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A Semantics-based Approach to Machine Perception
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Transcript of A Semantics-based Approach to Machine Perception
A SEMANTICS-BASED APPROACHTO MACHINE PERCEPTION
Cory Andrew Henson
August 27, 2013
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Committee: Amit Sheth (advisor)
Krishnaprasad Thirunarayan John Gallagher
Payam Barnaghi Satya Sahoo
Ph.D. Dissertation Defense
Wright State UniversityDept. of Computer Science and Engineering
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Thesis
Machine perception can be formalized using semantic web technologies in order to derive abstractions from sensor data using background knowledge on the Web, and efficiently executed on resource-constrained devices.
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3 primary issues to be addressed
Annotation of sensor data
SemanticSensor
Web
SemanticPerception
Intelligence
at the Edge
Interpretation of sensor data
Efficient execution onresource-constrained devices1 2 3
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lives in
has pet
is ahas petPerson Animal
Concrete Facts Resource Description Framework
Semantic Web(according to Farside)
General Knowledge Web Ontology Language
“Now! – That should clear up a few things around here!”
is a
Government
Media
Publications
Life SciencesGeographic
Semantic Web
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7http://www.opengeospatial.org/projects/groups/sensorwebdwg
Semantic Sensor Web
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Sensor systems are too often stovepiped
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With freedom comes responsibility1. discovery, access, and
search2. integration and
interpretation
We want to set this data free
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OGC Sensor Web Enablement (SWE)
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With freedom comes responsibility1. discovery, access, and
search2. integration and
interpretation
We want to set this data free
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RDF OWL
How are machines supposed to integrate and interpret sensor data?
Semantic Sensor Networks (SSN)
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W3C Semantic Sensor Network Ontology
Lefort, L., Henson, C., Taylor, K., Barnaghi, P., Compton, M., Corcho, O., Garcia-Castro, R., Graybeal, J., Herzog, A., Janowicz, K., Neuhaus, H., Nikolov, A., and Page, K.: Semantic Sensor Network XG Final Report, W3C Incubator Group Report (2011).
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W3C Semantic Sensor Network Ontology
Lefort, L., Henson, C., Taylor, K., Barnaghi, P., Compton, M., Corcho, O., Garcia-Castro, R., Graybeal, J., Herzog, A., Janowicz, K., Neuhaus, H., Nikolov, A., and Page, K.: Semantic Sensor Network XG Final Report, W3C Incubator Group Report (2011).
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W3C Semantic Sensor Network Ontology
Lefort, L., Henson, C., Taylor, K., Barnaghi, P., Compton, M., Corcho, O., Garcia-Castro, R., Graybeal, J., Herzog, A., Janowicz, K., Neuhaus, H., Nikolov, A., and Page, K.: Semantic Sensor Network XG Final Report, W3C Incubator Group Report (2011).
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Semantic Annotation of SWE
Lefort, L., Henson, C., Taylor, K., Barnaghi, P., Compton, M., Corcho, O., Garcia-Castro, R., Graybeal, J., Herzog, A., Janowicz, K., Neuhaus, H., Nikolov, A., and Page, K.: Semantic Sensor Network XG Final Report, W3C Incubator Group Report (2011).
Semantic Sensor Observation Service (SemSOS)
Cory Henson, Josh Pschorr,Amit Sheth, Krishnaprasad Thirunarayan, SemSOS: Semantic Sensor Observation Service, In Proceedings of the 2009 International Symposium on Collaborative Technologies and Systems (CTS 2009), Baltimore, MD, May 18-22, 2009. 17
Semantic Sensor Observation Service (SemSOS)
Joshua Pschorr, Cory Henson, Harshal Patni, and Amit P. Sheth. Sensor Discovery on Linked Data. Kno.e.sis Center Technical Report 2010.
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3 primary issues to be addressed
Annotation of sensor data
SemanticSensor
Web
SemanticPerception
Intelligence
at the Edge
Interpretation of sensor data
Efficient execution onresource-constrained devices1 2 3
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Semantic Perception
• The role of perception is to transform raw sensory data into a meaningful and correct representation of the external world.
