Augmented Personalized Health: dHealth approach to patient empowerment for managing chronic disease burden
•Télécharger en tant que PPTX, PDF•
0 j'aime•163 vues
Web site: https://aihealth.ischool.utexas.edu/AIHealthWWW2021/index.html Amit Sheth, Keynote at the International Workshop on AI in Health: Transferring and Integrating Knowledge for Better Health at The Web Conference 2021, 16 April 2021. Abstract: Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions. The exploitation of all relevant data, relevant medical knowledge, and AI techniques will extend and enhance human health and well-being. Augmented Personalized Healthcare (APH) strategy as we have defined involves empowering patients with self-monitoring (collecting relevant data), self-appraisal (interpreting data in the patient's context), self-management (assisting the patient in following personalized care plan to maintain health), to intervention (when the clinical help is needed) and disease progression tracking and prediction (http://bit.ly/AI-APH, http://bit.ly/APH-TED). While we have early investigations for several diseases, we will share some experience (such as developing a digital phenotype) from pediatric asthma that involved an evaluation with ~200 patients (http://bit.ly/kAsthma).
Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (20)
Similaire à Augmented Personalized Health: dHealth approach to patient empowerment for managing chronic disease burden
Similaire à Augmented Personalized Health: dHealth approach to patient empowerment for managing chronic disease burden (20)
Dernier
Dernier (20)
Augmented Personalized Health: dHealth approach to patient empowerment for managing chronic disease burden
- 1. Augmented Personalized Health dHealth approach to patient empowerment for managing chronic disease burden Background: http://bit.ly/APH-TED, http://bit.ly/k-APH, http://bit.ly/kAsthma The Web Conference 2021 International Workshop on AI in Health: Transferring and Integrating Knowledge for Better Health Keynote on April 16, 2021
- 2. 2 Amit Sheth Founding Director, Artificial Intelligence Institute http://aiisc.ai The University of South Carolina amit@sc.edu http://amit.aiisc.ai Special Thanks Some of the Healthcare collaborators: Maninder Kalra (Dayton Children’s Hospital) Phillis Raynor, Ronda Huges, Sara Donevant(UofSC) Some of the CS collaborators: Revathy Venkatraman, Joey Yip, Manas Gaur (AIISC) Krishnaprasad Thirunarayan (Wright State University Kaushik Roy AIISC, kaushikr@email.sc.edu Utkarshani Jaimini AIISC, ujaimini@email.sc.edu Ack: NIH/NICHD 1 R01 HD087132-01: KHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care
- 3. Healthcare projects in AI Institute
- 4. Augmented Personalized Health Big Data to Smart Data Augmented Personalised Health(APH) is a vision to enhance the healthcare by using AI techniques on semantically integrated PGHD, environmental data, clinical data, public health data & social data.
- 5. Smart Data: Data with Knowledge Smart data can answer - What causes my disease severity? - How well am I doing with respect to prescribed care plan? - Am I deviating from the care plan? - I am following the care plan but my disease is not well controlled. Do I need treatment adjustments? - How well controlled is my disease over the time?
- 6. APH: Health Management Strategies
- 7. Knowledge enabled Healthcare: kHealth Asthma A multisensory approach for personalized asthma care for children ◎ 6.3 million children in USA are affected by Asthma; 300 million adults & children worldwide ◎ Multifactorial disease, difficult to diagnose based on episodic visits and clinical records ◎ Stage 1: Mobile App with 29 parameters collected ◎ Stage 2: Virtual Health Assistant
- 8. Augmented Personalized Health for Asthma
- 10. Self Monitoring with kHealthDash: Knowledge enabled personalized DASHboard for Asthma Management Video link - https://youtu.be/yUgXCPwc55M
- 11. Self Appraisal with Digital Phenotype Score Jaimini U, Thirunarayan K, Kalra M, Venkataraman R, Kadariya D, Sheth A, “How Is My Child’s Asthma?” Digital Phenotype and Actionable Insights for Pediatric Asthma, JMIR Pediatric Parent 2018;1(2):e11988 https://medium.com/leoilab/digital-phenotyping-turning-our-smartphones-inward-141a75b2f2a3 ● Digital Phenotype Score (DPS) is defined as the score to quantify the digital phenotypes collected from the social media, smartphones, wearables, and sensors streams. ● DPS acts as a cumulative measure for the abstraction of knowledge and information from the raw digital phenotypic data. ● The integration of the DPS can enable personalized interventions in real time which are directly responsive to the healthcare need of a patient.
