Computing for Human Experience: Semantics empowered Cyber-Physical, Social and Ubiquitous Computing beyond the Web
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The document discusses the evolving relationship between technology and human experience, emphasizing a shift from machine-centric to human-centric design in computing. It highlights the importance of integrating various data sources, such as health information, to empower individuals in managing their health while improving human capabilities through contextual assistance. Key enablers include semantic web technologies, multimodal data integration, and enhancing machine perception, leading to better communication and understanding between people and machines.
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Given
PCT problem P is a 4-tuple ⟨D, M, C, Γ⟩
• D is a finite set of disorders
• M is a finite set of manifestations
• C is the causation function [C : D ⟶ Powerset(M)]
• Γ is the set of observations [Γ ⊆ M ]
à Δ is a valid explanation (i.e., is a parsimonious cover)
Goal
Translate P into OWL, o(P), such that o(P) ⊧ Δ
Convert PCT to OWL
Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth, Pascal Hitzler. Representation of Parsimonious Covering
Theory in OWL-DL. Proceedings of the 8th International Workshop on OWL: Experiences and Directions (OWLED
2011), San Francisco, CA, USA, June 5-6, 2011.](https://image.slidesharecdn.com/che-on-the-move-keynote-2011-180407205900/85/Computing-for-Human-Experience-Semantics-empowered-Cyber-Physical-Social-and-Ubiquitous-Computing-beyond-the-Web-57-320.jpg)
Computing for Human Experience: Semantics empowered Cyber-Physical, Social and Ubiquitous Computing beyond the Web
- 1. 1 Computing for Human Experience Keynote at On-the-Move Federated Conference, October 2011: http://www.onthemove-conferences.org/ Amit Sheth Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, OH, USA Special thanks & contributions: Cory Henson
- 2. 2 with 7 billion people Our 1 world living 1 experience at a time
- 3. 3 with a stream of experiences to be … shared
- 4. 4 Today, technology increasingly engages individuals, society and humanity with … 5 billion mobile phones 40+ billion mobile sensors 1-2 billion computers http://www.gartner.com/it/page.jsp?id=703807
- 5. 5 With constant connectivity enabled through global networks 5 billion mobile phones 40+ billion mobile sensors 1-2 billion computers
- 6. 6 So, how can we leverage this tech to improve our experiences without losing ourselves in the process?
- 7. 7 Or drown in data?
- 8. 8 From machine-centric to human-centric design Machines to accommodate our experiences, as opposed to the other way around Computing to liberate
- 9. 9 To accomplish this requires a fundamental shift in how we interact and communicate with computational machines We must take a more holistic view of computation, as a shared universe, populated by people and machines working in harmony to achieve our highest aspirations
- 10. 10 We have caught glimpses of this vision … Man-Machine Symbiosis – T. O’Reilly Memex – V. Bush Ambient Intelligence – E. Zelkha, B. Epstein Ubiquitous Computing – M. Weiser
- 11. 11 Let this affect be positive The ways in which technology and humans interact are fundamentally changing In turn, our (human) experiences are changing Our activities, decisions, thoughts, and feelings are affected by the ubiquitous integration of technology into the fabric of our lives
- 12. 12 Computing for Human Experience Amit Sheth Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, OH, USA Special thanks & contributions: Cory Henson
- 14. 14 Human capabilities such as sensing, perception, attention, memory, decision making, control, etc. CHE is an approach to improving the human condition through computational means, and with minimal burden This may be achieved through the contextual assistance, augmentation, and absolution of human capabilities
- 15. 15 Consider the following example …
- 16. A cross-country flight from New York to Los Angeles on a Boeing 737 plane generates a massive 240 terabytes of data - GigaOmni Media 16
- 17. 17 But, how much data is generated regarding the health and well-being of the pilot or passengers? zero, none, zilch!
- 18. 18 Image the ability to monitor and control our health with the same care and precision that goes into the 737. And not just providing doctors with such control, but you and me.
- 19. 19 Health information is now available from multiple sources • medical records • background knowledge • social networks • personal observations • sensors • etc.
