Semantic Web & Web 3.0 empowering real world outcomes in biomedical research and clinical practices

1. 1 Semantic Web & Web 3.0 empowering real world outcomes in biomedical research and clinical practices 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; Ack: Kno.e.sis HCLS team and collaborators Talk presented in Spain (WiMS 2013/UAM-Madrid, UMA-Malaga), June 2013

2. Integration

3. Semantics

4. Role of Semantic Web in HCLS • Improve the machine understandability and processing of all types of data by • Modeling and Background Knowledge • Annotation • Complex Querying/Analysis, Reasoning • Improve Insight from Biomedical Data • Improve Clinical Decision Making • Vastness/Volume • Velocity • Variety/Heterogeneity • Vagueness, Uncertainty, Inconsistency, Deceit Objective Challenges Approach

5. Identifiers: URI Character set: UNICODE Syntax: XML Data interchange: RDF Querying: SPARQL Taxonomies: RDFS Ontologies: OWL Rules: RIF/SWRL Unifying logic Proof Trust Cryptography User interface and applications Querying Data/Knowledge Representation Knowledge Representation Lots of need for NLP, ML, IR, and other technologies – SW significantly empowers these and closes some critical gaps

6. HCLS Apps @ Kno.e.sis • Semantic Search and Browsing(Doozer++, SCOONER, iExplore) • Semantics and Services enabled Problem Solving Environment for T.cruzi(SPSE) • Active Semantic Electronic Medical Record(ASEMR) • Mining and Analysis of EMR(ezFIND, ezMeasure, ezCAC) • kHealth (ADHF, Asthma, …) • PREscription Drug abuse Online Surveillance and Epidemiology(PREDOSE) Biomedical Healthcare Epidemiology

7. Insights Better Understanding Intuitive Browsing Hypothesis Generation Personalization Knowledge Exploration Doozer++ iExplore SCOONER Kino Kno.e.sis Bioinformatics toolkit http://knoesis.org/opensource http://knoesis.org/showcase

8. Knowledge Acquisition – Doozer++ • Building ontology is costly • Large volume of knowledge available in semi- structured/unstructured format • No assurance for the credibility of such knowledge

9. Knowledge Acquisition – Doozer++ Circle of Knowledge http://knoesis.org/node/71

10. Knowledge Acquisition – Doozer++

11. Knowledge Acquisition – Doozer++

12. j.1:category_scie nce j.1:category_neu roscience j.1:category_cog nitive_science j.1:category_psy chology j.1:category_beh avior j.1:category_phil osophy_of_mind j.1:category_brai n j.1:category_psy cholinguistics j.1:category_neu rology j.1:category_neu rophysiology 10 classes… Knowledge Acquisition – Doozer++

13. Doozer++ Demo Knowledge Acquisition from Community-Generated Content Continuous Semantics to Analyze Real-Time Data , IEEE Internet Computing (Volume 14)

14. • Identify Relationships • Textual pattern-based extraction for known relationships • Facts available in background knowledge • Find evidence for such facts • Combined evidence from many different patterns increases the certainty of a relationship between the entities Beyond Hierarchy

15. • Evaluating acquired knowledge • Explicit • User can vote for facts • Facts presented based on user interests • Implicit • User’s browsing history used as a indication of which propositions are correct and interesting • Now it adds validated knowledge back to community Validating Knowledge

16. Base Hierarchy from Wikipedia SenseLab Neuroscience Ontologies Meta Knowledgebase PubMed Abstracts Focused pattern based extraction Initial KB creation Enriched Knowledgebase HPC Keywords Kno.e.sis: NLP based triples NLM: Rule based BKR triples Building Human Performance & Cognition Ontology (HPCO) Merge http://wiki.knoesis.org/index.php/Human_Performance_and_Cognition_Ontology

17. Use Case for HPCO • Number of Entities – 2 million • Number of non-trivial facts – 3 million • NLP Based*: calcium-binding protein S100B modulates long-term synaptic plasticity • Pattern Based**: Olfactory Bulb has physical part of anatomic structure Mitral cell * Joint Extraction of Compound Entities and Relationships from Biomedical Literature , Web Intel. 2008 * A Framework for Schema-Driven Relationship Discovery from Unstructured Text, ISWC 2006 ** On Demand Creation of Focused Domain Models using Top-down and Bottom-up Information Extraction, Technical Report

