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1
kBot - Knowledge Enabled Personalized
Chatbot for Self-Management of Asthma
in Pediatric Population
Committee Members:
D...
2
Outline
● Introduction
○ Background
○ Challenges
○ Related work
● Methodology
○ Approach
○ Implementation
● Evaluation a...
Asthma
● Chronic pulmonary disease
● Multifactorial
● Can be serious (life threatening)
There is no cure for asthma, but i...
Asthma self-management
4
Strategies to help people better manage their asthma, keep it well controlled and live life to
th...
Tracking Asthma
Tracking signs and symptoms of asthma so as to
understand and manage patient asthmatic condition.
Know
con...
Medical Adherence
If patients know that their medication use is being monitored,
they are more likely to adhere.[1]
The im...
Pollen from
trees and
grass
Cold
air
Air Pollution
(PM2.5)
Ozone O3
Mold
spores
Smokes
tobacco, fire
place
Animal danders
f...
Avoiding Triggers
Asthma -multifactorial disease
Two different asthma patients might react differently
to same asthma trigge...
Self-management education
Self-management education for children to improve coping with asthma in daily life.
Knowledge:
●...
kHealth for Asthma
10
kHealth: A knowledge-enabled semantic platform that captures
patient generated multimodal data and a...
11
Challenges
★ Poor patient compliance badly affects the quality and quantity of data
collection.
★ Poor medical adherence...
Need...
A system to:
- Closely interact with patients
- Monitor patients’ wellbeing
- Encourage medical adherence
- Monito...
How chatbot can help?
Creates a conversational environment:
- Available 24/7
- Natural
- Engaging
- Prompt response
Releva...
Study Main objective Platform
Fadhil et al. (2017)
● Encourage adult population towards healthy eating habits to
prevent w...
15
Technology/Application for Asthma
Study Main objectives Limitations Platform
Wella Pets
● Teach proper inhaler techniqu...
Thesis Statement
16
Smart conversational systems can continuously monitor patient’s relevant
health signals, including the...
Methodology
17
18
kBot Features
Monitor patients’ environmental factors and alert potential asthma triggers.
- Data watcher - periodicall...
Data collection
Asthma related data
Captured from user conversation
● Day/night symptom
● Controller medicine use
● Peak fl...
Dialogue processing
20
I am experiencing cough
and wheeze
Intent: collect symptom
Entities: [Cough, Wheeze]
Context: curre...
Outdoor Data Collection
21
Parameter Frequency
PM2.5 Hourly (24/day)
Ozone Hourly (24/day)
Temperature Every 2 hour (12/da...
Contextualization and
Personalization
22
How kBot uses asthma domain knowledge to provide
contextually relevant information
Contextualization
23
Mayo clinic
https:...
24
Medication and inhaler use
★ Medicine images
★ Inhaler use video
Use-inhalers.com
Video Content
Educate patient
for pro...
Personalization
25
Example
A snapshot of kBot Conversation
where kBot uses patient’s past data
and environmental data to p...
26
Initial user profile setup
➔ Patient ID (Anonymized)
➔ Age
➔ Deployment period (date range)
➔ Gender
➔ Zip Code
➔ Clinic...
Environmental Alert
27
Data watcher
Notification
Service
Trigger_list:
{
R1: pollen,
R2: Ozone,
...}
Patient specific
Ranked...
28
PEF and Asthma Zone
Personal best PEF: Best PEF value out of 3 initial consecutive readings
Digital
Peak Flow Meter
PEF...
29
Technical Requirements
★ Continuously monitor patient’s health and environment data.
★ Process these data in near real-...
Architecture
30
Services
● Push Notification
-generate notification and alert
● Weather
-weather report and environmental data
● Data colle...
Data Exchange Security
32
1010...1...111..011.....1110.....0
Client Server
HTTP/1.1
Kno.e.sis private cloud
database
Socke...
Cloud Database
- Database + full-text search engine
- Apache Lucene library
- HTTP web interface
- Schema-free JSON docume...
Data Snapshots
{
Severity : "Severe",
zip : 45458,
gender : "Male",
best_peak : 306,
chat_id : "f6de6a3601519df63feedd6f1e...
35
kBot Client
★ Interface: chat room
★ Modes of input:Text and voice
★ Communication protocol: web sockets
★ Voice recogn...