- 20. 20 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
- 21. 21 Unfortunately, when personal health data is collected and presented, it often looks like this … gibberish.
- 22. 22 Health Metrics with Meaning Personal Health Dashboard What is needed is a more intuitive and intelligent representation of our health. Image: http://bit.ly/lV2V73
- 23. 23 How is this accomplished?
- 24. 24 • Integration of heterogeneous, multimodal data • Bridging the physical-cyber-social divide • Elevating abstractions that machines and people understand • Semantics at an extraordinary Scale These enablers are brought together through Semantic Web technologies Key Enablers of CHE
- 25. 25 Foundation on which these enablers stand
- 26. 26 Foundation on which these enablers stand
- 27. 27 Integration of heterogeneous, multimodal data
- 28. 28 Background Knowledge: ontologies, knowledge bases, LOD, databases, etc. Social/Community Data: social network data, wisdom of the crowds, etc. Sensor Data: observations from machine sensors, citizen sensors (i.e., patients, doctors), laboratory experiments, etc. Personal Context: location, schedule, items (e.g., accessible sensors), etc. Personal Medical History: Electronic Medical Records, Personal Health Records, Patient Visit Records, etc. Integration of heterogeneous, multimodal data
- 29. 29 Communications using online technologies to share opinions, insights, experiences and perspectives with each other. What is Social Media?
- 30. 30 Blogs – DiabetesMine, HealthMatters, WebMD, NYT HealthBlog, etc. Microblogs – Livestrong, Stupid Cancer, etc. Social Networks – OrganizedWisdom, PatientsLikeMe, DailyStrength, NursesRecommendDoctors, CureTogether, etc. Podcasts – John Hopkins Medical Podcasts, Mayo Clinic, etc. Forums – Revolution Health Groups, Google Health Groups, etc. Popular types of Healthcare Social Media
- 31. 31 HCPs aren’t waiting to be detailed, they’re turning to the social web to educate themselves 60% of physicians either use or are interested in using social networks 65% of docs plan to use social media for professional development Manhattan Research 2009, 2010 Sermo,com Compete.com This doc-to-doc blogger has 53,000 readers this month + 20,000 Twitter followers 112,000 docs talk to each other on Sermo. http://www.slideshare.net/IQLab/social-media-101-for-pharma-3494462 60% of physicians either use or are interested in using social networks
- 32. 32 http://www.slideshare.net/IQLab/social-media-101-for-pharma-3494462 People are turning to each other online to understand their health
- 33. 33 83% of online adults search for health information http://www.slideshare.net/IQLab/social-media-101-for-pharma-3494462
- 34. 34 83% of online adults search for health information 60% of them look for the experience of “someone like me” http://www.slideshare.net/IQLab/social-media-101-for-pharma-3494462
- 35. 35 "I don't know, but I can try to find out" is the default setting for people with health questions. Savannah Fox, The Social Life of Health Information, Pew Internet Report, May 12, 2011. Available at http://www.pewinternet.org/Reports/2011/Social-Life-of-Health-Info.aspx "I know, and I want to share my knowledge" is the leading edge of health care.
- 36. 36 Intra Community Activity and connectivity – How well connected are individual nodes (People) – What keeps them strongly connected over time (Relationship types - Knowledge of Content) Inter-Community Connectivity • Any bridges to connect to the other community? (People) • Any Similarity in actions with the other community (Can Content help?)Image: http://themelis-cuiper.com Will the two communities coordinate well during an event- crisis or disaster? • Interplay between all three dimensions – P, C, N People-Content-Network Analysis For more info: http://www.slideshare.net/knoesis/understanding-usercommunity-engagement- by-multifaceted-features-a-case-study-on-twitter
- 37. 37 People-Content-Network Analysis External Knowledge bases Dynamic Domain Model for the event Event oriented Community Social Network Mined User Interests and User Types User Profiles SEMANTIC ASSOCIATION TO UNDERSTAND ENHANCED ENGAGEMENT LEVEL
- 38. 38 Bridging the physical-cyber-social divide Computation is no longer confined to pure symbol manipulation. The previously strict relations between the digital and physical world are blurring.
