Ontology-enabled Healthcare Applications exploiting Physical-Cyber-Social Big Data
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Transcript of Ontology-enabled Healthcare Applications exploiting Physical-Cyber-Social Big Data
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Ontology-enabled Healthcare Applications Exploiting Physical-
Cyber-Social Big DataOntology Summit for the Health Care Track on Semantic Integration, 7 April 2016
Amit ShethKno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing:
An Ohio COE on BioHealth InnovationWright 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 Detection
on Synthetic FMV• Sensor & Information• Material Genomics• Cardiology Semantic Analysis
NIH: National Inst. of Health• kHealth - Asthma• eDrug Trends• Depression on Social Media• Drug Abuse Early Warning
NSF: National Science Foundation• Harassment on Social Media• Citizen & Physical Sensing• Twitris - Collective Intelligence• Aerial Surveillance• Visual Experience• Web Robot Traffic
Kno.e.sis’ research in World Wide Web ranks Wright State University among the top 10 organizations in the world based on 10-yr impact. Its total budget for currently active projects is $11,443,751, with $5,912,162 for new projects starting after July 2015. The significant majority of funds are highly competitive federal grants. World-class research is complemented by exceptional student outcomes and commercialization with local economic impact.
As an Ohio COE on Bio Health Innovation, Kno.e.sis conducts research leading to building intelligent systems for clinical, biomedical, policy, and epidemiological applications.
Example clinical/healthcare applications include major diseases such as asthma, depression, cardiology, dementia and GI.
This is complemented by social and development challenges such as marijuana legalization policy, harassment on social media, gender-based violence, and disaster coordination.
60+ Funded Students• 40 PhD• 16 MS• 5 BS
3
Collaborators
Projects @ Kno.e.sis
Image Credits: https://www.boxchilli.com/wp-content/uploads/2014/07/socialmediamarketing.png , http://rotwnews.com/wp-content/uploads/2014/04/DRUG-TRENDS-Talk.jpg, http://slapheadmarketing.com/wp-content/uploads/2012/05/Forum-Marketing.jpg
eDrugTrends is social media data analytics platform to monitor the cannabis and synthetic cannabinoids usage. It uses social media 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
Project Wiki
Daily average content: Tweets: 135,553Forum Posts*: 8,899Total: 144,452* Bluelight, Drugs-forum, and Reddit
Projects @ Kno.e.sis
Image Credits: https://i.ytimg.com/vi/GOK1tKFFIQI/maxresdefault.jpg, http://slapheadmarketing.com/wp-content/uploads/2012/05/Forum-Marketing.jpg, https://www.boxchilli.com/wp-content/uploads/2014/07/socialmediamarketing.png, https://encrypted-tbn1.gstatic.com/images?q=tbn:ANd9GcSucmCuyWvX4dFHv5XvS3KzjvD11hC8HwK9N4004LnBZOGLOgf6, http://www.crmchealth.org/sites/default/files/images/medical-records/Medical_Records_0.png?1314713869
Identifying combinations of online socio-behavioral factors and neighborhood environmental conditions that can enable detection of depressive behavior in communities and studying access and utilization of healthcare services
Depression Behavior
Data Sources
Electronic Medical RecordsPublic Surveys
Project WikiSearch Log
Depending on collection method,We get 7-17K tweets per day, and
Have 800K to 18M total tweets in several months.
Projects @ Kno.e.sis
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
Image Credits: https://www.boxchilli.com/wp-content/uploads/2014/07/socialmediamarketing.png, https://encrypted-tbn2.gstatic.com/images?q=tbn:ANd9GcS6qI3Z_Y0Uh0sPNCgy0J_0d66-5NsCwK3VqWsIkAKRmqjTSXK0uA
Project Wiki
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
Projects @ Kno.e.sis
Data Sources
image credits: https://www.boxchilli.com/wp-content/uploads/2014/07/socialmediamarketing.png, https://www.cooking-hacks.com/media/cooking/images/documentation/e_health_v2/e_health_sensors_small.png, http://www.co.freeborn.mn.us/ImageRepository/Document?documentID=483
Public Health APIs
Project Wiki
Projects @ Kno.e.sis
Monitor the health status of the military personnel in training period through self-reported fitness notes and continuous 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: https://www.cooking-hacks.com/media/cooking/images/documentation/e_health_v2/e_health_sensors_small.png, https://encrypted-tbn3.gstatic.com/images?q=tbn:ANd9GcROFoUIaLPDWcvcCmoi1-sl8Bl3CPUtZooX5HHPuDiQKGI7oFZfuQ
Self-reported Data
Project Wiki
Projects @ Kno.e.sis
PREDOSE developed 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
