kHealth: Proactive Personalized Actionable Information for Better Healthcare
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The document discusses 'khealth', a knowledge-enabled healthcare application that leverages web tools, mobile technology, and sensors to provide personalized healthcare insights and prevent diseases. It highlights the integration of various data sources, including social media and electronic medical records, to empower patients in managing their health through continuous monitoring and actionable information. The focus is on improving patient outcomes for conditions like asthma and heart failure by using technology for real-time health monitoring and personalized interventions.
![SSN
Ontology
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
Hyperthyroidism
… …
Elevated
Blood
Pressure
Systolic blood pressure of 150 mmHg
“150”
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kHealth: Proactive Personalized Actionable Information for Better Healthcare
- 1. 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
- 2. 2 A Historical Perspective on Collecting Health Observations Imhotep Laennec’s stethoscope Image Credit: British Museum 2600 BC ~1815 Today Diseases treated only by external observations First peek beyond just external 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
- 3. “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 Beyond the IoE based infrastructure, it is the possibility of developing applications that spans Physical, Cyber and the Social Worlds that is very exciting. 3 http://www.cisco.com/web/about/ac79/docs/innov/IoE_Economy.pdf What has changed now?
- 4. ‘OF human’ : Relevant Real-time Data Streams for Human Experience Petabytes of Physical(sensory)-Cyber-Social Data everyday! More on PCS Computing: http://wiki.knoesis.org/index.php/PCS 4
- 5. 6 MIT Technology Review, 2012 The Patient of the Future http://www.technologyreview.com/featuredstory/426968/the-patient-of-the-future/
- 6. ‘FOR human’ : Improving Human Experience (Smart Health) Weather Application Asthma Healthcare Application Personal Public Health Detection of events, such as wheezing sound, indoor temperature, humidity, dust, and CO level Close the window at home during day to avoid CO in gush, to avoid asthma attacks at night 7 Population Level Action in the Physical World Luminosity CO level CO in gush during day time
- 7. ‘FOR human’ : Improving Human Experience (Smart Energy) Weather Application Power Monitoring Application Personal Level Observations Electricity usage over a day, device at work, power consumption, cost/kWh, heat index, relative humidity, and public events from social stream 8 Population 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.
- 8. kHealth Knowledge-enabled Healthcare Four current applications: To reduce preventable readmissions of patients with ADHF and GI; Asthma in children; patients with Dementia 9
- 10. Empowering Individuals (who are not Larry Smarr!) for their own health 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 kHealth: knowledge-enabled healthcare 11
- 11. 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 12
- 12. kHealth Kit for the application for reducing ADHF readmission Weight Scale Heart Rate Monitor Blood Pressure Monitor 13 Sensors Android Device (w/ kHealth App) Readmissions cost $17B/year: $50K/readmission; Total kHealth kit cost: < $500 ADHF – Acute Decompensated Heart Failure
- 13. kHealth Kit for the application for Asthma management Sensordrone (Carbon monoxide, temperature, humidity) Node Sensor (exhaled Nitric Oxide) 15 Sensors Android Device (w/ kHealth App) Total cost: ~ $500 *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
- 14. 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 19
- 15. 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” 20
- 16. 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 Contact doctor Avoid going out in the evening due to high pollen levels Analysis Personalized Actionable Information Data Acquisition & aggregation 21
- 17. 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 22
- 18. 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 23
- 19. 24 Asthma Domain Knowledge Asthma Control and Actionable Information Domain Knowledge Asthma Control à 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
- 20. 25 Patient Health Score (diagnostic) How controlled is my asthma? Risk assessment model Semantic Perception Personal level Signals Public level Signals Domain Knowledge Population level Signals GREEN -- Well Controlled YELLOW – Not well controlled Red -- poor controlled
- 21. 26 Patient Vulnerability Score (prognostic) How vulnerable* is my control level today? Risk assessment model Semantic Perception Personal level Signals Public level Signals Domain Knowledge Population level Signals Patient health Score *considering changing environmental conditions and current control level
- 22. Background Knowledge 31 Health Signal Extraction to Understanding Physical-Cyber-Social System Observations Health Signal Extraction Health Signal Understanding Personal Population Level Acceleration readings from on-phone sensors Wheeze – Yes Do you have tightness of chest? –Yes Risk Category assigned by doctors <Wheezing=Yes, time, location> <ChectTightness=Yes, time, location> <PollenLevel=Medium, time, location> <Pollution=Yes, time, location> <Activity=High, time, location> PollenLevel Wheezing ChectTightness Pollution Activity PollenLevel Wheezing ChectTightness 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 Sensor and personal observations tweet reporting pollution level and asthma attacks Signals from personal, personal spaces, and community spaces Qualify Quantify Enrich Outdoor pollen and pollution Public Health Well Controlled - continue Not Well Controlled – contact nurse Poor Controlled – contact doctor
- 23. 36 How are machines supposed to integrate and interpret sensor data? RDF OWL Semantic Sensor Networks (SSN)
- 24. 39 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).
