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Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices
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Semantic Technology empowering Real World outcomes in Biomedical Research and Clinical Practices

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Talk at Case Western Reserve university: http://engineering.case.edu/eecs/node/392

Talk at Case Western Reserve university: http://engineering.case.edu/eecs/node/392

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  • Heterogeneity of data to be integrated(Variety)
  • QualityHow do you fix it? Measure it?How do you decide
  • Consumers are changedClinicians + drug makers + Insurance companiesTechnology savvy users + gadgets
  • We have lot of data, we are trying to use meaningfully, but still customer(users) are not satisfiedSo we need computer to understand the data
  • What is semantic web?http://en.wikipedia.org/wiki/Semantic_WebVast – huge dataVague – define ‘young’ ‘tall’Uncertainty - a patient might present a set of symptoms which correspond to a number of different distinct diagnoses each with a different probabilityDeceit -  intentionally misleading
  • The technology stack and usage of most popular technologies
  • Knowledge + data representation
  • Knowledge representation
  • Querying
  • Kno.e.sis products
  • This slide intend justify the development of tools doozer, scooner, iExplorerHuge amount of knowledge in different format and people are overloaded withKnowledge/Information, we need mechanism to better exploration of knowledgeAnd help them to find what they require(scooner, iExplorer) and derive new knowledge
  • Why doozer?Knowledge is available in various formats, but they are hardly helpful if not inStructured format. But building structured knowledgebase from available formats is achallenge
  • Human knowledge cycleDoozer is a one tool that supports this
  • Forms of open knowledgeWikipediaLODFormal models
  • Knowledge acquisition through Model creation
  • Hierarchy creation from wikipedia
  • Big picture
  • Doozer’s way of identifying relationships
  • Last two steps of knowledge cycle
  • Big pictureKno.e.sis: NLP based triples -  CarticRamakrishnan's and Pablo's work on open Information Extraction from biomedical text.Sentences in MedLine abstracts are parsed and split into Subject, Predicate and Object.In the Merge phase, only those triples that have Subject and Object that can be mapped to the initial KB are added to the enriched KB.BKR triples is that the BKR triples were probably verified by NLM before being published, whereas the Knoesis triples went into the KB unverified, apart from having to match initial KB concepts.
  • Last two steps of knowledge cycle
  • Why scooner
  • demo
  • Knowledge and data are separatedThere is no way to validate whether my data adheres to knowledge and vice-versa
  • Architecture
  • Generate Novel hypothesis
  • The challengeWhy ASEMR?
  • How ASEMR?
  • How ASEMR?
  • The architecture
  • Why SPSE?Integration of data gives more insights, but the heterogeneity of data stand against the integration
  • How SPSE
  • BenefitsGet Vihn’s help to reduce text
  • why
  • EMR documents not only contain data/information but knowledge tooBut scattered nature of knowledge makes it difficult to discover
  • The big pictureThe built knowledgebase should be able to explain the real world data,We used this claim in reverse order: real world data can be used to enhance the Knowledge base when it fails to explain the dataScenario: Extract all diseases from the documentGenerate all possible symptoms for these diseases using knowledgebaseExtract all symptoms from the documentIf there are more symptoms in document than the generated set, this indicates that we might be missingsome relationship betweenDisease and symptomsWe use this indication to generate questions that can be answered by the domain expert, this will allow us to enrich the knowledgebase
  • What we found is edema is symptom of hypertension.This method will reduce the workload of domain expertImagine we have 50 diseases and 100 symptomsThen there are 5000 possibilities,Domain expert has to go through each and validate, but with this methodWe will only ask the question only if we find evidence
  • What we achieved?Not sure whether this slide is requiredWe used lot of existing knowledgebases to build this knowledgebase
  • Unstructured data posing challenges in every field, but here is our attempt to overcomeThe challenge in healthcareTraditional methods - IR, Data mining, traditional NLP
  • People waiting to harness the unstructured healthcare data for all these applications
  • ArchitectureTo-Do – May need to use logos of ezFIND and ezMeasureData Cleaning:Adding section headersModify malformed section headersDe-identificationCAC – Computer Assisted CodingCDI – Clinical Document Improvement
  • Emphasize the capability of inferencing (only because we have knowledgebase) andPoint out that how difficult to formulate such queries if knowledgebase is not available
  • EMR doc has these two sentences‘Urinary tract infection’ (first sentence) is correctly annotated, but ‘infection’ in second one is not.Second ‘infection’ actually refers to ‘urinary tract infection’ in first sentence, but NLP engineDoes not understand this.We could find this because there are no evidences to suggest ‘infection’ in the document according to our knowledgebase.So after detecting this issue, we could annotate the second infection as urinary tract infection(this annotation is done manually) Detection is done with IntellegOOne could rather argue that annotating second ‘infection’ as just infection does not harm because urinary tract infection is alsoInfection, but detection of these things help to improve the annotation.