Evaluation
36
Preliminary evaluation for acceptance of the technology as a tool to self-manage
asthma.
Population: 8 asthma clinicians (...
38
Metrics Questions
Quality of
chatbot
Naturalness
● kBot uses simple and understandable vocabulary.
● kBot dialogues wer...
Evaluation result
39
11 point Likert Scale
Range: 0-10
0 - Strongly Disagreed
10 - Strongly Agreed
Total 16 responses
8 - ...
System Usability Scale (SUS)
40
➔ Standard 10 item questionnaire
➔ 5 point likert-scale (ranging 0-4)
➔ Evaluates usabilit...
Conclusion
41
- As a chronic multifactorial disease, assessing and managing
asthma within the traditional clinical setup h...
42
Future work
★ Incorporate IoT sensors to collect additional
patient data such as indoor air quality, sleep and
activity...
43
Resources
Paper: http://knoesis.org/node/2937
Video: https://youtu.be/e2Vq_3oh8wU
Github Repo (private)
Server: https://gi...
1. Dipesh Kadariya, Revathy Venkataramanan, Hong Yung Yip, Maninder Kalra, Krishnaprasad Thirunarayan, Amit Sheth. "kBot:
...
This research is partially supported by National Institute of Health(NICHD/NIH) under
the Grant Number: 1R01HD087132. Any ...
References
47
1. CDC, “Center for Disease Control and Prevention-Most recent asthma data,”
https://www.cdc.gov/asthma/most...
Resources used
48
1. Asthma background:
https://www.dreamstime.com/asthma-patient-girl-inhaling-medication-treating-shortn...
Acknowledgement
49
Dr. Amit Sheth
(Advisor)
Dr. Krishnaprasad
Thirunarayan
Dr. Valerie Shalin Dr. Maninder
Kalra
Committee...
kHealth Team
50
Dr. Amit Sheth
(Principal investigator)
Dr. Krishnaprasad
Thirunarayanan
(Co-investigator)
Dr. Maninder Ka...
Thank You!
52
Questions?
kBot: Knowledge-Enabled Personalized Chatbot for Self-Management of Asthma in Pediatric Population
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kBot: Knowledge-Enabled Personalized Chatbot for Self-Management of Asthma in Pediatric Population

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Video: https://youtu.be/Yv6L_b8ZrtU
Abstract:
Asthma, chronic pulmonary disease, is one of the major health issues in the United States. Given its chronic nature, 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.

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kBot: Knowledge-Enabled Personalized Chatbot for Self-Management of Asthma in Pediatric Population

  1. 1. 1 kBot - Knowledge Enabled Personalized Chatbot for Self-Management of Asthma in Pediatric Population Committee Members: Dr. Krishnaprasad Thirunarayan, Dr. Valerie Shalin, Dr. Maninder Kalra Master’s Thesis Defense Dipesh Kadariya Kno.e.sis Center Department of Computer Science and Engineering Wright State University, Dayton, Ohio June 24th , 2019 Advisor: Dr. Amit P. Sheth kHealth Ohio Center of Excellence in Knowledge-Enabled Computing
  2. 2. 2 Outline ● Introduction ○ Background ○ Challenges ○ Related work ● Methodology ○ Approach ○ Implementation ● Evaluation and results ● Conclusion and future work
  3. 3. Asthma ● Chronic pulmonary disease ● Multifactorial ● Can be serious (life threatening) There is no cure for asthma, but it can be managed so you live a normal, healthy life. Demographics: ● 26.5 million out of which 6.1 million are children ● Most chronic pediatric condition accounting for 13.8 million missed school days each year [Source : CDC] 3
  4. 4. Asthma self-management 4 Strategies to help people better manage their asthma, keep it well controlled and live life to the fullest. Key Challenges: ● Tracking asthma ○ Monitor patient’s day to day asthma condition ● Ensuring Medical adherence ○ Track and encourage patients towards medical adherence ● Avoiding environmental trigger ○ Continuously monitor and alert environmental triggers ● Self-management education ○ Educate patients with self-management strategies
  5. 5. Tracking Asthma Tracking signs and symptoms of asthma so as to understand and manage patient asthmatic condition. Know control zone track Medication PEF log Environmental triggers Asthma journal Tracking Asthma Tracking: ● Keep a journal (track symptoms) ● Tracking medication intake and inhaler use ● A daily peak flow log (pulmonary function) ● Know your zone ● Know your triggers 5
  6. 6. Medical Adherence If patients know that their medication use is being monitored, they are more likely to adhere.[1] The implication of innovative techniques, such as telemonitoring of patients to: ● Educate patients better ● Interventions such as: ○ Informational interventions: audio visual education ○ Behavioural interventions: medication reminders Main causes: ● Forgetfulness and/or carelessness. ● Inadequate training in the inhalation techniques. ● Lack of understanding Adherence to medication in asthma is a well recognized challenge 6