- 12. Digital Phenotype Score vs Asthma Control Test Score Jaimini U, Thirunarayan K, Kalra M, Venkataraman R, Kadariya D, Sheth A, “How Is My Child’s Asthma?” Digital Phenotype and Actionable Insights for Pediatric Asthma, JMIR Pediatric Parent 2018;1(2):e11988
- 13. Determining Personalized Asthma Triggers: Seasonal Dependency Venkataramanan R, Thirunarayan K, Jaimini U, Kadariya D, Yip HY, Kalra M, Sheth A. Determination of Personalized Asthma Triggers From Multimodal Sensing and a Mobile App: Observational Study. JMIR Pediatr Parent 2019;2(1):e14300. doi: 10.2196/14300 PMID: 31518318 PMCID: 6716491
- 14. Evidence based Path to Personalization Patient-A was monitored for 13 weeks encompassing winter to spring 2018. Type: Severe, low medication compliance. Absence of Pollen First 6 weeks Presence of Pollen Rest of the 7 weeks Pre (observe) 4 weeks Post (validate) 2 weeks Pre 4 weeks Post 3 weeks Pollen 0 Pollen 0 days Pollen 17 days Pollen 3 days PM2.5 20 days PM2.5 5 days PM2.5 14 days PM2.5 2 days Ozone 1 day Ozone 0 Ozone 0 Ozone 1 day Asthma Episodes* 21 days Asthma Episodes 5 days Asthma Episodes 17 days Asthma Episodes 3 days ● Absence of Pollen - PM2.5 is the trigger ● Presence of Pollen - Pollen and PM2.5. Severe symptoms occurred in this period. Presence of both PM2.5 and Pollen increased the intensity of asthma episodes. Venkataramanan R, Thirunarayan K, Jaimini U, Kadariya D, Yip HY, Kalra M, Sheth A. Determination of Personalized Asthma Triggers From Multimodal Sensing and a Mobile App: Observational Study. JMIR Pediatr Parent 2019;2(1):e14300. doi: 10.2196/14300 PMID: 31518318 PMCID: 6716491
- 15. Interventional Strategy Actionable insights using Digital Phenotype Score and Controller Compliance Score
- 16. Foobot – 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 Time of day Pollen level Increase in Pollen Level in the evening Virtual Health Assistant Actionable Information Personal level Signals Public level Signals Population level Signals What is the air quality indoors? Carry your inhaler with you when going outside A Scenario
- 17. Personalized Health Knowledge Graph
- 18. kHealth Chatbot Contextualization and Personalization kBOT initiates greeting conversation. Understands the patient’s health condition (allergic reaction to high ragweed pollen level) via the personalized patient’s knowledge graph generated from EMR, PGHD, and prior interactions with the kBOT. Generates predictions or recommended course of actions. Inference based on patient’s historical records and background health knowledge graph containing contextualized (domain-specific) knowledge. Figure: Example kBOT conversation which utilizes background health knowledge graph and patient’s knowledge graph to infer and generate recommendation to patients. ★ Conversing only information relevant to the patient Context enabled by relevant healthcare knowledge including clinical protocols. kBOT initiates greeting conversation.
- 19. kHealth Chatbot Demo https://youtu.be/oZCaM7rhJAg More on: https://www.slideshare.net/aiisc/kbot-knowledgeenabled-personalized-chatbot-for-selfmanagement-of-asthma-in-pediatric-population
- 20. APH: Virtual Health Assistant assisted Self Management using Reinforcement Learning and Personalized Knowledge Graph Use case: Mental Health
- 21. Construction of Personalized Knowledge Graph
- 22. Knowledge-infused Reinforcement Learning ● The input to the agent is sequential through many steps, it gets an input and a reward at every step and learns the right output gradually through reinforcement.
- 23. APH Self Management: Reinforcement Learning
- 24. “ 24 AIISC in core AI areas, and interdisciplinary AI/AI applications
- 25. Thanks! Open to Questions? You can find me at: amit@sc.edu https://aiisc.ai/ https://www.linkedin.com/company/1054055/ http://bit.ly/AIISC 25 Acknowledgement This research is supported by National Institutes of Health under the Grant Number 1 R01HD087132. The content of this study is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
- 26. Health related studies at the AI Institute
Notes de l'éditeur
- Patient was deployed for 13 weeks in winter to spring. The deployment period was divided into two (absence of pollen and presence of pollen) to disambiguate triggers. The first 6 weeks, absence of pollen, was divided further into learning period (where we learn the association between triggers and asthma symptoms) and prediction period (use what we learned to predict the symptoms). In the absence of pollen, we learned that PM2.5 is the trigger (# days PM2.5 is above normal vs # days of asthma episodes). In the prediction period, we saw that PM2.5 is high on 5 days and predicted that asthma episode will occur on those 5 days. It happened as predicted. In the presence of pollen, we predicted pollen and PM2.5 to be triggers and it was proved. Besides that, in the presence of pollen, the patient started receiving severe symptoms such as chest tightness, night awakenings and activity limitations proving that combination of these triggers is worsening their asthma condition.
- How is the bot initialized from the initial information. PKG continues to be updated with basic patient data, discharge summary, continuous patient interactions.
- How does a high level recommendation translate to dialogue and subsequent PKG updates
- Slide 3: Inner circle : talks about our research areas and strength