KHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care

Motivation

More than 25 million people in the U.S. are diagnosed with asthma, out of which 7 million are children [1]. Asthma related healthcare costs alone are around $50 billion a year [2]. Current reactive healthcare costs more than 17% of GDP in the US [3, 4]. Specifically, with the current reactive care for asthma, there were 155,000 hospital admissions and 593,000 ER visits in 2006 [16]. It is estimated that, by 2025, over 400 million people will be affected by asthma worldwide. With increasing adoption of mobile devices and low-cost sensors, an unprecedented amount of data is being collected by people [5]. This data collection has exacerbated the problem of understanding the data and making sense of it. In this project, we explore the role of knowledge empowered algorithms in making sense of this data deluge in the context of asthma assessment and management.
Digital health and mobile health applications are benefiting from semantic web research from Director of Artificial Intelligence Institute and Professor of Computer Science and Engineering at the University of South Carolina, Dr. Amit Sheth describes the development of mobile health applications with sensor technology to monitor patient health, mobile computational support, and clear feedback to the patient and physician.


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.

Keynote at WorldComp2014, July 21, 2014. Smart Data for you and me: Personalized and Actionable Physical Cyber Social Big Data.

Asthma: Challenges and Opportunities

Asthma is a great example of a problem that spans Physical-Cyber-Social (PCS) systems. The health signals related to asthma spans Physical (environmental), Cyber (CDC reports), and Social (asthma/symptom reports on social media) modalities. Specifically, for asthma, we group health signals as personal (wheezing level, exhaled Nitric Oxide), population (asthma reports on social media), and public health signals (CDC asthma reports).

kHealth Dashboard image

kHealth for Asthma

We tackle this important problem by a combination of active and passive sensing. Active sensing involves the patient in the loop (obtrusive) while passive sensing does not involve patient involvement (unobtrusive). Using a novel approach of utilizes low-cost sensors for continuous monitoring (active and passive sensing), we propose to develop algorithms that can take this multi-modal data and translate them to practical and actionable information for asthma patients and their healthcare provider. Specifically, provide information on asthma control level based on symptoms and their severity, asthma triggers and early alerts for increasing asthma symptoms. The kHealth-Asthma consist of kHealth kit, kHealth cloud, and kHealth Dashboard. kHealth Dashboard shows the frequency of data collection, the number of parameters collected, and the total number of data points collected per day per patient. (A) Dark blue; the kHealth kit components that are given to the patient. (B) Light blue; the kHealth kit components that collect patient-generated health data. (C) Green; the outdoor environmental parameters and their sources. (D) The kHealth cloud (gray). (E) The kHealth Dashboard. All kHealth data are anonymized and associated with respective randomly assigned patient IDs. FEV1: forced expiratory volume in 1 second; PEF: peak expiratory flow; PM2.5: particulate matter.


kHealth Android Application Introduction


kBot: Chatbot for Asthma Management


Given its chronic nature of the Asthma, the demand for continuous monitoring of patient’s adherence to the medication care plan, assessment of their environment triggers, and management of asthma control level can be challenging in traditional clinical settings and taxing on clinical professionals. A shift from a reactive to a proactive asthma care can improve health outcomes and reduce expenses. On the technology spectrum, smart conversational systems and Internet-of-Things (IoTs) are rapidly gaining popularity in the healthcare industry. By leveraging such technological prevalence, it is feasible to design a system that is capable of monitoring asthmatic patients for a prolonged period and empowering them to manage their health better. In this thesis, we describe kBot, a knowledge-driven personalized chatbot system designed to continuously track medication adherence of pediatric asthmatic patients (age 8 to 15) and monitor relevant health and environmental data. The outcome is to help asthma patients self manage their asthma progression by generating trigger alerts and educate them with various self-management strategies. kBOT takes the form of an Android application with a frontend chat interface capable of conversing both text and voice-based conversations and a backend cloud-based server application that handles data collection, processing, and dialogue management. The domain knowledge component is pieced together from the Asthma and Allergy Foundation of America, Mayoclinic, and Verywell Health as well as our clinical collaborator. Whereas, the personalization aspect is derived from the patient’s history of asthma collected from the questionnaires and day-to-day conversations. The system has been evaluated by eight asthma clinicians and eight computer science researchers for chatbot quality, technology acceptance, and system usability. kBOT achieved an overall technology acceptance score of greater than 8 on an 11-point Likert scale and a mean System Usability Score (SUS) greater than 80 from both evaluation groups.