  7. 7. Pollen from trees and grass Cold air Air Pollution (PM2.5) Ozone O3 Mold spores Smokes tobacco, fire place Animal danders from hair, skin, or feathers Dust mites feces Cockroaches feces Asthma Triggers Allergic Asthma - most common form of asthma Variations of trigger:[1] About 90% of pediatric asthma is due to allergies 7
  8. 8. Avoiding Triggers Asthma -multifactorial disease Two different asthma patients might react differently to same asthma trigger Personalized approach: - Monitor day-to-day asthma of each patient - Continuously monitor their environment - Systematically rank the trigger based on patients symptoms frequency - Alert patient when triggers are present Monitor Patients’ Asthma Monitor Patients’ Environment Potential Triggers Alert when present Personalized approach to avoid asthma triggers 8
  9. 9. Self-management education Self-management education for children to improve coping with asthma in daily life. Knowledge: ● Increase child’s knowledge and understanding of asthma and the medicine ● Bring positive attitude towards need for medication ● Healthy behaviour, healthy diet, healthy environment Self-management skills: ● Identify early signals and prevent asthma symptoms ● Act at an early stage of asthma symptom ● Relaxation techniques 9
  10. 10. kHealth for Asthma 10 kHealth: A knowledge-enabled semantic platform that captures patient generated multimodal data and analyze it for actionable insights. Completed: 83 Consented: 107 - Continuous monitoring -> useful insights of patients health - Poor medication adherence leads to poor asthma control - different patients reacted to different environmental triggers with varying intensity Findings:
  11. 11. 11 Challenges ★ Poor patient compliance badly affects the quality and quantity of data collection. ★ Poor medical adherence and improper inhaler use technique minimizes the medical effectiveness. ★ Due to multifactorial nature of asthma, general treatment approach such as a generic asthma care plan proves to be ineffective in most of the cases demanding a more personalized approach.
  12. 12. Need... A system to: - Closely interact with patients - Monitor patients’ wellbeing - Encourage medical adherence - Monitor patients environmental factors and alert triggers - Educate patients with self-management skills Overall, a system that enables patients to self-manage asthma and live their life to fullest. 12
  13. 13. How chatbot can help? Creates a conversational environment: - Available 24/7 - Natural - Engaging - Prompt response Relevant content based on responses Personalization Easier to gain trust Scalable 13 An asthma health companion
  14. 14. Study Main objective Platform Fadhil et al. (2017) ● Encourage adult population towards healthy eating habits to prevent weight gain Mobile- app Your.MD ● Provide online health counseling to patients ● Help find online doctors Web MANDY (Ni et al. 2017) ● A primary care chatbot to assist healthcare staffs by automating the patient intake process Mobile-app FLORENCE ● Reminds patients to regularly take their medication ● Provides information related to their medicine Facebook Messenger SMOKEY ● Alerts patients about air pollution in the city ● Regular air quality update Facebook Messenger 14 Technology in healthcare
  15. 15. 15 Technology/Application for Asthma Study Main objectives Limitations Platform Wella Pets ● Teach proper inhaler technique to asthmatic kids ● Help Identify trigger within house ● How to act during symptom ● No tracking of patients asthma ● Does not provide feedback to user inputs Mobile game (Android/iOS) Asthma Buddy National Asthma Council Australia ● Remind patients to take medication ● Track changes in symptoms ● Lack of Self-management education ● No environmental alert ● No PEF tracking Mobile App (Android/iOS)Propeller app Propeller health ● Automatically track medication use (where and when) ● Alert doctor and family members on worsening of asthma. ● Alert air quality issues ● Lack of Self-management education ● No PEF tracking AsthmaMD ● Log asthma symptoms in digital diary ● Asthma journal: Quick view of past asthma data ● Securely Share asthma record to physician ● Physician centric (directed to the physician not to the clinician)
  16. 16. Thesis Statement 16 Smart conversational systems can continuously monitor patient’s relevant health signals, including their medication adherence, and environmental data. Through contextual and personalized processing of these data, it can enable self-management of patient’s chronic disease. This work is presented in the context of asthma. Part of this work was published in SMARTCOMP 2019 workshop. 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.