• The systematic automation of this ability is the focus of machine perception.
• For correct interpretation of this representation, we need a formal account of how this is done.
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What can we learn from cognitive models of
perception?
People are good atmaking sense
of sensory input
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Perception is an active, cyclical process of exploration and interpretation.
The perception cycle is driven by prior knowledge, in order to generate and test hypotheses.
Some observations are more informative than others (in order to effectively test hypotheses*).
Ulric Neisser
Richard GregoryKenneth Norwich
1970’s 1980’s 1990’s
Cognitive Models of Perception
* Applies to machine perception within Intellego, NOT human perception.
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ObservedPropertie
s
PerceivedFeatures
Background knowledge
Explanation
Ontology of Perception
Focus
An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web (Applied Ontology, 2011)
Example: Medical diagnosis as perception
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Proactive,Preventative
Healthcare
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The Patient of the Future
MIT Technology Review, 2012
http://www.technologyreview.com/featuredstory/426968/the-patient-of-the-future/
Digital Doctor
Let’s provide people with the tools needed to monitor and
manage their own health
http://worldofdtcmarketing.com/mobile-health-apps-a-new-opportunity-for-healthcare-marketers/mobile-healthcare-marketing-trends/ 27
Medical/healthcare expert systems have been around for a long time
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1. Ubiquitous Sensing 2. Always-on Computing3. Knowledge on the
Web
3 recent developments have changed the technological landscape …
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Making sense of sensor data with
DATAsensor observations
KNOWLEDGEsituation awareness useful
for decision making
Primary challenge is to bridge the gap between data and knowledge
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SSNOntology
2 Interpreted data(deductive)[in OWL] e.g., threshold
1 Annotated Data[in RDF]e.g., label
0 Raw Data[in TEXT]e.g., number
Levels of Abstraction
3 Interpreted data (abductive)[in OWL]e.g., diagnosis
Intellego
“150”
Systolic blood pressure of 150 mmHg
ElevatedBlood
Pressure
Hyperthyroidism
less
use
ful …
…
mor
e us
eful
……
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ObservedPropertie
s
PerceivedFeatures
Background knowledgeon the Web
Low-level observed properties suggest explanatory hypotheses through abduction
Explanation
Focus
Ontology of Perception
An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web (Applied Ontology, 2011)
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Semantics of Explanation
Abduction – or, inference to the best EXPLANATION
Task• Given background knowledge of the environment (SIGMA), and• given a set of sensor observation data (RHO),• find a consistent explanation of the situation (DELTA)
Backgroundknowledge Features
(objects/events)in the world
Sensor observation
data
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Semantics of Explanation
Background knowledge is represented as a causal network between features (objects or events) in the world and the sensor observations they give rise to.
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Semantics of Explanation
Finding the sweet spot between abduction and OWL
• Simulation of Parsimonious Covering Theory in OWL-DL (using the single-feature assumption*)
* An explanation must be a single feature which
accounts forall observed properties
Semantic Perception: Converting Sensory Observations to Abstractions (IEEE Internet Computing, 2012)
Theorem: Given a PCT problem P and its translation o(P) into OWL, Δ = {e} is a PCT explanation if and only if ExplanatoryFeature(e) is deduced by an OWL-DL reasoner — that is, if and only if o(P) ⊧ ExplanatoryFeature(e).
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Finding the Sweet Spot
minimizeexplanations
degrade gracefullyw/ incomplete info
decidable
web reasoning
Abductive Logic (e.g., PCT)high complexity
Deductive Logic (e.g., OWL)(relatively) low complexity
Explanation(in Intellego)
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Explanatory Feature: A feature is explanatory w.r.t. a set of observed properties if it causes each property in the set.