18. Knowledge-based Browsing - SCOONER • Knowledge-based browsing: relations window, inverse relations, creating trails • Persistent Projects: Work bench, Browsing history, Comments, Filtering • Collaboration: Comments, Dashboard, Exporting projects, Importing projects

19. SCOONER Demo SCOONER Details An Up-to-date Knowledge-Based Literature Search and Exploration Framework for Focused Bioscience Domains , IHI 2012- 2nd ACM SIGHIT International Health Informatics Symposium

20. Kino • An integrated suite of tools that enables scientists to annotate – unstructured resources – semi-structured resources • Annotates documents by accessing NCBO ontologies, via the NCBO Web API. • Includes two main components – A browser-based annotation front-end – An annotation-aware back-end index that provides faceted search capabilities

21. Kino Architecture

22. Example: Annotating Literature

23. Annotation the XML file with NCBO Ontology rel rel Ontology concept

24. Kino Search •Search the annotated document with the concept of interest •Return all annotated document with selected concept

25. Kino Demo Kino: A Generic Document Management System for Biologists Using SA-REST and Faceted Search. ICSC 2011

26. iExplore Interactive Browsing and Exploring Biomedical Knowledge

27. Architecture

28. Generate Novel Hypothesis

29. iExplore video iExplore Demo

30. Turning to Applications with End Users

31. Active Semantic Electronic Medical Record - ASEMR • New Drugs • Adds interaction with current drugs • Changes possible procedures to treat an illness • Insurance coverage changes • Will pay for drug X, but not Y • May need certain diagnosis before expensive tests • Physicians are require to keep track of ever changing landscape

32. • A Document • With semantic annotations • entities linked to ontology • terms linked to specialized lexicon • With actionable information • rules over semantic annotations • rule violation indicated with alerts Atrial fibrillation with prior stroke, currently on Pradaxa, doing well. Mild glucose intolerance and hyperlipidemia, being treated by primary care. ASEMR – Active Semantic Document

33. • Type of ASD • Three Ontologies • Practice Information about practice such as patient/physician data • Drug Information about drugs, interaction, formularies, etc. • ICD/CPT Describes the relationships between CPT and ICD codes ASEMR – Active Semantic Patient Record

34. encounter ancillary event insurance_ carrier insurance facility insurance_ plan patient person practitioner insurance_ policy owl:thing ambularory _episode ASEMR – Practice Ontology Hierarchy

35. owl:thing prescription _drug_ brand_name brandname_ undeclared brandname_ composite prescription _drug monograph _ix_class cpnum_ group prescription _drug_ property indication_ property formulary_ property non_drug_ reactant interaction_ property property formulary brandname_ individual interaction_ with_prescri ption_drug interaction indication generic_ individual prescription _drug_ generic generic_ composite interaction_ with_non_ drug_reactant interaction_ with_mono graph_ix_cl ass ASEMR – Drug Ontology Hierarchy

36. ASEMR

37. 0 100 200 300 400 500 600 Jan 04 M ar04 M ay 04 Jul04Sept04 N ov 04 Jan 05 M ar05 M ay 05 Jul05 Month/Year Charts Same Day Back Log Before ASEMR

38. 0 100 200 300 400 500 600 700 Sept 05 Nov 05 Jan 06 Mar 06 Month/Year Charts Same Day Back Log After ASEMR

39. • Error Prevention • Patient care • Insurance • Decision Support • Patient satisfaction • Reimbursement • Efficiency/Time • Real-time chart completion • “semantic” and automated linking with billing ASEMR - Benefits

40. ASEMR Demo Active Semantic Electronic Medical Record, ISWC 2006

41. Semantics and Services enabled Problem Solving Environment for T.cruzi - SPSE • Majority of experimental data reside in labs • Integration of lab data facilitate new insights • Formulating queries against such data required deep technical knowledge A Semantic Problem Solving Environment for Integrative Parasite Research: Identification of Intervention Targets for Trypanosoma cruzi, 2012

42. SPSE • Data Sources • Internal Lab Data • External Database • Ontological Infrastructure • Parasite Lifecycle • Parasite Experiment • Query Processing • Cuebee

43. • Integrated internal data with external databases, such as KEGG, GO, and some datasets on TriTrypDB • Developed semantic provenance framework and influenced W3C community • SPSE supports complex biological queries that help find gene knockout, drug and/or vaccination targets. For example: • Show me proteins that are downregulated in the epimastigote stage and exist in a single metabolic pathway. • Give me the gene knockout summaries, both for plasmid construction and strain creation, for all gene knockout targets that are 2-fold upregulated in amastigotes at the transcript level and that have orthologs in Leishmania but not in Trypanosoma brucei. SPSE