  • NLP engine annotate the fluid as ‘body_fluid’ which is a symptomBut here the term ‘fluid’ does not refer to symptom rather the form of medication ‘boluses’We could find this issue because there was no disease in the document to suggest the ‘body fluid’
  • In this case NLP does not detect second statement is talking about history.But with the knowledgebase we have, we can say patient actually has AF.So we resolve the inconsistency here.Example from document 673
  • NLP does not understand the first sentenceIt says ‘not’ attached to shortness of breath which is wrong according to semantics of the sentence.But we can resolve this issue by using knowledgebaseExample from document 595
  • Why PREDOSE?Data collection practices – interactive interviews, surveysData analysis limitations- coding
  • Why PREDOSE?
  • The objective
  • The Architecture
  • May need one more slide to show the achievements
  • Multi model healthcare data
  • Recent advancement in observation mechanisms and data sharing
  • Sensors play key role
  • But still we are here
  • We need to get here
  • Kno.e.siskHealth ideaOngoing work : simulating first two phasesOur product is MobileMDDemo is at the end of the slides
  • The ChallengeWe have sensors to measure movements, heart rate, sleeping, galvanic skin response etc…But we don’t know how to aggregate
  • Key ingredients which will help to understand the healthcare data(measurements)
  • Numbers->abstractions->knowledge integration(static knowledge about the domain, personal background)->predictionAdvantages: early detection and alert generation
  • Transcript

    • 1. Semantic technology empowering real world outcomes in biomedical research and clinical practices Talk presented at Case Western Reserve University on Nov 26, 2012 Amit ShethKno.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: SujanParera 1
    • 2. Integration
    • 3. Semantics
    • 4. Alan Smith Vinh HemantP Sujan Nguyen urohit Perera Wenbo Wang Cory HensonPramod Koneru Amit Sheth KalpaMaryam Panahiazar GunaratnaAshutoshJadhav Sanjaya Wijeratne Pramod Prateek PavanKapanip Delroy Sarasi Lalithsena Ajith Anantharam Jain athi Lu Chen Cameron Ranabahu
    • 5. Semantic Web • Improve Insight from Biomedical DataObjective • Improve Clinical Decision Making • Vastness/Volume • VelocityChallenges • Variety/Heterogeneity • Vagueness, Uncertainty, Inconsistency, Deceit • Improve the machine understandability andApproach processing of data of all types to • Modeling and Background Knowledge • Annotation • Complex Querying/Analysis, Reasoning
    • 6. User interface and applications Trust Knowledge Proof Representation Unifying logicQuerying Ontologies: Rules: Cryptography Querying: OWL Data/Knowledge RIF/SWRL SPARQL Representation Taxonomies: RDFS Data interchange: RDF Syntax: XML Identifiers: URI Character set: UNICODE
    • 7. Applications Epidemiology Biomedical • PREscription Drug abuse Online Surveillance and• Semantic Search and Epidemiology(PREDOSE) Browsing(Doozer++, SCOONER, iExplore)• Semantics and Services Healthcare enabled Problem Solving • Active Semantic Electronic Environment for Medical Record(ASEMR) T.cruzi(SPSE) • Mining and Analysis of EMR(ezFIND, ezMeasure) • kHealth
    • 8. Doozer++ Some of the semantic tools iExplore SCOONER Knowledge Insights Exploration Hypothesis Intuitive Generation Browsing BetterPersonalization Understanding
    • 9. 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
    • 10. Knowledge Acquisition – Doozer++ Circle of Knowledge http://knoesis.org/node/71
    • 11. Knowledge Acquisition – Doozer++
    • 12. Knowledge Acquisition – Doozer++
