kHealth: Proactive Personalized Actionable Information for Better Healthcare Put Knoesis Banner PDA@IoT, in conjunction with VLDB, September, 2014 Amit Sheth , Pramod Ananthram, T.K. Prasad The Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis ) Wright State, USA
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kHealth: Proactive Personalized Actionable Information for Better Healthcare
Amit Sheth, Pramod Anantharam, Krishnaprasad Thirunarayan, "kHealth: Proactive Personalized Actionable Information for Better Healthcare", Workshop on Personal Data Analytics in the Internet of Things at VLDB2014, Hangzhou, China, September 5, 2014.
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kHealth: Proactive Personalized Actionable Information forBetter Healthcare
Put Knoesis Banner
PDA@IoT, in conjunction with VLDB, September, 2014
Amit Sheth, Pramod Ananthram, T.K. PrasadThe Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)
Beyond the IoE based infrastructure, it is the possibility of developing applications that spansPhysical, Cyber and the Social Worlds that is very exciting.
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
Empowering Individuals (who are not Larry Smarr!) for their own health
kHealth: knowledge-enabled healthcare
What?
• kHealth is a knowledge-based approach/application for patient-centric health-care that exploits:(a) Web based tools and social media, (b) Mobile phone technology and wireless sensors, (c) For synthesizing personalized actions from heterogeneous health data
(i) For disease prevention and treatment(ii) For health, fitness and well-being
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Weight Scale
Heart Rate Monitor
Blood PressureMonitor
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Sensors
Android Device (w/ kHealth App)
Readmissions cost $17B/year: $50K/readmission; Total kHealth kit cost: <
$500
kHealth Kit for the application for reducing ADHF readmission
ADHF – Acute Decompensated Heart Failure
Sensordrone (Carbon monoxide,
temperature, humidity) Node Sensor
(exhaled Nitric Oxide)
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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
Why?
• “Unintelligible” health data deluge due to – Continuous monitoring of patients using passive and
active sensors– Continuous monitoring of environment using
sensors– Public health reports– Population level information– Social media conversations– Personal Electronic Medical Records (EMRs)– Wide use of affordable mobile/wireless technologies
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Why?
• Empowering patients to improve health by– Abstracting and integrating low-level sensor data
to more meaningful health signals – Recommending personalized actions
• Ubiquitous, timely and effective health management and telemedicine– Involve patient and health-care team without
causing “interaction fatigue”
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kHealth: Health Signal Processing Architecture
Personal level Signals
Public level Signals
Population level Signals
Domain Knowledge
Risk Model
Events from Social Streams
Take Medication before going to work
Avoid going out in the evening due to high pollen levels
Contact doctor
AnalysisPersonalized Actionable
Information
Data Acquisition & aggregation
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How?
• Data collection from various sources– Active and passive sensing devices– Social media crawling– EMR
tweet reporting pollution level and asthma attacks
Acceleration readings fromon-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
Health Signal Extraction to Understanding
Well Controlled - continueNot Well Controlled – contact nursePoor Controlled – contact doctor
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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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What if we could automate this sense making ability?
• Mapping low-level sensor values to coarse-grain abstract values– E.g., Blood pressure: 150/100 => High bp
• Extracting signatures for high-level human comprehensible features from low-level sensor data stream.– E.g., Parkinson disease : unsteady walk, fall,
slurred speech, etc.
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How do we implement machine perception efficiently on aresource-constrained device?
Use of OWL reasoner is resource intensive (especially on resource-constrained devices), in terms of both memory and time
• Runs out of resources with prior knowledge >> 15 nodes• Asymptotic complexity: O(n3)
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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
Henson et al. 'An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices, ISWC 2012.
• Problem size increased from 10’s to 1000’s of nodes• Time reduced from minutes to milliseconds• Complexity growth reduced from polynomial to
linear
Evaluation on a mobile device
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2 Prior knowledge is the key to perceptionUsing SW technologies, machine perception can be formalized and integrated with prior knowledge on the Web
3 Intelligence at the edgeBy downscaling semantic inference, machine perception can
execute efficiently on resource-constrained devices
Semantic Perception for smarter analytics: 3 ideas to takeaway
1 Translate low-level data to high-level knowledgeMachine perception can be used to convert low-level sensory signals into high-level knowledge useful for decision making