44. Complex queries can also include: - on-the-fly Web services execution to retrieve additional data - inference rules to make implicit knowledge explicit SPSE

45. • So many ontologies • Rich in number of concepts • Mostly concentrated on taxonomical relationships • Applications require domain relationships • A is_symptom_of B • C is_treated_with D Knowledge Enrichment from Data

46. Data Information Knowledge Knowledge Enrichment from Data

47. IntellegOBackground knowledge Modified background knowledge EMR Knowledge Enrichment from Data Data Driven Knowledge Acquisition Method for Domain Knowledge Enrichment in Healthcare, BIBM 2012 An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web, Applied Ontology 2011

48. Knowledge Enrichment from Data atrial Fibrillation hypertension diabetes chest pain weight gain discomfort in chest rash skin cough weight loss headache edema shortness of breath fatigue syncope weight loss chest pain discomfort in chest dizzy shortness of breath nausea vomiting headache cough weight gain Diseases Symptoms Symptoms From EMR From KB Is edema symptom of atrial fibrillation? Is edema symptom of hypertension? Is edema symptom of diabetes?

49. Domains Cardiology Orthopedics Oncology Neurology Etc… No of concepts 1008161 Problems(diseases, symptoms) 125778 Procedures 262360 Medicines 298993 Medical Devices 33124 Relationships 77261 is treated with (disease -> medication) 41182 is relevant procedure (procedure -> disease) 3352 is symptom of (symptom -> disease) 8299 contraindicated drug (medication -> disease) 24428 Knowledge Enrichment from Data with the above method + UMLS healthline.com druglib.com

50. • 80% unstructured healthcare data • Pose challenges in • Searching • Understanding • Mining • Knowledge discovery • Decision support • Evidence based medicine • Federal policies promote meaningful use and pose constraints to healthcare system Healthcare Challenge

51. Coding Complexity ICD-9 ICD-10 Diagnostic Codes 14,000 69,000 Procedure Codes 3,800 72,000 ICD-9 (Current) ICD-10 Conversion (1st Oct,2014) Clinical Documentation & Coding-Billing Challenges Example: 821.01: ICD-9 code for “closed” Fractured Femur, or thigh bone. Translates to 36 codes in ICD-10 with details regarding the precise nature of fracture, which thigh was fractured, whether a delay in healing occurred etc. Healthcare Challenge

52. • Traditional methods doesn’t work • Understanding the context is crucial Need to Do Better Healthcare Challenge

53. Search Mining Decision Support Knowledge Discovery Evidence-based Medicine NLP + Semantics Healthcare Challenge – The Solution

54. ezHealth 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

55. ezHealth - Benefits • Advance search • All hypertension patients with ejection fraction <40 • All MI patients who are taking either beta- blockers or ACE Inhibitors • Patients diagnose with Atrial Fibrillation on Coumadin or Lovanox • Support core-measure initiative

56. Error Detection EMR: 1. “Sepsis due to urinary tract infection….” 2. “Her prognosis is poor both short term and long term, however, we will do everything possible to keep her alive and battle this infection." SNM:40733004_infection SNM:68566005_infection_urinary_tract A syntax based NLP extractor (such as Medlee) can extract this term and annotate as SNM:40733004_infection By utilizing IntellegO and cardiology background knowledge, we can more accurately annotate the term as SNM:68566005_infection_urinary_tract *MedLEE with usage of IntellegO Problem Problem *MedLEE is NLP engine optimized to parse clinical documents

57. EMR: ”The patient is to receive 2 fluid boluses." SNM:32457005_body_fluid A syntax based NLP extractor (such as Medlee) can extract this term and annotate as SNM:32457005_body_fluid MedLEE Problem Fluid is part of buloses treatment, not a problem with IntellegO By utilizing IntellegO and cardiology background knowledge, we can determine that this is not a symptom – hence annotation is incorrect. Treatment Error Detection

58. The balance of evidence would suggest that his episode of atrial fibrillation seems to be an isolated event He has had no documented atrial fibrillation since that time Patient has atrial fibrillation Patient does not have atrial fibrillation NLP NLP Atrial FibrillationSyncope Is_symptom_of Warfarin Atenolol AspirinIs_medication_for Resolve Inconsistency Using domain relationships we validated that patient has atrial fibrillation Symptoms Medication Medication Medication