- 25. 41 What if we could automate this sense making ability? … and do it efficiently and at scale
- 26. SSN Ontology 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 Hyperthyroidism … … Elevated Blood Pressure Systolic blood pressure of 150 mmHg “150” 42
- 27. 43 Making sense of sensor data with
- 28. People are good at making sense of sensory input What can we learn from cognitive models of perception? • The key ingredient is prior knowledge 44
- 29. 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 45
- 30. Observe Property * based on Neisser’s cognitive model of perception Perceive Feature 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 46
- 31. To enable machine perception, Semantic Web technology is used to integrate sensor data with prior knowledge on the Web 47
- 32. Prior knowledge on the Web W3C Semantic Sensor Network (SSN) Ontology Bi-partite Graph 48
- 33. Prior knowledge on the Web W3C Semantic Sensor Network (SSN) Ontology Bi-partite Graph 49
- 34. Explanation is the act of choosing the objects or events that best account for a set of observations; often referred to as hypothesis building Observe Property Perceive Feature Explanation 1 Explanation Translating low-level signals into high-level knowledge 50
- 35. Discrimination is the act of finding those properties that, if observed, would help distinguish between multiple explanatory features Observe Property Perceive Feature Explanation Discrimination 2 Focusing attention on those aspects of the environment that provide useful information Discrimination 51
- 36. Discrimination Discriminating Property: is neither expected nor not-applicable DiscriminatingProperty ≡ ¬ExpectedProperty ⊓ ¬NotApplicableProperty Discriminating Property Explanatory Feature elevated blood pressure clammy skin palpitations Hypertension Hyperthyroidism Pulmonary Edema 52
- 37. 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. 53
- 38. How do we implement machine perception efficiently on a resource-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) 57
- 39. 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 Henson et al. 'An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices, ISWC 2012. 58
- 40. Efficient execution of machine perception Use bit vector encodings and their operations to encode prior knowledge and execute semantic reasoning 010110001101 0011110010101 1000110110110 101100011010 0111100101011 000110101100 0110100111 59
- 41. 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 O(n3) < x < O(n4) O(n) 60
- 42. Semantic Perception for smarter analytics: 3 ideas to takeaway 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 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 61
- 43. 62 thank you, and please visit us at http://knoesis.org
Editor's Notes
- #2: Starting slide Various Big data problems – Traditional examples vs what we are doing examples. Variety and Velocity than Volume. kHealth problem. People will be interested in Smart Data. Traditional ML techniques, High Performance Computing, Statistics. Human level of Abstraction is Smart data.
- #3: "2600 BC – Imhotep wrote texts on ancient Egyptian medicine describing diagnosis and treatment of 200 diseases in 3rd dynasty Egypt.” Sir William Osler, 1st Baronet, was a Canadian physician and one of the four founding professors of Johns Hopkins Hospital. He was called the father of modern medicine. Sir William Osler called Imhotep as the true father of medicine.
- #4: There are over 99.4% of physical devices that may one day be connected to The Internet still unconnected. - CISCO IBSG, 2013
- #5: All the data related to human activity, existence and experiences More on PCS Computing: http://wiki.knoesis.org/index.php/PCS
- #6: TKP: Should not knowledge be used to bridge the gap between data and decision and action? Or are we saying we need to glean knowledge?