ExplanatoryFeature ≡ isPropertyOf∃ —.{p1} … isPropertyOf⊓ ⊓ ∃ —.{pn}
elevated blood pressure
clammy skin
palpitations
Hypertension
Hyperthyroidism
Pulmonary Edema
Observed Property Explanatory Feature
Semantics of Explanation
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ObservedPropertie
s
PerceivedFeatures
Background knowledgeon the Web
Hypotheses imply the informational value of future observations through deduction
Explanation
Focus
Ontology of Perception
An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web (Applied Ontology, 2011)
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Universe of observable properties
Semantics of Focus
To predict which future observations have informational value, find those observable properties that can discriminate between the set of hypotheses.
ExpectedProperties
Not-applicableProperties
Discriminating
Properties
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Expected Property: A property is expected w.r.t. a set of features if it is caused by each feature in the set.
ExpectedProperty ≡ isPropertyOf.{f∃ 1} … isPropertyOf.{f⊓ ⊓ ∃ n}
elevated blood pressure
clammy skin
palpitations
Hypertension
Hyperthyroidism
Pulmonary Edema
Expected Property Explanatory Feature
Semantics of Focus
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Not Applicable Property: A property is not-applicable w.r.t. a set of features if it is not caused by any feature in the set.
NotApplicableProperty ≡ ¬∃isPropertyOf.{f1} … ¬⊓ ⊓ ∃isPropertyOf.{fn}
elevated blood pressure
clammy skin
palpitations
Hypertension
Hyperthyroidism
Pulmonary Edema
Not Applicable Property Explanatory Feature
Semantics of Focus
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Discriminating Property: A property is discriminating w.r.t. a set of features if it is neither expected nor not-applicable.
DiscriminatingProperty ≡ ¬ExpectedProperty ¬NotApplicableProperty⊓
elevated blood pressure
clammy skin
palpitations
Hypertension
Hyperthyroidism
Pulmonary Edema
Discriminating Property
Explanatory Feature
Semantics of Focus
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Off-the-shelf OWL-DL reasoners are too resource intensive in terms of both memory and time
• Runs out of resources with background knowledge >> 20 nodes
• Asymptotic complexity: O(n3)
O(n3) < x < O(n4)
Semantic perception on resource-constrained devices
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3 primary issues to be addressed
Annotation of sensor data
SemanticSensor
Web
SemanticPerception
Intelligence
at the Edge
Interpretation of sensor data
Efficient execution onresource-constrained devices1 2 3
Internet of Things
46http://www.idgconnect.com/blog-abstract/900/the-internet-things-breaking-down-barriers-connected-world
47http://share.cisco.com/internet-of-things.html
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Basis Watch• Heart Rate Monitor• Accelerometer• Skin Temperature• Galvanic Skin Response
Homo Digitus
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How do we make sense of this data … and do it efficiently and at scale?
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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
Intelligence at the Edge
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Use bit vector encodings and their operations to encode background knowledge and execute perceptual inference
Efficient execution of semantic perception
0110001110001110110011100101011001110101
OWL-DL
An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices (ISWC, 2012)
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lift
lower
Translate background knowledge, observations, and explanations between Semantic Web and bit vector representation
Lifting and lowering of knowledge
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Efficient execution of semantic perception
Bit vector algorithms are provably equivalent to the OWL inference (i.e., semantics preserving)
Intuition: discover and dismiss those features that cannot explain the set of observed properties.
Intuition: discover and assemble those properties that discriminate between the explanatory features
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bp 1
cs 0
pa 1
HN HM PE
bp 1 1 1
cs 0 1 0
pa 1 1 0
HN HM PE
1 1 1
HN HM PE
1 1 1
1 1 0
AND =>
AND
1 1 1
ObservedProperty Prior Knowledge
PreviousExplanatory Feature
CurrentExplanatory Feature
=>
INTUITION: The strategy employed relies on the use of the bit vector AND operation to discover and dismiss those features that cannot explain the set of observed properties.
Explanation: efficient algorithm
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bp 1
cs 0
pa 1
HN HM PE
bp 0 1 1
cs 0 1 0
pa 1 1 0
HN HM PE
1 1 0
HN HM PE
0 1 0AND => 0 1 0
ObservedProperty Prior Knowledge
PreviousExplanatory Feature
CurrentExplanatory Feature
=
INTUITION: The strategy employed relies on the use of the bit vector AND operation to discover and assemble those properties that discriminate between the explanatory features
bp 0
cs 0
pa 0
Discriminating
Property
1 1 0
… expected?