  17. 17. Methodology 17
  18. 18. 18 kBot Features Monitor patients’ environmental factors and alert potential asthma triggers. - Data watcher - periodically monitor weather data - Mechanism to rank triggers based on symptom frequency - Weather alert system Monitor and encourage patients to improve medical adherence. - Customizable Medication reminders - Educate patients on importance of medicine to keep their asthma well controlled Collect data on patient’s asthma for a given period of time in order to assess the control level. - Refined and modified kHealth asthma questionnaire for simplicity and naturalness - Ask questions in the form of conversation Educate patients on asthma self-management skills. - Skills on how to deal with common symptoms - Relaxation techniques - How to avoid common asthma triggers
  19. 19. Data collection Asthma related data Captured from user conversation ● Day/night symptom ● Controller medicine use ● Peak flow meter reading ● Current symptom ● Worsening symptom ● Rescue medicine use 19 }Daily Asthma Questionnaire }Report Symptom Environmental parameters Collected from 3rd party weather APIs ● Ozone ● Particulate matter 2.5 ● Pollen index ● Temperature ● Humidity Twice a day: morning and evening Patient initiated reporting
  20. 20. Dialogue processing 20 I am experiencing cough and wheeze Intent: collect symptom Entities: [Cough, Wheeze] Context: current_symptom User: KHB111 timeStamp: 01-02-2018 19:00:00 kBot That’s not good… Did you take your Albuterol for the relief? parse and process the user dialog. ★ Intent: Group of typical examples and logic that relates to a common goal of the user. ★ Context: Set of conversation or fact around which conversation is going on.
  21. 21. Outdoor Data Collection 21 Parameter Frequency PM2.5 Hourly (24/day) Ozone Hourly (24/day) Temperature Every 2 hour (12/day) Humidity Every 2 hour (12/day) Pollen count Twice a day Data Collection Data watch Collects outdoor parameters per zip code from 3rd party weather APIs at different frequencies and commits to the cloud storage Constantly checks outdoor parameter values collected by Data Collector and calls kBot environmental alert service if encountered a parameter in unhealthy range.
  22. 22. Contextualization and Personalization 22
  23. 23. How kBot uses asthma domain knowledge to provide contextually relevant information Contextualization 23 Mayo clinic https://www.mayoclinic.org/diseases-conditions/asthma/sympto ms-causes/syc-20369653 Asthma and allergy foundation of America https://www.aafa.org/ Verywellhealth https://www.verywellhealth.com/asthma-overview-4582010 Asthma domain knowledge
  24. 24. 24 Medication and inhaler use ★ Medicine images ★ Inhaler use video Use-inhalers.com Video Content Educate patient for proper use of inhaler Medication images Help patient identify the correct medicine Domain Knowledge Medication Image American Association for Respiratory Care
  25. 25. Personalization 25 Example A snapshot of kBot Conversation where kBot uses patient’s past data and environmental data to provide personalized alert to the patient. A conversation without contextualization and personalization provides a response to the user question like a question answering system. Hi bot! Hi there! It looks like fairly sunny outside. Enjoy the day. How’s the weather today? Thank you! Personalization: Tailored future course of action based on individual characteristics of the patients such as their health history, environment, activity, and lifestyle. Color coded personalized alert based on patient health condition.