Internal Revenue Board (IRB)

Dayton Children's Hospital Institutional Review Board (IRB) approved the pilot study in October 2013 which began enrolling pediatric patients and their parents to use the kHealth kit for Asthma. IRB continuation was approved in October 2014. Please contact Prof. Amit Sheth [amit at knoesis.org] or Dr. Shalini Forbis [ForbisS at childrensdayton.org] to obtain the exact copy of IRB.

Acknowledgement

This work is supported by National Institutes of Health under the Grant Number 1 R01 HD087132-01: KHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care. The content of this webpage is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Publications

1. Dipesh Kadariya, Revathy Venkataramanan, Hong Yung Yip, Maninder Kalra, Krishnaprasad Thirunarayan, Amit Sheth. "kBot: Knowledge-enabled Personalized Chatbot for Asthma Self-Management". In Proceedings of the IEEE SMARTSYS Workshop on Smart Service Systems (SMARTCOMP 2019). IEEE, 2019.
2. Amit Sheth, Hong Yung Yip, Arun Iyengar, Paul Tepper. Cognitive Services and Intelligent Chatbots: Current Perspectives and Special Issue Introduction. IEEE Internet Computing, 23 (2), March-April 2019.
3. Revathy Venkataramanan, Krishnaprasad Thirunarayan, Utkarshani Jaimini, Dipesh Kadariya, Hong Yung Yip, Maninder Kalra, Amit Sheth. Determination of Personalized Asthma Triggers from Multimodal Sensing and Mobile Application.,JMIR Pediatr Parent 2019;2(1):e14300 DOI: 10.2196/14300
4. Utkarshani Jaimini, Krishnaprasad Thirunarayan, Maninder Kalra, Revathy Venkataramanan, Dipesh Kadariya, Amit Sheth. “How Is My Child’s Asthma?” Digital Phenotype and Actionable Insights for Pediatric Asthma., JMIR Pediatr Parent 2018;1(2):e11988, DOI: 10.2196/11988. PMCID: PMC6469868 NIHMSID: NIHMS1006255 PMID: 31008446
5. Vaikunth Sridharan, Revathy Venkataramanan, Dipesh Kadariya, Krishnaprasad Thirunarayan, Amit Sheth, Maninder Kalra. “Knowledge-Enabled Personalized Dashboard for Asthma Management in Children”, Annals of Allergy, Asthma & Immunology 121.5 (2018): S42.
6. Amelie Gyrard, Manas Gaur, Krishnaprasad Thirunarayan, Amit Sheth and Saeedeh Shekarpour. Personalized Health Knowledge Graph. 1st Workshop on Contextualized Knowledge Graph (CKG) co-located with International Semantic Web Conference (ISWC), 8-12 October 2018, Monterey, USA.
7. Mahda Noura, Amelie Gyrard, Sebastian Heil, Martin Gaedke. Automatic Knowledge Extraction to build Semantic Web of Things Applications. IEEE Internet of Things (IoT) Journal 2019.
8. Amit Sheth, Hong Yung Yip, Utkarshani Jaimini, Dipesh Kadariya, Vaikunth Sridharan, Revathy Venkataramanan, Tanvi Banerjee, Krishnaprasad Thirunarayam, Maninder Kalra. Feasibility of Recording Sleep Quality And Sleep Duration Using Fitbit in Children with Asthma Abstract in the 32nd Annual Meeting of the Associated Professional Sleep Societies (SLEEP), 2-6 June 2018, Baltimore, MD.
9. Amit Sheth, Hong Yung Yip, Utkarshani Jaimini, Dipesh Kadariya, Vaikunth Sridharan, Revathy Venkataramanan, Tanvi Banerjee, Thirunarayam K, Maninder Kalra. Augmented Personalized Health: Using Semantically Integrated Multimodal Data for Patient Empowered Health Management Strategies. mHealth Technology Showcase, National Institute of Health- June 2018.