    • 13. Knowledge Acquisition – Doozer++ j.1:category_scie ncej.1:category_psy j.1:category_cog j.1:category_neu chology nitive_science roscience10 classes… j.1:category_beh j.1:category_phil j.1:category_neu avior osophy_of_mind rology j.1:category_psy j.1:category_brai j.1:category_neu cholinguistics n rophysiology
    • 14. Doozer++ DemoKnowledge Acquisition from Community-Generated ContentContinuous Semantics to Analyze Real-Time Data , IEEE InternetComputing (Volume 14)
    • 15. Beyond Hierarchy• 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
    • 16. Validating Knowledge• 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
    • 17. Building Human Performance & Cognition Ontology (HPCO) HPC Base Hierarchy fromKeywords Wikipedia Focused pattern based extraction SenseLab Neuroscience Ontologies Initial KB creation Meta Knowledgebase PubMed Abstracts Merge Kno.e.sis: NLP based triples NLM: Rule based Enriched BKR triples Knowledgebase
    • 18. 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
    • 19. 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
    • 20. SCOONER DemoAn Up-to-date Knowledge-Based Literature Search and Exploration Frameworkfor Focused Bioscience Domains , IHI 2012- 2nd ACM SIGHIT InternationalHealth Informatics Symposium
    • 21. iExploreInteractive Browsing and Exploring Biomedical Knowledge
    • 22. Architecture
    • 23. Generate Novel Hypothesis
    • 24. iExplore DemoiExplore video
    • 25. Turning toApplications with End Users
    • 26. 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
    • 27. ASEMR – Active Semantic Document • 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.
    • 28. ASEMR – Active Semantic Patient Record • 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
    • 29. ASEMR – Practice Ontology Hierarchy facility insurance_ ancillary owl:thing carrier ambularory insurance _episode insurance_encounter plan person event insurance_ patient policy practitioner
    • 30. ASEMR – Drug Ontology Hierarchy formulary_ non_drug_ interaction_ property formulary reactant property indication indication_ property owl:thingmonograph property_ix_class prescription interaction_ _drug_ with_non_ brandname_ prescription brand_name drug_reactantprescription individual _drug interaction_drug_property brandname_ brandname_ composite prescription interaction_ undeclared _drug_ with_mono interaction_ generic graph_ix_cl with_prescri cpnum_ generic_ ass ption_drug group composite generic_ individual
    • 31. ASEMR
    • 32. Charts Ja n 100 200 300 400 500 600 0 04 M ar 04 M ay 04 Ju l0 Se 4 pt 04 N ov 04 Ja n 05Month/Year M ar 05 M ay 05 Before ASEMR Ju l0 5 Back Log Same Day
    • 33. After ASEMR 700 600 500Charts 400 Same Day 300 Back Log 200 100 0 Sept Nov 05 Jan 06 Mar 06 05 Month/Year
    • 34. ASEMR - Benefits• Error Prevention • Patient care • Insurance• Decision Support • Patient satisfaction • Reimbursement• Efficiency/Time • Real-time chart completion • “semantic” and automated linking with billing
    • 35. ASEMR DemoActive Semantic Electronic Medical Record, ISWC 2006
    • 36. Semantics and Services enabledProblem 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 knowledgeA Semantic Problem Solving Environment for Integrative Parasite Research:Identification of Intervention Targets for Trypanosomacruzi, 2012
    • 37. SPSE• Query Processing • Cuebee• Ontological Infrastructure • Parasite Lifecycle • Parasite Experiment• Data Sources • Internal Lab Data • External Database
    • 38. SPSE• 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 Trypanosomabrucei.