0 0 0… not-applicable?ZERO Bit Vector
0 1 0
=
=> FALSE
=> FALSE
1
Is the propertydiscriminating?
Focus: efficient algorithm
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O(n3) < x < O(n4) O(n)
Evaluation on a mobile device
Efficiency Improvement
• Problem size increased from 10’s to 1000’s of nodes• Time reduced from minutes to milliseconds• Complexity growth reduced from polynomial to
linear
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Technical contributions in a nutshell
1. Semantic Sensor Web: Developed technologies for the semantic annotation of sensor data on the Web
- Semantic Sensor Web (IEEE Internet Computing, 2008) – 276 citations (as of Aug. 2013)- SemSOS: Semantic Sensor Observation Service (International Symposium on Collaborative Technologies and
Systems, 2009)- Semantic Sensor Network XG Final Report (W3C Incubator Group Report, 2011)
2. Semantic Perception: Designed a declarative specification of perception, capable of utilizing an off-the-shelf OWL-DL reasoner
- An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web (Applied Ontology Journal, 2011)
- Semantic Perception: Converting Sensory Observations to Abstractions (IEEE Internet Computing: Special Issue on Context-Aware Computing, 2012) – most downloaded paid paper in IEEE-IC 2012
3. Intelligence at the Edge: Implemented efficient algorithms for executing perceptual inference on resource-constrained devices
- An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices (International Semantic Web Conference, 2012)
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W3C Reports1. Semantic Sensor Network XG Final Report (W3C Incubator Group Report, 2011)
Journal Publications2. Physical-Cyber-Social Computing: An Early 21st Century Approach (IEEE Intelligent Systems, 2013)3. Semantic Perception: Converting Sensory Observations to Abstractions (IEEE Internet Computing, 2012)4. Semantics for the Internet of Things: Early Progress and Back to the Future (International Journal on Semantic Web and Information Systems, 2012)5. The SSN ontology of the W3C semantic sensor network incubator group (Web Semantics: Science, Services and Agents on the World Wide Web, 2012)6. An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web (Applied Ontology, 2011)7. Semantic Sensor Web (IEEE Internet Computing, 2008)
Conference Publications8. An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices (International Semantic Web Conference,
2012)9. Computing Perception from Sensor Data (IEEE Sensors Conference, 2012)10. SemSOS: Semantic Sensor Observation Service (International Symposium on Collaborative Technologies and Systems, 2009)11. Situation Awareness via Abductive Reasoning for Semantic Sensor Data: A Preliminary Report (International Symposium on Collaborative
Technologies and Systems, 2009).
Workshop Publications12. SECURE: Semantics Empowered Rescue Environment (International Workshop on Semantic Sensor Networks, 2011) 13. Representation of Parsimonious Covering Theory in OWL-DL (International Workshop on OWL: Experiences and Directions, 2011)14. Provenance Aware Linked Sensor Data (Workshop on Trust and Privacy on the Social and Semantic Web, 2010)15. Linked Sensor Data (International Symposium on Collaborative Technologies and Systems, 2010)16. A Survey of the Semantic Specification of Sensors (International Workshop on Semantic Sensor Networks, 2009)17. An Ontological Representation of Time Series Observations on the Semantic Sensor Web (International Workshop on the Semantic Sensor Web)18. Video on the Semantic Sensor Web (W3C Video on the Web Workshop, 2007)
Relevant Publications
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Application
Proactive, preventative healthcare
Heart disease is a critical issue
~815,000 (2011)
http://millionhearts.hhs.gov/abouthds/cost-consequences.html 60
Acute Decompensated Heart Failure (ADHF)
• Affects nearly 6 million people (in
U.S.)
• 555,000 new cases are diagnosed
each year
61U.S. Department of Health & Human Services. (2011). Hospital Compare. http://www.hospitalcompare.hhs.gov (Accessed on February 19, 2012).