- 39. 39 Psyleron’s Mind-Lamp (Princeton U), connections between the mind and the physical world. Neuro Sky's mind-controlled headset to play a video game. MIT’s Fluid Interface Group: wearable device with a projector for deep interactions with the environment Bridging the physical-cyber divide
- 40. 40 Foursquare is an online application which integrates a persons physical location and social network. Bridging the physical-cyber-social divide 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
- 41. 41 Tweeting Sensors sensors are becoming social Bridging the physical-cyber-social divide
- 42. 42 Bridging the physical-cyber-social divide Select topicSelect date Topic tree Spatial Marker N-gram summaries Wikipedia articles Reference newsRelated tweets Images & Videos Tweet traffic Sentiment Analysis Network Analysis Community Interaction of Various user types For more info: http://twitris.knoesis.org/
- 43. 43 Heliopolis is a suburb of Cairo. Dynamic Model Creation Continuous Semantics
- 44. 44 Events “Both Ahmadinejad & Mousavi declare victory in Iranian Elections.” “situation in tehran University is so worrisome. police have attacked to girls dormitory #tehran #iranelection” “Reports from Azadi Square - 4 people killed by police, people killed police who shot. More shots being fired #iranelections” June 12 2009 June 13 2009 June 15 2009 KeyphrasesModels Ahmadinejad & Mousavi are politicians in Iran Tehran University is a University in Iran Azadi Square is a city square in Tehran Dynamic Model Creation
- 45. 45 The design and building of physical-cyber-social systems requires effective conceptualization and communication between people and machines. To reach this vision requires advancement in the area of machine perception, enabling machines the ability to abstract over low-level observations.
- 46. 46 Abstraction Abstraction provides the ability to interpret and synthesize information in a way that affords effective understanding and communication of ideas, feelings, perceptions, etc. between machines and people.
- 47. 47 The process of interpreting stimuli is called perception; and studying this extraordinary human capability can lead to insights for developing effective machine perception. People are excellent at abstraction; of sensing and interpreting stimuli to understand and interact with the world.
- 48. “real-world” conceptualization of “real-world” Sensor Sensor Data / Social Data observation perception Physical Cyber Social 48
- 49. 49 Both people and machines are capable of observing qualities, such as redness. * Formally described in a sensor/observation ontology observes Observer Quality
- 50. 50 Sensor and Sensor Network (SSN) Ontology http://www.w3.org/2005/Incubator/ssn/XGR-ssn/
- 51. 51 The ability to perceive is afforded through the use of background knowledge, relating observable qualities to entities in the world. * Formally described in domain ontologies (and knowledge bases) inheres in Quality Entity
- 53. 53 With the help of sophisticated inference, both people and machines are also capable of perceiving entities, such as apples. • the ability to degrade gracefully with incomplete information • the ability to minimize explanations based on new information • the ability to reason over data on the Web • fast (tractable) perceives EntityPerceiver
- 54. 54 minimize explanations degrade gracefully tractable Web reasoning Abductive Logic high complexity Deductive Logic (e.g., OWL) (relatively) low complexity Perceptual Inference (i.e., abstraction) Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth, Pascal Hitzler. Representation of Parsimonious Covering Theory in OWL-DL. Proceedings of the 8th International Workshop on OWL: Experiences and Directions (OWLED 2011), San Francisco, CA, USA, June 5-6, 2011.
- 55. 55 • Goal is to account for observed symptoms with plausible explanatory hypotheses (abductive logic) • Driven by background knowledge modeled as a bipartite graph causally linking disorders to manifestations Yun Peng, James A. Reggia, "Abductive Inference Models for Diagnostic Problem-Solving" m1 m2 m3 d1 d2 d3 m4 disorder manifestationcauses explanation observations Parsimonious Covering Theory
- 56. 56 PCT Parsimonious Cover • coverage: an explanation is a cover if, for each observation, there is a causal relation from a disorder contained in the explanation to the observation • parsimony: an explanation is parsimonious, or best, if it matches some criteria of suitability (i.e., single disorder assumption)
- 57. 57 Given PCT problem P is a 4-tuple ⟨D, M, C, Γ⟩ • D is a finite set of disorders • M is a finite set of manifestations • C is the causation function [C : D ⟶ Powerset(M)] • Γ is the set of observations [Γ ⊆ M ] à Δ is a valid explanation (i.e., is a parsimonious cover) Goal Translate P into OWL, o(P), such that o(P) ⊧ Δ Convert PCT to OWL Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth, Pascal Hitzler. Representation of Parsimonious Covering Theory in OWL-DL. Proceedings of the 8th International Workshop on OWL: Experiences and Directions (OWLED 2011), San Francisco, CA, USA, June 5-6, 2011.