- #7: - Larry Smarr is a professor at the University of California, San Diego And he was diagnosed with Chrones Disease What’s interesting about this case is that Larry diagnosed himself He is a pioneer in the area of Quantified-Self, which uses sensors to monitor physiological symptoms Through this process he discovered inflammation, which led him to discovery of Chrones Disease This type of self-tracking is becoming more and more common
- #8: Actionable information example: In Asthma use case we have a sensor – sensordrone which records luminosity and CO levels A high correlation between CO level and luminosity is found This is an actionable information to the user interpreting it as CO in gush during day time => Mitigating action can be “closing the window” during day
- #9: Also, we have weather application which performs abstraction on weather sensory observations to identify blizzard conditions (food for actions!!) : -- 20,000 weather stations (with ~5 sensors per station) -- Real-Time Feature Streams - live demo: http://knoesis1.wright.edu/EventStreams/ - video demo: https://skydrive.live.com/?cid=77950e284187e848&sc=photos&id=77950E284187E848%21276
- #12: - With this ability, many problems could be solved - For example: we could help solve health problems (before they become serious health problems) through monitoring symptoms and real-time sense making, acting as an early warning system to detect problematic health conditions
- #14: ADHF – Acute Decompensated Heart Failure
- #16: 1)www.pollen.com(For pollen levels) 2)http://www.airnow.gov/(For air quality levels) 3)http://www.weatherforyou.com/(For temperature and humidity)
- #17: Data overload in the context of asthma
- #18: “
- #22: Research on Asthma has three phases Data collection: what signals to collect? Analysis: what analysis to be done? Actionable information: what action to recommend? In the next slide, we take a peek into the analysis that we do for Asthma
- #26: What is the current state of a person/patient? => Summarizing all the observations (sensor and personal) into a single score indicating health of a person Instead of presenting all the raw data (often to much e.g., Asthma application we have developed collects CO, temperature, and humidity every 10 seconds resulting in 8,640 observations/day) which may not be comprehensible to the patient, we empower them by providing actionable summaries.
- #27: What is the likely state of the person in future? => Given the current state and the changing environmental conditions, estimate the state of the person by summarizing it into a number which is actionable. For example, vulnerability score for a person with Asthma is computed with environmental factors (pollen, air quality, external temperature and humidity) and current state of the patient. Intuitively, a person with well controlled asthma should have a lower vulnerability score than a person with poorly controlled asthma both being in a poor environmental state.
- #31: In the absence of declarative knowledge in a domain, we resort to statistical approaches to glean insights from data Even if there is declarative knowledge of a domain, it may have to be personalized The CO level may be related to the luminosity as observed by the sensordrone – as it gets brighter the CO level also increases => high CO level in daytime If such an insight is provided to a person, the interpretation can be: Some activity inside the house leads to high CO levels Outside activity leads to high CO levels inside the house Since the person knows that he/she is absent in the house during mornings, it has to be something from outside. - Person narrows down to a possible opened window at home (forgot to close more often)
- #32: There are two components in making sense of Health Signals: Health signal extraction – processing, aggregating, and abstracting from raw sensor/textual data to create human intelligible abstractions Health signal understanding – derive (1) connections between abstractions and (2) Action recommendation: Continue Contact nurse Contact doctor
- #34: Only score based structure extraction is presented here. Other popular structure extraction techniques include constraint based approaches which finds independences between random variables X1, …, Xn I-Map => different structures result in the same loglikelihood score. Thus recovering the original structure of the graph generating data using data alone is considered impossible! We go the the rescue of declarative knowledge to: (1) choose promising structures and (2) to break ties when two structure results in the same score
- #36: Massive amount of data is collected by sensors and mobile devices yet patients and doctors care about “actionable” information. This data has all the four Vs of big data and we used knowledge enabled techniques to transform it into value In the context of PD, we analyzed massive amount of sensor data collected by sensors on a smartphones to understand detection and characterization of PD severity.
- #42: - what if we could automate this sense making ability? - and what if we could do this at scale?
- #45: sense making based on human cognitive models
- #47: perception cycle contains two primary phases explanation translating low-level signals into high-level abstractions inference to the best explanation discrimination focusing attention on those properties that will help distinguish between multiple possible explanations used to intelligently task sensors and collect additional observations (rather than brute force approach of blindly collecting all observations)
- #60: compute machine perception inferences -- i.e., explanation and discrimination -- of high-complexity on a resource-constrained devices in miliseconds Difference between the other systems and what this system provides
- #61: Intelligence at the age. Shipping computation and domain models to the edge (Distributed)
- #63: More at: http://wiki.knoesis.org/index.php/PCS And http://knoesis.org/projects/ssw/