59. She denies any chest pain but is not really function due to leg stiffness, swelling an shortness of breath Regarding the shortness of breath, we will send for a dobutamine stress echocardiogram Patient does not have shortness of breath Patient has shortness of breath NLP NLP Shortness of Breath Is_symptom_of Obesity Hypertension Sleep Apnea Obstructive Resolve Inconsistency Using domain relationships we validated that patient has shortness of breath Disorder Disorder Disorder

60. PREscription Drug abuse Online Surveillance and Epidemiology - PREDOSE • Non-medical use of Prescription Drugs • Fastest Growing Drug problem in US • Director ONDCP Gil Kerlikowske, Epidemic* • Pathway to heroin addiction • Escalating accidental overdose deaths • Current Epidemiological Data Systems • Interactive Interviews • Online Surveys • Manual Coding

61. Specific Aims Describe drug user’s knowledge, attitudes, and behaviors related to illicit use of Prescription Drugs (Content Analysis) Describe temporal patterns of non-medical use of Prescription Drugs (Trend Analysis)

62. Overall Approach 1. Automate Data Collection • Social Media - Online Web forums 2. Create Structured Domain Vocabulary • Drug Abuse Ontology (DAO) 3. Automate Information Extraction • Entity, Relationship, Triple, Sentiment, Template 4. Develop Tools for Data Analysis a) Content Analysis - Content Explorer, Template Pattern Explorer, Proximity Search b) Trend Analysis - Trend explorer, Emerging pattern explorer

63. 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

64. Research Highlights Drug Abuse Ontology • First ontology on prescription drug abuse Ontology-based Entity Identification • Gold standard dataset 601 posts • Buprenorphine – 33:1 Slang-to-drug mentions • Loperamide – 24:1 Slang-to-drug mentions: • 85% Precision, 72% Recall

65. Research Highlights Template-based (Knowledge-Aware) Search • Complex Information Needs Solution 1. Ontology-based Search 2. Rule-based Search – Intensity, Frequency, Dosage, Interval 3. Context-Free Grammar – Queries Interpretable by PREDOSE 4. Data Sources – Ontology, Lexicon, Lexico-ontology, Alphabet

66. Entity +ve Sentiment Opiated Effect Extra-medical Use of Loperamide Loperamide-Withdrawal Discovery

67. kHealth 71 Health information is now available from multiple sources • medical records • background knowledge • social networks • personal observations • sensors • etc.

68. 72 Foursquare is an online application which integrates a persons physical location and social network. Community of enthusiasts that share experiences of self-tracking and measurement. FitBit Community allows the automated collection and sharing of health-related data, goals, and achievements kHealth

69. 73 Sensors, actuators, and mobile computing are playing an increasingly important role in providing data for early phases of the health-care life-cycle This represents a fundamental shift: • people are now empowered to monitor and manage their own health; • and doctors are given access to more data about their patients kHealth

70. 74 kHealth

71. 75 Personal Health Dashboard kHealth

72. 76 Personal Health Dashboard 1  2  3 Continuous Monitoring Personal Assessment Medical Service Auxiliary Information – background knowledge, social/community support, personal context, personal medical history kHealth

73. 77 ? kHealth

74. kHealth – Key Ingredients 78 Background Knowledge Social Network Input Personal Observations Personal Medical History

75. 79 Abstractions Observations kHealth

76. 80 kHealth - Technology observes inheres in perceives sends focus sends observation Observer Quality EntityPerceiver

77. 82 kHealth - Technology Background Knowledge as Bi-partite Graph

78. 83 kHealth - Technology Explanation: is the act of choosing the objects or events that best account for a set of observations; often referred to as hypothesis building Discrimination: is the act of finding those properties that, if observed, would help distinguish between multiple explanatory features

79. 84 kHealth - Technology Explanatory Feature: a feature that explains the set of observed properties ExplanatoryFeature ≡ ∃ssn:isPropertyOf—.{p1} ⊓ … ⊓ ∃ssn:isPropertyOf—.{pn} elevated blood pressure clammy skin palpitations Hypertension Hyperthyroidism Pulmonary Edema Observed Property Explanatory Feature Explanation

80. 85 kHealth - Technology Discrimination Expected Property: would be explained by every explanatory feature ExpectedProperty ≡ ∃ssn:isPropertyOf.{f1} ⊓ … ⊓ ∃ssn:isPropertyOf.{fn} elevated blood pressure clammy skin palpitations Hypertension Hyperthyroidism Pulmonary Edema Expected Property Explanatory Feature