- 58. 58 headache extreme exhaustion severe ache and pain stuffy nose sneezing sore throat severe cough mild ache and pain mild cough flu cold fever disorder manifestationcauses PCT Background Knowledge in OWL disorders (D) for all d ∈ D, write d rdf:type Disorder ex: flu rdf:type Disorder cold rdf:type Disorder manifestations (M) for all m ∈ M, write m rdf:type Manifestation ex: fever rdf:type Manifestation headache rdf:type Manifestation … causes relations (C) for all (d, m) ∈ C, write d causes m ex: flu causes fever flu causes headache … Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth, Pascal Hitzler. Representation of Parsimonious Covering Theory in OWL-DL. Proceedings of the 8th International Workshop on OWL: Experiences and Directions (OWLED 2011), San Francisco, CA, USA, June 5-6, 2011.
- 59. 59 observations (Γ) for mi ∈ Γ, i =1 … n, write Explanation owl:equivalentClass causes value m1 and … causes value mn ex: Explanation owl:equivalentClass causes value sneezing and causes value sore-throat causes value mild-cough explanation (Δ) Δ rdf:type Explanation, is deduced ex: cold rdf:type Explanation flu rdf:type Explanation and PCT Observations and Explanations in OWL Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth, Pascal Hitzler. Representation of Parsimonious Covering Theory in OWL-DL. Proceedings of the 8th International Workshop on OWL: Experiences and Directions (OWLED 2011), San Francisco, CA, USA, June 5-6, 2011.
- 60. 60 The ability to perceive efficiently is afforded through the cyclical exchange of information between observers and perceivers. Traditionally called the Perception Cycle (or Active Perception) sends focus sends observation Observer Perceiver
- 62. 62 Cognitive Theories of Perception (timeline) 1970’s – Perception is an active, cyclical process of exploration and interpretation. - Nessier’s Perception Cycle 1980’s – The perception cycle is driven by background knowledge in order to generate and test hypotheses. - Richard Gregory (optical illusions) 1990’s – In order to effectively test hypotheses, some observations are more informative than others. - Norwich’s Entropy Theory of Perception
- 63. 63 Key Insights • Background knowledge plays a crucial role in perception; what we know (or think we know/believe) influences our perception of the world. • Semantics will allow us to realize computational models of perception based on background knowledge. • Internet/Web expands our background knowledge to a global scope; thus our perception is global in scope • Social networks influence our knowledge and beliefs, thus influencing our perception Contemporary Issues
- 64. 64 observes inheres in Integrated together, we have an general model – capable of abstraction – relating observers, perceivers, and background knowledge. perceives sends focus sends observation Observer Quality EntityPerceiver
- 65. 65 Modeled in set-theoretic notation with components mapped to Parsimonious Covering Theory and OWL Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth. An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web. Applied Ontology, 2011 (accepted).
