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K health ieeems2015
- 1. Knowledge-driven Personalized Contextual mHealth Service for Asthma Management in Children Pramod Anantharam, Tanvi Banerjee, Amit Sheth, Krishnaprasad Thirunarayan, Surendra Marupudi, Vaikunth Sridharan Ohio Center of Excellence in Knowledge-enabled Computing(Kno.e.sis), Wright State University, USA Presentation for: IEEE 4th International Conference on Mobile Services, June 27 – July 2, 2015, NY, USA
- 2. 2 http://www.technologyreview.com/featuredstory/426968/the-patient-of-the-future/ MIT Technology Review, 2012 The Patient of the Future
- 3. Through analysis of physical, physiological, and environmental observations, 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 3
- 4. Asthma Dementia Heart Failure Liver Cirrhosis kHealth Application Areas 4
- 5. 5 1http://www.nhlbi.nih.gov/health/health-topics/topics/asthma/ 2http://www.lung.org/lung-disease/asthma/resources/facts-and-figures/asthma-in-adults.html 3Akinbami et al. (2009). Status of childhood asthma in the United States, 1980–2007. Pediatrics,123(Supplement 3), S131-S145. 25 million 300 million $50 billion 155,000 593,000 People in the U.S. are diagnosed with asthma (7 million are children)1. People suffering from asthma worldwide2. Spent on asthma alone in a year2 Hospital admissions in 20063 Emergency department visits in 20063 Asthma: Severity of the problem
- 6. 6 Asthma: A Multi-faceted and Symptomatically Variable Health Issue Personal level Signals Public level Signals Population level Signals 1Marcus, Philip, Kevin R. Murphy, Abid Rahman, and Christopher D. O’Brien. "Intrapatient symptom variability in adults and children with asthma: Results of a survey." Advances in therapy 22, no. 5 (2005): 488-497. “ … survey indicates that adult patients and caregivers of pediatric patients report variability in asthma symptoms over time, even when asthma medications are taken.”1
- 7. 7 Asthma: Actionable Information How is my Asthma control? Should I take additional medication today? How can I reduce my asthma attacks at home? “… Far better an approximate answer to the right question, which is often vague, than the exact answer to the wrong question, which can always be made precise.” -- John Tukey, Ann. Math. Stat. 33 (1962
- 8. 8 Personal level Signals Public level Signals Population level Signals Domain Knowledge Asthma: Challenges in Heterogeneity, Variability, and Personalization http://www.tuberktoraks.org/managete/fu_folder/2011-03/html/2011-3-291-311.html Contextual Personalized Actionable OR
- 9. Sensordrone (Carbon monoxide, temperature, humidity) 9 Sensor Platforms Android Device (w/ kHealth App) Total cost: ~ $550 kHealth Kit for the application for Asthma management Along with sensor platforms in the kit, the application uses a variety of population level signals from the web: Pollen level Air Quality Temperature & Humidity Node Sensor (exhaled Nitric Oxide) Fitbit ChargeHR (Activity, sleep quality) 9
- 10. kHealth Kit: Sensor Platforms Sensordrone • Precision gas sensor • Reducing gas sensor • Oxidizing gas sensor • Non-contact thermometer • Humidity sensor • Temperature sensor • Light sensor • Color sensors • Pressure • Proximity • Expansion connector Node Sensor • Exhaled Nitric Oxide Fitbit ChargeHR • Heart Rate • All-Day Activity • Sleep Monitoring 10
- 11. kHealth Kit: Android Application 11 For collecting observations from both machine sensors and from patients in the form of a questionnaire
- 12. kHealth: Health Signal Processing Architecture Personal level Signals Public level Signals Population level Signals Domain Knowledge Risk Model Events from Textual Streams Take Medication before going to work Avoid going out in the evening due to high pollen levels Contact doctor Analysis Personalized Actionable Information Data Acquisition & Aggregation 12 12
- 13. 13 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 controlled Red – Poor control How controlled is my asthma?