    • 39. SPSEComplex queries can also include:- on-the-fly Web services execution to retrieve additional data- inference rules to make implicit knowledge explicit
    • 40. Knowledge Enrichment from Data• 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
    • 41. Knowledge Enrichment from Data Knowledge Information Data
    • 42. Knowledge Enrichment from DataBackgroundknowledge IntellegO Modified background knowledge EMR An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web, Applied Ontology 2011 Data Driven Knowledge Acquisition Method for Domain Knowledge Enrichment in Healthcare, BIBM 2012
    • 43. Knowledge Enrichment from Data From EMR From KB Diseases Symptoms fatigueatrial Fibrillation Is edema symptom of atrial fibrillation? syncope hypertension Is edema symptom of hypertension? Symptoms weight loss diabetes Is edema symptom of diabetes? chest pain chest pain discomfort in chest weight gain dizzy discomfort in chest shortness of breath rash skin nausea cough vomiting weight loss headache headache cough edema weight gain shortness of breath
    • 44. Knowledge Enrichment from Data Domains No of concepts 1008161 Cardiology Problems(diseases, symptoms) 125778with the above Orthopedics Procedures 262360 method Oncology Medicines 298993 + Neurology Medical Devices 33124 UMLS Etc…healthline.com druglib.com 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
    • 45. Healthcare Challenge• 80% unstructured healthcare data • Pose challenges in • Searching • Understanding • Mining • Knowledge discovery • Decision support• Evidence based medicine• Federal policies promote meaningful use
    • 46. Healthcare ChallengeCoding Complexity ICD-9 ICD-10Diagnostic Codes 14,000 69,000Procedure Codes 3,800 72,000 Clinical ICD-9 ICD-10 Conversion Documentation & (Current) (1st Oct,2014) Coding-Billing ChallengesExample: 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 offracture, which thigh was fractured, whether a delay in healing occurred etc.
    • 47. Healthcare Challenge Need to Do Better • Traditional methods doesn’t work• Understanding the context is crucial
    • 48. Healthcare Challenge – The Solution Decision Support Search NLP Mining + Semantics Knowledge Discovery Evidence-based Medicine
    • 49. ezHealthezCAC ezFIND ezMeasure ezCDI ezKB <problem value="Asthma" cui="C0004096"/> <med value="Losartan" code="52175:RXNORM" /> <med value="Spiriva" code="274535:RXNORM" /> <procedure value="EKG" cui="C1623258" /> ezNLP cTAKES www.ezdi.us
    • 50. 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
    • 51. Error DetectionEMR:1. “Sepsis due to urinary tract infection….”2. “Her prognosis is poor both short term and long term, however, wewill do everything possible to keep her alive and battle this infection." without IntellegO with usage of IntellegO Problem Problem SNM:40733004_infection SNM:68566005_infection_urinary_tract A syntax based NLP extractor By utilizing IntellegO and cardiology (such as Medlee) can extract background knowledge, we can more this term and annotate accurately annotate the term as asSNM:40733004_infection SNM:68566005_infection_urinary_tract
    • 52. Error DetectionEMR: ”The patient is to receive 2 fluid boluses." without IntellegO with IntellegO Problem Treatment SNM:32457005_body_fluid Fluid is part of buloses treatment, not a problem A syntax based NLP extractor By utilizing IntellegO and cardiology (such as Medlee) can extract background knowledge, we can determine this term and annotate that this is an incorrect annotation. asSNM:32457005_body_fluid
    • 53. Resolve InconsistencyThe balance of evidence would suggest NLPthat his episode of atrial fibrillation seems Patient has atrial fibrillationto be an isolated eventHe has had no documented atrial NLP Patient does not have atrialfibrillation since that time fibrillation Syncope Atrial Fibrillation Warfarin Atenolol Is_symptom_of Is_medication_for Aspirin
    • 54. Resolve InconsistencyShe denies any chest pain but is not really NLP Patient does not havefunction due to leg stiffness, swelling an shortness of breathshortness of breathRegarding the shortness of breath, we will NLPsend for a dobutamine stress Patient has shortness of breathechocardiogram Shortness of Breath Obesity Hypertension Sleep Apnea Is_symptom_of Obstructive
    • 55. PREscription Drug abuse Online Surveillance and Epidemiology - PREDOSE• Non-Medical Use of Prescr - iption Drugs • Fastest growing drug issue in US • Escalating accidental overdose deaths• Epidemiological Data Systems • Data collection practices • Data analysis limitations