10. Utkarshani Jaimini, Hong Yung Yip, Revathy Venkataramanan, Dipesh Kadariya, Vaikunth Sridharan, Tanvi Banerjee, Krishnaprasad Thirunarayan, Maninder Kalra, Amit Sheth. Khealth Digital Personalized Healthcare technology for Pediatric Asthma. mHealth Technology Showcase, National Institute of Health- June 2018.
11. Amit P. Sheth, Tanvi Banerjee, Utkarshani Jaimini, Dipesh Kadariya, Vaikunth Sridharan, Krishnaprasad Thirunarayan, Revathy Venkataramanan, Hong Yung Yip, Maninder Kalra. Correlating Multimodal Signals with Asthma Control in Children Using kHealth Personalized Digital Health System. 2018.
12. Amelie Gyrard, Antoine Zimmermann and Amit Sheth. Building IoT based applications for Smart Cities: How can ontology catalogs help?. IEEE Internet of Things Journal 2018
13. Mahda Noura, Amelie Gyrard, Sebastian Heil and Martin Gaedke. Concept extraction from the web of things knowledge bases. International Conference WWW/Internet. 21-23 October 2018, Budapest, Hungary
14. Amelie Gyrard, Manas Gaur, Swati Padhee, Amit Sheth and Mihaela Juganaru-Mathieu. Knowledge Extraction for the Web of Things (KE4WoT): WWW 2018 Challenge Summary. WWW '18 Companion Proceedings of The Web Conference, 23-27 April 2018, Lyon, France
15. Paul Groth, Amelie Gyrard. Demo Track Chairs' Welcome & Organization. WWW '18 Companion Proceedings of The Web Conference 2018. 23-27 April 2018, Lyon, France
16. Amit Sheth, Utkarshani Jaimini, Hong Yung Yip. How Will the Internet of Things Enable Augmented Personalized Health?. IEEE Intelligent Systems. IEEE; 2018 ;33(1).
17. Amit Sheth, Sujan Perera, Sanjaya Wijeratne, Krishnaprasad Thirunarayan. "Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples". In 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). Leipzig, Germany: ACM; 2017. p. 1-9.
18. Amit Sheth, Utkarshani Jaimini, Krishnaprasad Thirunarayan, Tanvi Banerjee. Augmented Personalized Health: How Smart Data with IoTs and AI is about to Change Healthcare. In 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI 2017). Modena, Italy; 2017.
19. Utkarshani Jaimini, Tanvi Banerjee, William Romine, Krishnaprasad Thirunarayan, Amit Sheth. Investigation of an Indoor Air Quality Sensor for Asthma Management in Children. In IEEE Sensors Letters, Volume 1, Issue 2, April 2017.
20. Amit Sheth. Ontology-enabled Healthcare Applications Exploiting Physical-Cyber-Social Big Data. In Ontology Summit 2016. Virtual; 2016.
21. Pramod Anantharam, Tanvi Banerjee, Amit Sheth, Krishnaprasad Thirunarayan, Surendra Marupudi, Vaikunth Sridharan, Shalini Forbis. Knowledge-driven Personalized Contextual mHealth Service for Asthma Management in Children. 2015 IEEE International Conference on Mobile Services. New York, NY: IEEE; 2015. p. 284 - 291.
22. Amit Sheth. Smart Data: How You and I Will Exploit Big Data for Personalized Digital Health and Many Other Activities. 2015.
23. 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.

References

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