8ng gaps in a knowledge base

Data

●  Qualita8ve studies such as telephonic survey which suffer from limited popula;on coverage and large temporal gaps.

●  To address limita8ons of the qualita8ve studies, researchers have used various data sources such as social media (e.g. Twieer), web search logs, and neighborhood factors.....but in silos

Depression

Social Media

Web Search log

Neighborhood factors

EHR data

Depression Behavior

Depression Behavior

PREDOSE

Popula'on Level

Personal

Wheeze – Yes Do you have 2ghtness of chest? –Yes

Observations Physical-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> <2, 1, 1,3, 1, RiskCategory> <2, 1, 1,3, 1, 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 -‐ con8nue Not Well Controlled – contact nurse Poor Controlled – contact doctor

kHealth

Dealing with Heterogeneity

He showed shortness of breath in last visit

Dyspnea was observed in his last visit

It is observed that pa8ent has labored breathing

The pa8ent was breathing comfortably in room air

He showed short of breath in last visit C0013404

shortness of breath dyspnea

Labored or difficult breathing associated with a variety of disorders, indica8ng inadequate ven8la8on or low blood

oxygen.

rdfs:label rdfs:label

is_defined_as

Expressing the Shortness of Breath

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 preZy damn good

didn’t do shit

feel great

Sen8ment Extrac8on

bad headache

+ve

-‐ve

Triples

DOSAGE PRONOUN

INTERVAL Route of Admin.

RELATIONSHIPS SENTIMENTS

DIVERSE DATA TYPES

ENTITIES



Buprenorphine

subClassOf

bupe

En8ty Iden8fica8on

has_slang_term

Suboxone Subutex

subClassOf

bupey

has_slang_term

Drug Abuse Ontology (DAO) 83 Classes

37 Proper8es

33:1 Buprenorphine 24:1 Loperamide

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 8mes a week)

INTERVAL: <PERIOD_IND><PERIOD> (e.g. several years)

Smarter Data Generated with Ontologies

Data Integra;on with Ontologies

UniPort

Internal Lab Data

T.cruzi DB NCBI Data Sources

PubMed

T.cruzi immunology ontology Parasite Experiment ontology T.cruzi life cycle ontology

Aligned Ontologies

Ontologies Developed at Kno.e.sis

•  Drug Abuse Ontology •  Depression Insight Ontology •  Healthcare Ontology •  Human Performance and Cogni8on Ontology (HPCO) •  Ontology for Parasite Lifecycle (BioPortal) •  Parasite Experiment Ontology (BioPortal) •  Provenir Ontology (Provenir)

Thank You

Visit Us @ knoesis.org Follow us @ facebook.com/Kno.e.sis

with addi;onal background at hZp://knoesis.org/amit/hcls

Ohio Center of Excellence in Knowledge-‐enabled Compu;ng -‐ An Ohio Center of Excellence in BioHealth Innova;on

Wright State University

Ontology-enabled Healthcare Applications Exploiting ...€¦ · 1 Ontology-enabled Healthcare...

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