• 4.8 million hospitalizations per year
• 50% are readmitted within 6
months
• 25% are readmitted within 30 days
• 70% due to worsening conditions
• Costing $17 billion per year
ADHF hospital readmission rates are too high
62U.S. Department of Health & Human Services. (2011). Hospital Compare. http://www.hospitalcompare.hhs.gov (Accessed on February 19, 2012).
Congress has incentivized hospitals to lower readmission rates
63U.S. Department of Health & Human Services. (2011). Hospital Compare. http://www.hospitalcompare.hhs.gov (Accessed on February 19, 2012).
Current state-of-the-art
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Score (0: Not at all, 1: A little, 2: A great deal, 3: Extremely)
Heart Failure Somatic Awareness Scale (HFSAS)
Current state-of-the-art
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kHealth – knowledge-enabled healthcare
Approach: • Use semantic perception inference• with data from cardio-related sensors• and curated medical background knowledge on
the Web
1. to monitor and abstract health conditions2. to ask the patient contextually relevant
questions
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Cardiology Background Knowledge
• Symptoms: 284
• Disorders: 173
• Causal Relations: 1944
Unified Medical Language System
Causal Network
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kHealth Kit
Weight Scale
Heart Rate Monitor
Blood PressureMonitor
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Sensors
Android Device (w/ kHealth App)
Total cost: < $500
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Explanation in kHealth
• Abnormal heart rate• High blood pressure
• Panic Disorder• Hypoglycemia• Hyperthyroidism• Heart Attack• Septic Shock
Observed Property Explanatory Feature
via Bluetooth
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Focus in kHealth
Are you feeling lightheaded?
Are you have trouble taking deep breaths?
yes
yes
• Abnormal heart rate• High blood pressure• Lightheaded• Trouble breathing
• Panic Disorder• Hypoglycemia• Hyperthyroidism• Heart Attack• Septic Shock
Contextually dependent questioning based on prior observations(from 284 possible questions)
Observed Property Explanatory Feature
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Evaluation of kHealth
Evaluate the ability to discriminate between sets of potential disorders using:
1. HFSAS/WANDA’s restricted set of observable symptoms (12)
2. kHealth’s more comprehensive set of observable symptoms (284)
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Evaluation of kHealth
Evaluation Metrics:1. Efficiency: How many observations (or questions) required to
minimize the set of explanations?2. Specificity: How specific is the resulting minimum set of
explanations (i.e., how many explanatory disorders in the set)?
Explanatory Disorders
(computed by Intellego)
Actual Disorder(extracted from EMR)
Possible Disorders(derived from cardiology
KB)
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Evaluation of kHealth – Early Results
HFSAS/WANDAEfficiency: ~7.45 (# questions asked)Specificity: ~11.95* (# minimum
explanations)
* Converged to 1 explanation 20% of the time
• 496 EMRs• ~3.2 diagnosed
disorders per EMR
• 173 possible disorders
The approach utilized by kHealth is
more efficient and more specificthan HFSAS/WANDA.
kHealthEfficiency: ~7.28 (# questions asked)Specificity: 1 (# minimum explanations)
Explanatory Disorders
(computed by Intellego)
Actual Disorder(extracted from EMR)
Possible Disorders(derived from cardiology
KB)
Pre-clinical usability trial
Dr. William Abraham, M.D.Director of Cardiovascular
Medicine
74
Sensingand
Perception
HealthCare
Academic Standards
Org.
Industry Government
ResearchCollaborators
by Research Topicand Organization Type
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Special Thanks to AFRL and DAGSI
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AFRL/DAGSI Research Topic SN08-8: Architectures for Secure Semantic Sensor Networks for
Multi-Layered Sensing
77
Semantic Sensor Web Team
78
A SEMANTICS-BASED APPROACHTO MACHINE PERCEPTION
Cory Andrew Henson
August 27, 2013
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Thank you.
For additional information visit: http://knoesis.org/researchers/cory