  26. 26. 26 Initial user profile setup ➔ Patient ID (Anonymized) ➔ Age ➔ Deployment period (date range) ➔ Gender ➔ Zip Code ➔ Clinician contact point ➔ Prescribed rescue medicine(s) ➔ Prescribed controller medicine(s) Patient consent and deployment User Index ... ... Update Patient Use patient’s past data to generate personalized response. Cloud Database {... Patient data ...} kBot ★ Symptom trend ★ Medication list ★ Medication compliance ★ Personalized triggers ★ Environmental data
  27. 27. Environmental Alert 27 Data watcher Notification Service Trigger_list: { R1: pollen, R2: Ozone, ...} Patient specific Ranked trigger list Ranking: Frequency of co-occurrence with the symptom within 48 hour window. [Shortness of Breath] 5:00 PM [Pollen] > 5 9:00 AM 1 Instance of Co-occurrence HTTP POST {zip, trigger } Personalized Environmental Alert Push Notification() Patients: Zip = affected zip code and trigger in [Trigger_list] Trigger Frequency of co-occurrence Pollen 4 Ozone 2 Temperature 1 July 24th July 23rd Example: Detects environmental parameters in unhealthy range
  28. 28. 28 PEF and Asthma Zone Personal best PEF: Best PEF value out of 3 initial consecutive readings Digital Peak Flow Meter PEF<50% 50%<PEF<80% PEF>80% Zones based on PEF Frequency: Twice a day PEF reading Personal best Asthma zone If Red/Yellow Alert Patient Compares reported PEF with patients personal best
  29. 29. 29 Technical Requirements ★ Continuously monitor patient’s health and environment data. ★ Process these data in near real-time ★ Generate prompt response ★ Scalable over growing end user and their data ★ Secure data processing and exchange
  30. 30. Architecture 30
  31. 31. Services ● Push Notification -generate notification and alert ● Weather -weather report and environmental data ● Data collector -capture data from user text ● Email - Send out email ● FileIO - Log raw conversation ● Elastic -Database operations 31
  32. 32. Data Exchange Security 32 1010...1...111..011.....1110.....0 Client Server HTTP/1.1 Kno.e.sis private cloud database Socket.IO Socket.IO (web-socket) - Real-time data exchange - Persistent connection - Continuous data stream Secure Socket Layer(SSL) certificate - Binds cryptographic key - HTTPS connection - Secure connection between intended parties - Encrypted data transmission HTTP connection - Create, Read, Update, Delete (CRUD) Encrypted Channel
  33. 33. Cloud Database - Database + full-text search engine - Apache Lucene library - HTTP web interface - Schema-free JSON documents (noSQL) - Dynamic mapping - Open Sourced Engineered for performance and scalability 33
  34. 34. Data Snapshots { Severity : "Severe", zip : 45458, gender : "Male", best_peak : 306, chat_id : "f6de6a3601519df63feedd6f1e50d133", token : "ewpdLngmfV8:APA91bEHzFbFIFMUFdwn1a0VTsWjrBjNJfo85oZL 8HoQinIQl_78ScAO6Uev9yYvL7fMUFdwn1a0VTsWjrBjNJf0SSHYz ewpdLngmfV8:APA91bEHzF78ScAO6UevbFIFM-", new_user : "False", LA_med : "singulair - ", end_Time : "2018-11-30 16:48:16.269", otherZipCode : 45459, start_Time : "2018-11-01 16:48:16.269", SA_med : "albuterol - ", rem_1 : "08:00 AM", rem_2 : "9:00 PM", user : "KBtest002", age : "15", kit_number : "kit-001", timestamp : "2018-11-25 16:48:16.134", Clinician_contact: “999-888-7777”, Triggers: [pollen, temp, pm2.5] } { userName : "KBtest002", timeStamp : "2019-01-03T00:59:02.619299-05:00", day-symptoms : "Cough", SA-intake-day : "no", day-activity-limit : "A little", LA-intake : "yes", day_peakflow_reading : "284" } { userName : "KBtest002", timeStamp : "2018-12-07T02:24:03.394125-05:00", SA-intake-current : "yes", current_symptoms : ["Wheeze","chest tightness"], current-activity-limit : "a little" } { Nyt_peakflow_reading : "289", timeStamp : "2019-01-02T13:26:20.629127-05:00", night_Symptoms : ["chest tightness"], userName : "KBtest002", SA-intake-night : "yes" } khealth_userlist question_ans_day question_ans_night question_ans_current Medication merged with Q&A and split in to 3 34