image credits: https://www.boxchilli.com/wp-content/uploads/2014/07/socialmediamarketing.png, http://slapheadmarketing.com/wp-content/uploads/2012/05/Forum-Marketing.jpg, https://encrypted-tbn3.gstatic.com/images?q=tbn:ANd9GcTrMsVTVc6RJrWZtst5ZTILWoD83HO0DPbj3I89YSqMiNRdwI7S
Project Wiki
Projects @ Kno.e.sis
The scientific analysis of the parasite Trypanosoma cruzi (T. cruzi), the principal causative agent of human Chagas disease, is the driving biological application of this project. We developed and deployed a novel ontology-driven semantic problem-solving environment (SPSE) for T.cruzi
SPSE – T.cruzi
Data Sources
image credits: https://encrypted-tbn2.gstatic.com/images?q=tbn:ANd9GcRB-YQT3LWMXm9vfv3IdclcMDjP-_ChizcFMw53OAkptnHdaUAn6w, http://www.clfs.umd.edu/biology/machadolab/images/trypanosoma.jpg, http://web.eecs.umich.edu/~dkoutra/courses/W16_484/
Public & Private Databases (Uniprot, GO, KEGG, TriTrypDB
Project Wiki
experimental data from mass spectrometry and microarray experiments
Textual Data
Ontologies Developed at Kno.e.sis
• Drug Abuse Ontology – 83 classes, 37 properties• Depression Insight Ontology – ongoing work• Healthcare Ontology/ezDI Knowledge Graph – proprietary• Human Performance and Cognition “Ontology” – 2 million entities, 3
million facts (HPCO)• Ontology for Parasite Lifecycle – 360 classes, 12 properties (BioPortal)• Parasite Experiment Ontology – 142 classes, 40 properties (BioPortal)• Provenir Ontology - 88 classes, 23 properties (Provenir) – a key input to
W3C provenance work
Earlier at UGA: ProPreO (500+ classes), GlycO,…
Semantic Filtering
Data Integration
Knowledge Enrichment
Entity Annotation
Triple Extraction
Sentiment Analysis/Intent Mining/User Modeling
r1
Search/Browsing/Summarization/Trend/Analysis/Prediction
Knowledge Base Usages @ Kno.e.sis
Data alone is not enough.KB+NLP+ML
Explanation Module
Explained?
Yes
NoHypothesis
FilteringHypothesis Generation
Hypothesis with High
Confidence
D
D D
DD
D
Patient Notes
UMLS
Knowledgebase Enrichment
Sujan Perera, Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth, and Sahas Nair. "Semantics driven approach for knowledge acquisition from EMRs." Biomedical and Health Informatics, 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 particular domain may contain required knowledge● Partial knowledge about the domain can be used to efficiently acquire
domain knowledge from data that can fill existing gaps in a knowledge base
Data
Data Integration with Ontologies
UniPort
Internal Lab Data
T.cruzi DBNCBI Data Sources
PubMed
T.cruzi immunology ontology Parasite Experiment ontology T.cruzi life cycle ontology
Aligned Ontologies
● Qualitative studies such as telephonic survey which suffer from limited population coverage and large temporal gaps.
● To address limitations of the qualitative studies, researchers have used various data sources such as social media (e.g. Twitter), web search logs, and neighborhood factors.....but in silos
Depression
Social Media
Web Search log
Neighborhood factors
EHR data
Depression Behavior
Depression Behavior
PREDOSE
Population Level
Personal
Wheeze – YesDo you have tightness 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 fromon-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 - continueNot Well Controlled – contact nursePoor Controlled – contact doctor
kHealth
SSN
Dealing with Heterogeneity
He showed shortness of breath in last visit
Dyspnea was observed in his last visit
It is observed that patient has labored breathing
The patient was breathing comfortably in room air
He showed short of breath in last visitC0013404
shortness of breath dyspnea
Labored or difficult breathing associated with a variety of disorders, indicating inadequate
ventilation or low blood oxygen.
rdfs:labelrdfs:label
is_defined_as
Expressing the Shortness of Breath
explicit mention
syntactic variation
synonym
positive implicit mention
negative implicit mention
individual literal
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
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.
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.
Buprenorphine
subClassOf
bupe
Entity Identification
has_slang_term
SuboxoneSubutex
subClassOf
bupey
has_slang_term
Drug Abuse Ontology (DAO)
83 Classes37 Properties
33:1 Buprenorphine24:1 Loperamide
Ontology Lexicon Lexico-ontology Rule-based Grammar
ENTITIESTRIPLES
EMOTIONINTENSITYPRONOUN
SENTIMENT
DRUG-FORMROUTE OF ADM
SIDEEFFECT
DOSAGEFREQUENCY
INTERVAL
Suboxone, Kratom, Herion, Suboxone-CAUSE-Cephalalgia
disgusted, amazed, irritatedmore than, a, few of
I, me, mine, myIm glad, turn out bad, weird
ointment, tablet, pill, filmsmoke, 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)
Smarter Data Generated with Ontologies
Thank You
Visit Us @ knoesis.org Follow us @ facebook.com/Kno.e.sis
One example of commercial applications: ezdi.com
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