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Healthcare innovations at Kno.e.sis sept2016

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Reloaded Sept 2016 presentation which Slideshare lost!

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Healthcare innovations at Kno.e.sis sept2016

  1. 1. 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
  2. 2. 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
  3. 3. Top organization in WWW: 10-yr Field Rating 3
  4. 4. • 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
  5. 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
  6. 6. 7 Healthcare Innovation at Kno.e.sis (with subset of applications) Personalized Digital Health
  7. 7. 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
  8. 8. 9 Collaborators
  9. 9. The Patient of the Future MIT Technology Review, 2012 http://www.technologyreview.com/featuredstory/426968/the-patient-of-the-future/ 10
  10. 10. 11 kHealth: Knowledge empowered personalized digital mhealth With applications to: Asthma, Dementia, ADHF, GI, (other chronic disease) Contact: Prof. Amit Sheth
  11. 11. Brief Introduction Video
  12. 12. 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
  13. 13. 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
  14. 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
  15. 15. 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
  16. 16. 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
  17. 17. 18 kHealth to Manage ADHF (Acute Decompensated Heart Failure)
  18. 18. 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> <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 - continue Not Well Controlled – contact nurse Poor Controlled – contact doctor Health Signal Extraction to Understanding
  19. 19. 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
  20. 20. 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
  21. 21. 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
  22. 22. 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
  23. 23. 26 PREDOSE: Social media analysis driven epidemiology Application: Prescription drug abuse and beyond Contact: Delroy Cameron
  24. 24. 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
  25. 25. 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
  26. 26. 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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
  28. 28. 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 Classes 37 Properties 33:1 Buprenorphine 24:1 Loperamide 31
  29. 29. 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
  30. 30. 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
  31. 31. 35 EMR and clinical text analysis: Intelligence from clinical data Contact: Sujan Parera
  32. 32. • 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
  33. 33. 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
  34. 34. 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
  35. 35. 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
  36. 36. 43 Social Health Signals Contact: Ashutosh Jadhav
  37. 37. • 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
  38. 38. 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
  39. 39. 46 Search and Explore Top health news Faceted search (by health topics) Social Health Signals
  40. 40. 47 On going projects
  41. 41. 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
  42. 42. kHealth - Dementia Principal Investigators: Tanvi Banerjee Mentors: Amit Sheth, Larry Lawhorne Students: …. Ohio Center of Excellence in Knowledge-Enabled Computing 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
  43. 43. 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 Ohio Center of Excellence in Knowledge-Enabled Computing  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)
  44. 44. eDrug Trends Ohio Center of Excellence in Knowledge-Enabled Computing 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
  45. 45. 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 Ohio Center of Excellence in Knowledge-Enabled Computing  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)
  46. 46. 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 Ohio Center of Excellence in Knowledge-Enabled Computing  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)
  47. 47. 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
  48. 48. • 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
  49. 49. • 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
  50. 50. Sheth Group: All Funded
  51. 51. 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

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