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kHealth: Proactive Personalized Actionable Information for Better Healthcare
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Transcript of kHealth: Proactive Personalized Actionable Information for Better Healthcare
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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)
Wright State, USA
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A Historical Perspective on Collecting Health Observations
Diseases treated onlyby external observations
First peek beyond justexternal observations
Information overload!
Doctors relied only on external observations
Stethoscope was the first instrument to go beyond just external
observations
Though the stethoscope has survived, it is only one among many observations
in modern medicine
http://en.wikipedia.org/wiki/Timeline_of_medicine_and_medical_technology
2600 BC ~1815 Today
Imhotep
Laennec’s stethoscope
Image Credit: British Museum
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“The next wave of dramatic Internet growth will come through the confluence of people, process, data, and things — the Internet of Everything (IoE).”
- CISCO IBSG, 2013
http://www.cisco.com/web/about/ac79/docs/innov/IoE_Economy.pdf
Beyond the IoE based infrastructure, it is the possibility of developing applications that spansPhysical, Cyber and the Social Worlds that is very exciting.
What has changed now?
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4Petabytes of Physical(sensory)-Cyber-Social Data everyday! More on PCS Computing: http://wiki.knoesis.org/index.php/PCS
‘OF human’ : Relevant Real-time Data Streams for Human Experience
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6http://www.technologyreview.com/featuredstory/426968/the-patient-of-the-future/
MIT Technology Review, 2012
The Patient of the Future
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Detection of events, such as wheezing sound, indoor temperature, humidity,
dust, and CO level
Weather Application
Asthma Healthcare Application
Close the window at home during day to avoid CO in
gush, to avoid asthma attacks at night
‘FOR human’ : Improving Human Experience (Smart Health)
Population Level
Personal
Public Health
Action in the Physical World
Luminosity
CO levelCO in gush during day time
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Electricity usage over a day, device at work, power consumption, cost/kWh,
heat index, relative humidity, and public events from social stream
Weather Application
Power Monitoring Application
‘FOR human’ : Improving Human Experience (Smart Energy)
Population Level Observations
Personal Level Observations
Action in the Physical World
Washing and drying has resulted in significant cost
since it was done during peak load period. Consider
changing this time to night.
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kHealthKnowledge-enabled Healthcare
Four current applications: To reduce preventable readmissions of patients with
ADHF and GI; Asthma in children; patients with Dementia
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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
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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
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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
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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
• Syntactic and semantic integration – Qualitative/imprecise citizen observations– Quantitative/precise sensor observations
• Provide complementary and collaborative information• Using Semantic Web technologies, e.g., SemSOS
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How?
• Semantic Perception: Reasoning for decision making and action generation– Perception cycle– Personalized action recommendation using
• Patient health score (linear scale, RYG-abstraction) • Patient vulnerability score (personalization)
– Qualify vs quantify
• Domain (e.g. disease) specific knowledge
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Asthma Domain Knowledge
Domain Knowledge
ICS= inhaled corticosteroid, LABA = inhaled long-acting beta2-agonist, SABA= inhaled short-acting beta2-agonist ; *consider referral to specialist
Asthma Control and Actionable Information
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Patient Health Score (diagnostic)
Risk assessment model
Semantic Perception
Personal level Signals
Public level Signals
Domain Knowledge
Population level Signals
GREEN -- Well Controlled YELLOW – Not well controlledRed -- poor controlled
How controlled is my asthma?
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Patient Vulnerability Score (prognostic)
Risk assessment model
Semantic Perception
Personal level Signals
Public level Signals
Domain Knowledge
Population level Signals
Patient health Score
How vulnerable* is my control level today?
*considering changing environmental conditions and current control level
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Population Level
Personal
Wheeze – YesDo you have tightness of chest? –Yes
Observations Physical-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 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?
… and do it efficiently and at scale
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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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Making sense of sensor data with
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People are good at making sense of sensory input
What can we learn from cognitive models of perception?• The key ingredient is prior knowledge
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Semantic Perception : Perception Cycle
Semantic perception in kHealth involves:• Abductive reasoning to derive candidate
explanations for sensor data, and• Deductive reasoning to disambiguate among
multiple explanations with patient inputs and additional targeted sensor observations.
Intellego
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46* based on Neisser’s cognitive model of perception
ObserveProperty
PerceiveFeature
Explanation
Discrimination
1
2
Perception Cycle*
Translating low-level signals into high-level knowledge
Focusing attention on those aspects of the environment that provide useful information
Prior Knowledge
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To enable machine perception,
Semantic Web technology is used to integrate sensor data with prior knowledge on the Web
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Prior knowledge on the Web
W3C Semantic Sensor Network (SSN) Ontology Bi-partite Graph
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Prior knowledge on the Web
W3C Semantic Sensor Network (SSN) Ontology Bi-partite Graph
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ObserveProperty
PerceiveFeature
Explanation1
Translating low-level signals into high-level knowledge
Explanation
Explanation is the act of choosing the objects or events that best account for a set of observations; often referred to as hypothesis building
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Discrimination is the act of finding those properties that, if observed, would help distinguish between multiple explanatory features
ObserveProperty
PerceiveFeature
Explanation
Discrimination2
Focusing attention on those aspects of the environment that provide useful information
Discrimination
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Discrimination
Discriminating Property: is neither expected nor not-applicable
DiscriminatingProperty ≡ ¬ExpectedProperty ¬NotApplicableProperty⊓
elevated blood pressure
clammy skin
palpitations
Hypertension
Hyperthyroidism
Pulmonary Edema
Discriminating Property Explanatory Feature
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Semantic Perception : Abstraction
• 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.
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Efficient execution of machine perception
Use bit vector encodings and their operations to encode prior knowledge and execute semantic reasoning
0101100011010011110010101100011011011010110001101001111001010110001101011000110100111
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O(n3) < x < O(n4) O(n)
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
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
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thank you, and please visit us at
http://knoesis.org
Smart Data to Big Data; Physical-Cyber-Social Computing