- 14. 14 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 GREEN – Low YELLOW – Moderate Red – High
- 15. 15 Sensordrone – for monitoring environmental air quality Wheezometer – for monitoring wheezing sounds Can I reduce my asthma attacks at night? What are the triggers? What is the wheezing level? What is the propensity toward asthma? What is the exposure level over a day? Commute to Work Decision Support for Doctors and Patients: A Scenario Luminosity CO level CO in gush during day time Actionable Information Personal level Signals Public level Signals Population level Signals What is the air quality indoors?
- 16. 16 Usability and decision support trial Dr. Shalini G. Forbis, MD, MPH
- 17. Deployment Details and Data Collection kHealth kit was deployed with four patients for feasibility study of data collection and preliminary analysis of data to derive value 17
- 18. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Did patient take albuterol last night due to cough or wheeze? 0 0.05 0.1 0.15 0.2 0.25 Exhaled Nitric Oxide Medication (Albuterol) related to decreasing Exhaled Nitric Oxide 18
- 19. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 How much did asthma or asthma symptoms limit patient's activity today? 0 0.05 0.1 0.15 0.2 0.25 Exhaled Nitric Oxide Activity limitation related to high exhaled Nitric Oxide 19
- 20. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Has patient had wheeze, chest tightness, or asthma related cough today? 0 0.05 0.1 0.15 0.2 0.25 Exhaled Nitric Oxide Low exhaled Nitric Oxide observed with absence of coughing 20
- 21. 0 0.5 1 1.5 2 2.5 Pollen 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 How much did asthma or asthma symptoms limit patient's activity today? Activity limitation observed with high pollen activity 21
- 22. Conclusion Unprecedented and Continuous Access to Intimate Patient Data is Possible • Doctors can utilize this information for informed decision making • Require techniques to find promising hypothesis to be vetted by doctors Personalized Treatment of Asthma is Challenging • Asthma is a multifaceted and symptomatically variable ailment • Patient specific understanding of response to triggers would help in personalization Contextual Application for Actionable Information is Challenging • Detecting recurring conditions of response to triggers is required for context awareness • Background knowledge from doctors would be crucial for recommending actions
- 23. Future Work Carry out a Large Scale Pilot & Clinical Trial • kHealth kit is prepared to be deployed with over 200 asthma patients Formulate Patient Vulnerability Score • This score is not formally specified in the asthma diagnosis guidelines from NIH1 • The complexity of asthma makes it hard to define vulnerability score (prognostic) • Personalization is crucial even if such a score can be defined Add New Sensors for Monitoring Triggers such as Smoke and Indoor Air Quality • We need these sensors for correlating it with the report of asthma attacks • Remedial measures can be suggested by experts based on indoor conditions 1 http://www.nhlbi.nih.gov/files/docs/guidelines/asthgdln.pdf
- 24. Acknowledgements Partial support for this research was provided by Wright State University’s VP of Research under a challenge grant.
- 25. Thank you Thank you, and please visit us at http://knoesis.org For more information on kHealth, please visit us at http://knoesis.org/projects/khealth Link to the paper: http://www.knoesis.org/library/resource.php?id=2153
Hinweis der Redaktion
- Time series observations are readily and naturally available in domains such as finance, health care, smart cities, and system health monitoring. Increasingly, time series observations include both sensor and textual data generated in the same spatio-temporal context creating both challenges for dealing with heterogeneous data and opportunities for obtaining comprehensive situational awareness. For example, in a city, there are machine sensors and citizen sensors observing the city infrastructure (e.g., bridges, power grids) and city dynamics (e.g., traffic flow, power consumption). In this research, we investigate extraction of city events from textual observations and utilize them explain variations in the sensor observations. This will improve our understanding of city events and their manifestations due to the complementary nature of observations provided by the machine sensors and citizen sensors.
- - 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 sdd link to video
- - 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
- Characteristics of asthma – why is it a complex condition?
- Asthma requires that we provide contextual, personalized, and actionable information to the patient by analyzing observations from Personal, Public, and Population level modalities
- 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)
- 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
- 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.
- 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.
- 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)