- 67. 67 Weather Application Detection of events, such as blizzards, from weather station observations on LinkedSensorData Weather Application Demos: Real-Time Feature Streams
- 68. 68 Weather ApplicationSECURE: Semantics Empowered Rescue Environment Rescue robots detect different types of fires, which may require different methods/tools to extinguish, and relays this knowledge to first responders. Demo: SECURE: Semantics Empowered Rescue Environment
- 69. 69 Weather ApplicationHealthcare Application Detection of errors in Electronic Medical Records and missing knowledge in a cardiology domain model EMR
- 70. 70 Weather ApplicationHealthcare Application EMR: "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 without IntellegO with IntellegO Problem Problem
- 71. 71 Weather ApplicationHealthcare Application EMR: ”The patient is to receive 2 fluid buloses." SNM:32457005_body_fluid A syntax based NLP extractor (such as Medlee) can extract this term and annotate as SNM:32457005_body_fluid without IntellegO 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 an incorrect annotation. Treatment
- 72. 72 In 2008, the rate of data generation surpassed storage capacity. With 7 billion people, and a growing number of sensors, how can such a such a system scale? By shining a light on relevant human experience, supported by knowledge, while dimming the minutia of data. Semantic Scalability http://gigaom.com/cloud/sensor-networks-top-social-networks-for-big-data-2
- 73. 73 Semantic Scalability ex. – keyword based search/index, non-textual data, multimodal data for an event It is clear that purely syntax-based solutions will not scale
- 74. 74 Semantic Scalability 1. Focusing attention on important information and ignoring irrelevant data 2. Converting low-level data (observations) to high-level knowledge (abstractions) 3. Utilizing CHE technology to more evenly distribute responsibility and activities among people and machines Path to Web scale semantics
- 75. 75 We were able to demonstrate 50% savings in sensing resource requirements during the detection of a blizzard. 1. Focusing attention on important information and ignoring irrelevant data Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth. An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web. Applied Ontology, 2012. (accepted)
- 76. 76 2. Converting low-level data to high-level knowledge (observations to abstractions) Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth. An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web. Applied Ontology, 2012. (accepted) Experiment – during a blizzard, we utilized Intelleg0 to collect and analyze over 110,000 sensor observations, from: • 800 weather stations (~5 sensors per station) • across 5 states (Utah, Nevada, Colorado, Wyoming, and Idaho) • for 6 days (April 1 – 6, 2003)
- 77. 77 2. Converting low-level data to high-level knowledge (observations to abstractions) We were able to demonstrate an order of magnitude resource savings between storing observations vs. relevant abstractions Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth. An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web. Applied Ontology, 2012. (accepted)
- 78. 78 2. Converting low-level data to high-level knowledge (observations to abstractions) Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth. An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web. Applied Ontology, 2012. (accepted) While this is a good result, the benefit provided for a single person – a single experience – is far more dramatic.
- 79. There are almost 7 billion people on earth, and only ~10-15 million doctors (~700:1 - 467:1) 3. Utilizing CHE technology to more evenly distribute responsibility and activities among people and machines 79
- 80. 80 Providing people with the tools to monitor and manage their own health will dramatically reduce the burden on doctors, and improve the health of the people These doctors are severely overburdened, answering less than 60% of questions posed by patients regarding their health and well being
- 81. 81 The health-care ecosystem of the future includes machines, people, and social networks continuously, ubiquitously, and unobtrusively monitoring and managing our health
- 82. 82 CHE approach to Health Care 1 à 2 à 3 Continuous Monitoring Personal Assessment Medical Service Auxiliary Information – background knowledge, social/community support, personal context, personal medical history
- 83. 83 Continuous Monitoring Phase Monitoring health metrics and vital signs utilizing unobtrusive body sensors Continuously collecting information, watching for worrisome symptoms
- 84. 84 Personal Assessment Phase Assessment of symptoms from personal observation and/or health sensors available at home. Utilizing background knowledge, personal medical history, and current sensor data to formulate and ask specific questions of patient that will aid in explaining symptoms.
- 85. 85 Medical Services Phase Assessment of symptoms gathered from continuous and personal phases, with additional sophisticated equipment, advanced treatment, and specialized medical knowledge not previously available. Utilizing background knowledge, personal medical history, and current sensor data to formulate a diagnosis.
