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This talk, given to the executive committee of the Boonsoft School of Medicines summarizes/introduces some of the projects on clinical and healthcare applications, and health informatics including consumer health behavior and social media use in healthcare. I focus on personalized digital health, handling/mining of healthcare big data, high-level description of innovations and especially applications involving clinical partners that empower patients, support better clinical decision making, reduce clinician's information overload, or improve clinical outcomes. [Because some of the evaluations are undergoing now, some of these benefits are yet to be quantitatively and qualitatively assessed.] 2 min. video on a Personalized Digital Health application (Asthma control in Children): Also see: for related information.

Text of Healthcare innovations at Kno.e.sis

  • Healthcare Innovations at Kno.e.sis Put Knoesis Banner Presentation to the Boonshoft School of Medicine Executive Committee, July 10, 2014 Amit Sheth Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) Wright State University, USA
  • Among top universities in the world in World Wide Web (cf: 10-yr impact, Microsoft Academic Search: among top 10 in June2014) Largest academic group in the US in Semantic Web + Social/Sensor Webs, Mobile/Cloud/Cognitive Computing, Big Data, IoT, Health/Clinical & Biomedicine Applications Exceptional student success: internships and jobs at top salary (IBM Watson/Research, MSR, Amazon, CISCO, Oracle, Yahoo!, Samsung, research universities, NLM, startups ) 100 researchers including 15 World Class faculty (>3K citations/faculty) and ~45 PhD students- practically all funded Extensive research for largely multidisciplinary projects; world class resources; industry sponsorships/collaborations (Google, IBM, ) 2
  • Amit Sheths PHD students Ashutosh Jadhav Hemant Purohit Vinh Nguyen Lu Chen Pavan Kapanipathi Pramod Anantharam Sujan Perera Alan Smith Swapnil Soni Maryam Panahiazar Sarasi Lalithsena Shreyansh Batt Kalpa Gunaratna Delroy Cameron Sanjaya Wijeratne Wenbo Wang Kno.e.sis in 2014 = ~100 researchers (15 faculty, ~50 PhD students) 3 Special thanks Special thanks Special thanks Special thanks Special thanks: This presentation covers some of the work of these researchers.
  • 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 4 Healthcare is changing way too fast
  • The Patient of the Future MIT Technology Review, 2012 5
  • 6 Collaborators
  • 7 Healthcare Innovation at Kno.e.sis (with subset of applications)
  • 8 kHealth: Knowledge empowered personalized digital mhealth With applications to: ADHF, GI, Asthma, [Geriatrics] Contact: Prof. Amit Sheth
  • Brief Introduction Video
  • 10 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
  • Weather Application 11 Detection of events, such as wheezing sound, indoor temperature, humidity, dust, and CO2 level Weather ApplicationAsthma Healthcare Application Action in the Physical World Close the window at home during day to avoid CO2 inflow, to avoid asthma attacks at night Public Health Personal Population Level FOR human: Improving Human Experience
  • 12 Making sense of sensor data with
  • Through physical monitoring and analysis, our cellphones could act as an early warning system to detect serious health conditions, and provide actionable information canary in a coal mine knowledge-enabled healthcare 13 kHealth
  • 14 kHealth to Manage ADHF (Acute Decompensated Heart Failure)
  • 15 1 2 3Akinbami et al. (2009). Status of childhood asthma in the United States, 19802007. 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
  • 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
  • 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 17 Personal, Public Health, and Population Level Signals for Monitoring Asthma
  • 18 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
  • 19 Population Level Personal Wheeze Yes Do you have tightness of chest? Yes ObservationsPhysical-Cyber-Social System Health Signal Extraction Health Signal Understanding Wheezing ChectTightness PollenLevel Pollution Activity Wheezing ChectTightness PollenLevel Pollution Activity 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
  • 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 youre miserable! Very High Alert for B-ALLERGEN Ragweed I-ALLERGEN pollen. B-FACILITY Oklahoma I-FACILITY Allergy I-FACILITY Clinic says its 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
  • intelligence at the edge Approach 1: Send all sensor observations to the cloud for processing Approach 2: downscale semantic processing so that each device is capable of machine perception 21 Henson et al. 'An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices, ISWC 2012.
  • Use bit vector encodings and their operations to encode prior knowledge and execute semantic reasoning 010110001101 0011110010101 1000110110110 101100011010 0111100101011 000110101100 0110100111 22 Efficient execution of machine perception
  • O(n3) < x < O(n4) O(n) Efficiency Improvement Problem size increased from 10s to 1000s of nodes Time reduced from minutes to milliseconds Complexity growth reduced from polynomial to linear 23 Evaluation on a mobile device
  • 2 Prior knowledge is the key to perception Using SW technologies, machine perception can be formalized and integrated with prior knowledge on the Web 3 Intelligence at the edge By downscaling semantic inference, machine perception can execute efficiently on resource-constrained devices 1 Translate low-level data to high-level knowledge Machine perception can be used to convert low-level sensory signals into high-level knowledge useful for decision making 24 Semantic Perception for smarter analysis: 3 ideas to takeaway
  • 25 PREDOSE: Social media analysis driven epidemiology Application: Prescription drug abuse and beyond Contact: Delroy Cameron
  • 26 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 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
  • In 2008, there were 14,800 prescription painkiller deaths* * 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 27
  • Early Identification and Detection of Trends Access hard-to-reach Populations Large Data Sample Sizes Group Therapy: 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 28
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