    • 56. PREDOSE • Poor Scalability • Limited Reusability • Interoperability is challenging • Small sample sizeOf course, junkie that I am, I decided to repeat the experiment. Today, after waiting 48 hoursafter my last bunk 4 mg injection, I injected 2 mg. There wasnt really any rush to speak of,but after 5 minutes I started to feel pretty damn good. So I injected another 1 mg. That wasabout half an hour ago. I feel great now. http://wiki.knoesis.org/index.php/PREDOSE
    • 57. PREDOSEDescribe drug user’s knowledge, attitudes, andbehaviors related to illicit use of Prescription Drugs(Information extraction)Describe temporal patterns of non-medical use ofPrescription Drugs(Trend Detection)
    • 58. PREDOSEStage 3. Data Analysis and Interpretation Qualitative and Quantitative Analysis Scooner Cuebee of Drug User Knowledge, Attitudes and Behaviors 10 9 Semantic Web Tools Temporal Analysis for Trend DetectionStage 2. Automatic Coding Semantic Web Database Ontology Information Extraction Module 8 Schema 5 Natural 7 e.g. Opioid, Semantics-based Language Pain Pills Instances e.g. Suboxone, 6 + Techniques Processing Entity, Relationship, Sentiment = Subutex and Triple Extraction Triples/RDF DatabaseStage 1. Data Collection 1 3 2 4 Web Crawler Informal Text Web Forums Data Cleaning Data Store
    • 59. PREDOSEForum Y Entity (pre) Entity (confirmed) +ve Sentiment -ve Sentiment
    • 60. PREDOSEExtra-medical Use of LoperamideOpiated Effect Entity +ve Sentiment
    • 61. PREDOSEAll ForumsForum XForum YForum Z
    • 62. kHealthHealth information is now available from multiple sources • medical records • background knowledge • social networks • personal observations • sensors • etc. 68
    • 63. kHealth FitBit Community allows the automated collection and sharing of health-related data, goals, and achievementsFoursquare is an online application whichintegrates a persons physical location and Community of enthusiasts that share experiences ofsocial network. self-tracking and measurement. 69
    • 64. kHealthSensors, actuators, and mobile computing are playing anincreasingly important role in providing data for early phases ofthe health-care life-cycleThis 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 70
    • 65. kHealth 71
    • 66. kHealthPersonal Health Dashboard 72
    • 67. kHealthPersonal Health Dashboard Continuous Monitoring Personal Assessment Medical Service 1 2 3 Auxiliary Information – background knowledge, social/community support, personal context, personal medical history 73
    • 68. kHealth ? 74
    • 69. kHealth – Key IngredientsBackground Knowledge Personal Observations Social Network Input Personal Medical History 75
    • 70. kHealthObservations Abstractions 76
    • 71. kHealth - Technology observes Observer Quality sends sendsobservation inheres in focus perceives Perceiver Entity 77
    • 72. kHealth - Technology Background Knowledge asBi-partite Graph 79
    • 73. kHealth - TechnologyExplanation: is the act of choosing the objects or events that bestaccount for a set of observations; often referred to as hypothesisbuildingDiscrimination: is the act of finding those properties that, ifobserved, would help distinguish between multiple explanatoryfeatures 80
    • 74. kHealth - TechnologyExplanation Explanatory Feature: a feature that explains the set of observed properties ExplanatoryFeature ≡ ∃ssn:isPropertyOf—.{p1} ⊓ … ⊓ ∃ssn:isPropertyOf—.{pn} Observed Property Explanatory Feature elevated blood pressure Hypertension clammy skin Hyperthyroidism palpitations Pulmonary Edema 81
    • 75. kHealth - TechnologyDiscrimination Expected Property: would be explained by every explanatory feature ExpectedProperty ≡ ∃ssn:isPropertyOf.{f1} ⊓ … ⊓ ∃ssn:isPropertyOf.{fn} Expected Property Explanatory Feature elevated blood pressure Hypertension clammy skin Hyperthyroidism palpitations Pulmonary Edema 82
    • 76. kHealth - TechnologyDiscrimination Not Applicable Property: would not be explained by any explanatory feature NotApplicableProperty≡ ¬∃ssn:isPropertyOf.{f1} ⊓ … ⊓ ¬∃ssn:isPropertyOf.{fn} Not Applicable Property Explanatory Feature elevated blood pressure Hypertension clammy skin Hyperthyroidism palpitations Pulmonary Edema 83
    • 77. kHealth - TechnologyDiscrimination Discriminating Property: is neither expected nor not- applicable DiscriminatingProperty ≡ ¬ExpectedProperty ⊓ ¬NotApplicableProperty Discriminating Property Explanatory Feature elevated blood pressure Hypertension clammy skin Hyperthyroidism palpitations Pulmonary Edema 84
    • 78. kHealth Demo 87
    • 79. kHealth 88
    • 80. Thank You Visit Us @ www.knoesis.orgwith additional background at http://knoesis.org/amit/hcls

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