- 86. 86 Continuous Personal Medical Personal Medical History (e.g., Electronic Medical Record, genomic sequence) Background Knowledge (e.g., Ontologies, Knowledgebases) Auxiliary Information Symptoms/ Explanations Symptoms/ Explanations access & update (PHR) access & update (PHR) access & update (EMR) CHE approach to Health Care Personal Context (e.g., available sensors, location, schedule) Social/Community Support (e.g., Patient Network, crowd sourcing)
- 87. 87 Continuous Monitoring Phase: Example • Abnormal heart rate • Clammy skin • Panic Disorder • Hypoglycemia • Hyperthyroidism • Heart Attack • Septic Shock • Check phone for instructions• Patient has history of Heart Disease Observed Symptoms Possible Explanations Electronic Medical Record Health Alert
- 88. 88 Basis is a wrist-watch that also monitors pulse rate, movement, temperature, and galvanic skin response. Continuous Phase Technology
- 89. 89 Fitbit Tracker uses a MEMS 3-axis accelerometer that measures your motion patterns to tell you your calories burned, steps taken, distance traveled, and sleep quality. Continuous Phase Technology
- 90. 90 Personal Assessment Phase: Example Are you feeling lightheaded? Are you have trouble taking deep breaths? yes yes 1. Take medication: Methimazole 2. See doctor: how about Tues. @ 11am? • Patient has history of Hyperthyroidism • Patient has prescription for Methimazole Have you taken your Methimazole medication? Do you have low blood pressure when standing? yes • Abnormal heart rate • Clammy skin • Lightheaded • Trouble breathing • Low blood pressure • Panic Disorder • Hypoglycemia • Hyperthyroidism • Heart Attack • Septic Shock Observed Symptoms Possible Explanations Electronic Medical Record Health Alert no
- 91. 91 Lark is a sleep sensor that monitors circadian rhythms and functions as an "un- alarm," vibrating to wake you at a point in your sleep cycle when you feel alert, not groggy. Personal Phase Technology
- 92. 92 Instant Heart Rate takes your pulse when you place your finger over your phone’s camera lens. The app uses light from the camera flash to detect color changes caused by blood moving through your finger. Personal Phase Technology
- 93. 93 Telcare makes a blood glucose meter (right) for diabetics that broadcasts readings to a mobile-phone app (center) where patients can see results and set goals. Personal Phase Technology
- 94. 94 iBGStar is a plug-in glucose meter for the iPhone, developed by Sanofi-Aventis, providing the ability for patients to monitor and manage Diabetes. Personal Phase Technology
- 95. 95 Withings Blood Pressure Monitor provides easy and convenient blood pressure readings in the convenience of home. Personal Phase Technology
- 96. 96 WebMD provides a wealth of health information and an application to diagnose symptoms. Personal Phase Technology
- 97. 97 Medical Services Phase: Example • Patient has history of Hyperthyroidism • Patient has prescription for Methimazole Are your blood sugar levels low? • Abnormal heart rate • Clammy skin • Lightheaded • Trouble breathing • Low blood pressure • Hypoglycemia • Hyperthyroidism Observed Symptoms Possible Explanations Electronic Medical Record
- 99. 99 Health Guard provides a secure way to store and analyze health records for casual browsing or emergency use (i.e., MS Health Vault records). Medical Phase Technology
- 100. 100 Mobile MIM gives physicians a sophisticated, hands-on mobile system for viewing and annotating radiology images, such as CT scans. Medical Phase Technology
- 101. 101 Dr. Watson is a health and medical question and answering system developed by IBM, utilizing supercomputer intelligence for medical diagnostics. Medical Phase Technology
- 102. 102 Continuous Personal Medical Personal Medical History (e.g., Electronic Medical Record, genomic sequence) Background Knowledge (e.g., Ontologies, Knowledgebases) Auxiliary Information Symptoms/ Explanations Symptoms/ Explanations access & update (PHR) access & update (PHR) access & update (EMR) CHE approach to Health Care Personal Context (e.g., available sensors, location, schedule) Social/Community Support (e.g., Patient Network, crowd sourcing)
- 103. 103 Improving the experience of health-care improves all other experiences CHE holds the potential to revolutionize the practice of health-care by embracing the relationship between ourselves, our machines, and our health
- 104. 104 “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” – M. Weiser Physical-Cyber-Social Abstraction Integration Scalability
- 105. 105 thank you, and please visit us at http://knoesis.org Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, Ohio, USA More: Vision Paper: Computing for Human Experience: http://wiki.knoesis.org/index.php/Computing_For_Human_Experience Computing for Human Experience