81. 86 kHealth - Technology Discrimination Not Applicable Property: would not be explained by any explanatory feature NotApplicableProperty ≡ ¬∃ssn:isPropertyOf.{f1} ⊓ … ⊓ ¬∃ssn:isPropertyOf.{fn} elevated blood pressure clammy skin palpitations Hypertension Hyperthyroidism Pulmonary Edema Not Applicable Property Explanatory Feature

82. 87 kHealth - Technology Discrimination Discriminating Property: is neither expected nor not- applicable DiscriminatingProperty ≡ ¬ExpectedProperty ⊓ ¬NotApplicableProperty elevated blood pressure clammy skin palpitations Hypertension Hyperthyroidism Pulmonary Edema Discriminating Property Explanatory Feature

83. 90 kHealth Demo An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web, Applied Ontology 2011 Representation of Parsimonious Covering Theory in OWL-DL (OWLED 2011) An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices (ISWC 2012) Data Driven Knowledge Acquisition Method for Domain Knowledge Enrichment in Healthcare, BIBM 2012

84. 91 kHealth

85. 92 kHealth - Asthma • Can we detect asthma/allergy early? – Using data from on-body sensors, and environmental sensors – Using knowledge from an asthma ontology, generated from asthma knowledge on the Web and domain experts – Generate a risk measure from collected data and background knowledge • Can we characterize asthma/allergy progression? – State of asthma patient may change over time – Identifying risky progressions before worsening of the patient state • Does the early detection of asthma/allergy, and subsequent intervention/treatment, lead to improved outcomes? – Improved outcomes could be improved health (less serious symptoms), less need for invasive treatments, preventive measures (e.g. avoiding risky environmental conditions), less cost, etc.

86. • GO (well controlled) – peak flow 80-100%* – Good breathing and sleep: Acceleration reading pattern – No cough: microphone – Good physical activity: Acceleration reading pattern • CAUTION (not well controlled) – peak flow 60-80%* – Cough and Wheeze: microphone – Tight chest: Acceleration readings – Wakes up at night: Acceleration reading pattern • STOP (poor control) – peak flow < 60%* – Medicine not helping: medicine = TRUE and still in STOP state – Breathing hard and fast: microphone – Can’t walk or talk well: Acceleration and microphone 93* Measured using peak flow meter Asthma Control Level and Corresponding Sensor Observations

87. 94 Physical Social http://ngs.ics.uci.edu/blog/?p=1478 Cyber Data Collection Analysis Action Take Medication before going to work Avoid going out in the evening due to high pollen levels Domain ExpertsDomain Knowledge Risk Model Action Model Overall Landscape

88. 95 Personal Level Events Population Level Events (Personal Level Events) (Personalized Events) (Population Level Events) Population-level Events Relevant at the Personal-level Machine sensors:  Pollen levels  Pollution levels  Accelerometer  Peak flow meter  Medication tracking Personal sensors:  Symptoms (kHealth) (EventShop) Qualify & Quantify -Detect all the factors influencing asthma -Find the role of each factor in influencing asthma Asthma Risk Profile -Contextual information to personalize risk -Risk score computation Asthma Mitigation -Corrective action based on risk score What are the factors influencing my asthma? What is the contribution of each of these factors? How controlled is my asthma? (risk score) What will be my action plan to manage asthma? Storage Pose Questions Receive answers Access/update patient information Machine sensors:  Pollen levels  Pollution levels Personal sensors:  Symptoms  Asthma prevalence

89. 96 Community Spaces Personal Spaces 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> <2, 1, 1,3, 1, RiskCategory> <2, 1, 1,3, 1, RiskCategory> <2, 1, 1,3, 1, RiskCategory> . . . Actionable Information Action: contact doctor now Explanation: Increased activity is the primary cause of wheezing and high risk category 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

90. • Collaborators: AHC (Dr. Agrawal), CITAR-WSU, ezDI (ezdi.us), NLM (Dr. Bodenrider), CTEGD-UGA (Dr. Mnning/Prof. Tarleton), NCBO - Stanford, Welcome Trust, AFRL, Boonshoft Sch of Med – WSU (Dr. Forbis, …), • Funding: NIH (NHLBI R01: 1R01HL087795-01A1; NIDA: R21 DA030571 ), NSF, AFRL, Industry…. Acknowledgements

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

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

93. 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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