  35. 35. 35 kBot Client ★ Interface: chat room ★ Modes of input:Text and voice ★ Communication protocol: web sockets ★ Voice recognition library: Android Speech-to-text ★ Speech synthesis library: Google cloud Text-to-Speech kBot Android app (primary interface) FrontEnd Light Weight Rich Media Support Quick Reply Push Notification 35
  36. 36. Evaluation 36
  37. 37. Preliminary evaluation for acceptance of the technology as a tool to self-manage asthma. Population: 8 asthma clinicians (domain experts) and 8 computer science researchers (non-domain experts) Evaluation criteria: chatbot performance, technology acceptance and system usability Naturalness ● Dialogue naturalness ● Dialogue ambiguity ● Dialogue simplicity Interpretability ● User intent in the conversation ● User ability to express their asthmatic condition through conversation Information Delivery ● Right information at right time ● Information relevant to asthma care. 37
  38. 38. 38 Metrics Questions Quality of chatbot Naturalness ● kBot uses simple and understandable vocabulary. ● kBot dialogues were unambiguous. ● kBot dialogues were natural. Information delivery ● kBot provides patients with the right information at right time. ● Information provided by kBot helps an asthma patient manage their asthma better. Interpretability ● kBot properly understood what a patient intended to say during the conversation. ● The patients will be able to express their current asthma condition and medication usage accurately through the conversation. Technology acceptance ● The information kBot is trying to collect through the conversation adequately conveys a patient’s asthma condition. ● I recommend this technology to monitor and manage a patient’s daily asthma condition. ● Overall, I am very satisfied with this technology. Evaluation Metrics
  39. 39. Evaluation result 39 11 point Likert Scale Range: 0-10 0 - Strongly Disagreed 10 - Strongly Agreed Total 16 responses 8 - Clinicians 8 - Researchers Mean >= 8.25
  40. 40. System Usability Scale (SUS) 40 ➔ Standard 10 item questionnaire ➔ 5 point likert-scale (ranging 0-4) ➔ Evaluates usability of wide variety of systems ➔ Can be used for small sample sizes ➔ The final SUS score range from 0-100. kBot achieved a SUS score better than 82 from both the evaluation population. > 68 (above Average)
  41. 41. Conclusion 41 - As a chronic multifactorial disease, assessing and managing asthma within the traditional clinical setup has always been challenging. - Technology such as chatbot can engage with patients in their day-to-day life monitoring and helping them manage their asthma. - Use of in-depth contextual knowledge and personalized approach increasing its effectiveness and achieve better compliance.
  42. 42. 42 Future work ★ Incorporate IoT sensors to collect additional patient data such as indoor air quality, sleep and activity to get better insights into patients health. ★ Design a custom language model and train it on real-life patient-doctor conversation data to deliver a more natural and human like conversation. ★ Connect kBot with kHealth Dashboard enabling clinician to have real-time insights into patients health and decision making.
  43. 43. 43
  44. 44. Resources Paper: http://knoesis.org/node/2937 Video: https://youtu.be/e2Vq_3oh8wU Github Repo (private) Server: https://github.com/Dkadariya/KnoAid Android client: https://github.com/Dkadariya/kBot-app Web client: https://github.com/Dkadariya/kbot-web 44
  45. 45. 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. 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 (forthcoming). doi:10.2196/14300 3. 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. 4. Vaikunth Sridharan, Revathy Venkataramanan, Dipesh Kadariya, Krishnaprasad Thirunarayan, Amit Sheth, Maninder Kalra. "Knowledge-enabled Personalized Dashboard for Asthma Management in Children". In proceedings of the American College of Allergy, Asthma & Immunology Annual Meeting(2018). 5. Amit Sheth, Hong Yung Yip, Utkarshani Jaimini, Dipesh Kadariya, Vaikunth Sridharan, Revathy Venkataramanan, Tanvi Banerjee, Krishnaprasad Thirunarayam, Maninder Kalra. "Personalized Health: Using Semantically Integrated Multimodal Data for Patient Empowered Health Management Strategies". Technology Showcase, National Institute of Health - June 2018. 6. Utkarshani Jaimini, Hong Yung Yip, Revathy Venkataramanan, Dipesh Kadariya, Vaikunth Sridharan, Tanvi Banerjee, Krishnaprasad Thirunarayam, Maninder Kalra, Amit Sheth. "Digital Personalized Healthcare technology for Pediatric Asthma". mHealth Technology Showcase, National Institute of Health - June 2018. 7. 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. 8. Amit Sheth, Tanvi Banerjee, Utkarshani Jaimini, Dipesh Kadariya, Vaikunth Sridharan, Krishnaprasad Thirunarayam, Revathy Venkataramanan, Hong Yung Yip, Maninder Kalra. "Correlating Multimodal Signals With Asthma Control In Children Using kHealth Personalized Digital Health System". Abstract in American Thoracic Society (ATS) International Conference , 18-23 May 2018. Publications 45
  46. 46. This research is partially supported by National Institute of Health(NICHD/NIH) under the Grant Number: 1R01HD087132. Any options, findings, and conclusions or recommendations expressed in this work are those of the author(s) and do not necessarily reflect the official views of the National Institute of Health. 46
  47. 47. References 47 1. CDC, “Center for Disease Control and Prevention-Most recent asthma data,” https://www.cdc.gov/asthma/most_recent_data.htm, 2018, [ accessed March 8-2019]. 2. https://www.lung.org/lung-health-and-diseases/lung-disease-lookup/asthma/learn-about-asthma/what-is-asthma.html 3. K. Sumino and M. D. Cabana, “Medication adherence in asthma patients,” Current opinion in pulmonary medicine, vol. 19, no. 1, pp. 49–53, 2013. 4. A. Whittamore, “Technology can revolutionise supported self-management of asthma,” Primary Care Respiratory Society UK vol. 4, 2017. 5. A. Fadhil and S. Gabrielli, “Addressing challenges in promoting healthy lifestyles: the al-chatbot approach,” in Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare. ACM, 2017, pp. 261–265. 6. L. Ni, C. Lu, N. Liu, and J. Liu, “Mandy: Towards a smart primary care chatbot application,” in International Symposium on Knowledge and Systems Sciences. Springer, 2017, pp. 38–52. 7. R. Shah and L. Velez, “Effectiveness of software-based patient education on inhaler technique: A clinical study,” European Respiratory Journal ,vol. 44, no. s58, 2014. 8. Dayer, L., Heldenbrand, S., Anderson, P., Gubbins, P. O., & Martin, B. C. (2013). Smartphone medication adherence apps: potential benefits to patients and providers. Journal of the American Pharmacists Association : JAPhA, 53(2), 172–181. doi:10.1331/JAPhA.2013.12202 9. https://www.webmd.com/asthma/allergic-asthma-what-is-it#1 10. http://www.rtmagazine.com/2007/02/self-monitoring-asthma-at-home/ 11. Colland VT. Learning to cope with asthma: a behavioural self-management program for children. Patient Educ Couns. 1993 Dec 31;22(3):141-52. PubMed PMID: 8153036.
  48. 48. Resources used 48 1. Asthma background: https://www.dreamstime.com/asthma-patient-girl-inhaling-medication-treating-shortness-o-asthma-patient-girl-inhaling-me dication-treating-shortness-image102477568 2. Self-management background: https://freerangestock.com/photos/48233/happy-child-drawing-a-sunny-landscape-.html 3. Medical adherence background: https://www.freepik.com/free-photo/pills-forming-heart_3377211.htm 4. Chatbot background: https://www.freepik.com/free-vector/artificial-intelligence-isometric-ai-robot-mobile-phone-screen-chatbot-app_3629606.ht m 5. Icons: https://icons8.com/
  49. 49. Acknowledgement 49 Dr. Amit Sheth (Advisor) Dr. Krishnaprasad Thirunarayan Dr. Valerie Shalin Dr. Maninder Kalra Committee members: Revathy Venkatramanan (Collaborator) Shreyansh Bhatt Kirill Kultinov Vaikunth Sridharan
  50. 50. kHealth Team 50 Dr. Amit Sheth (Principal investigator) Dr. Krishnaprasad Thirunarayanan (Co-investigator) Dr. Maninder Kalra Co-investigator (Pulmonologist at Dayton Children’s Hospital) Revathy Venkatramanan Utkarshini Jaimini Hong Yung Yip Kirill Kultinov Dr. Tanvi Banerjee (Co-investigator) Faculty Clinical Collaborator Graduate Students
  51. 51. Thank You! 52 Questions?

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