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2019-06-17T22:32:40Z

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{| class="wikitable" style="float:right; margin-left: 10px;" border="1" cellpadding="10"
|-
! scope="row" | Session
| [[session::Medical]]
|-
! scope="row" | Duration
| [[duration::1.5 hour]]
|-
! scope="row" rowspan="3" | Date/Time
| [[has date::Mar 27 2019 16:00 GMT]]
|-
| 9:00am PDT/12:00pm EDT
|-
| 4:00pm GMT/5:00pm CET
|-
! scope="row" | Co-Champions:
| [[RamDSriram|Ram D. Sriram]] and [[DavidWhitten|David Whitten]]
|}

= [[OntologySummit2019|Ontology Summit 2019]] Medical Explanation Session 2 =

== Agenda ==
The speaker today is:
* Ugur Kursuncu and Manas Gaur
** Explainability of Medical AI through Domain Knowledge
** [http://bit.ly/2YuPUe3 Video Recording]
** Wright State University

== Conference Call Information ==
* Date: '''Wednesday, 27-March-2019'''
* Start Time: 9:00am PDT / 12:00pm EDT / 5:00pm CET / 4:00pm GMT / 1600 UTC
** ref: [http://www.timeanddate.com/worldclock/fixedtime.html?month=03&day=27&year=2019&hour=12&min=00&sec=0&p1=179 World Clock]
* Expected Call Duration: 1.5 hours

* The Video Conference URL is https://zoom.us/j/689971575
** iPhone one-tap :
*** US: +16699006833,,689971575# or +16465588665,,689971575#
** Telephone:
*** Dial(for higher quality, dial a number based on your current location): US: +1 669 900 6833 or +1 646 558 8665
*** Meeting ID: 689 971 575
*** International numbers available: https://zoom.us/u/Iuuiouo
* [http://bit.ly/2MRyySS Chat Room]

== Attendees ==
* [[BruceBray|Bruce Bray]]
* [[KenBaclawski|Ken Baclawski]]
* [[RaviSharma|Ravi Sharma]]
* [[TerryLongstreth|Terry Longstreth]]
* [[ToddSchneider|Todd Schneider]]

== Proceedings ==
[12:09] Ken Baclawski: The recording will be posted after the session, and an outline of the slide content is posted below.

[12:16] RaviSharma: with help of live chat I can now see his slides.

[12:18] ToddSchneider: How should we understand the notion 'concept based information'?

[12:22] RaviSharma: Ugur - what is either the improvement in diagnosis with use of All AI data, multimodal medical data vs only social media data?

[12:23] RaviSharma: or improvement in probability of social media based only data?

[12:26] ToddSchneider: Is the mapping of the 'personal data' into/with the medical knowledge based on natural language terms or phrases?

[12:26] RaviSharma: how does openness of patient in soc media affect the result?

[12:28] RaviSharma: medical entity data?

[12:28] TerryLongstreth: At what point do the subjects (patients..?) know that their social media accounts are being scanned/extracted? Did the research control for intentional misdirection on the part of subjects after they learned? Or was the use of social media data covert/hidden from the subjects?

[12:32] ToddSchneider: Is there an assumption of the existence of social media data for a person?

[12:32] RaviSharma: if you limit the social interaction among the similar patients what do you expect the result to be compared to social media data?

[12:36] ToddSchneider: Perhaps a better question is 'How much personal data is needed' (for the system to be 'useful')?

[12:50] Ken Baclawski: Arash Shaban-Nejad Semantic "Analytics for Global Health Surveillance" will be speaking on April 17. Slides are available at http://bit.ly/2YvlHLK

[12:53] TerryLongstreth: PSQ9 - questionnaire

[12:54] RaviSharma: thanks ken

[12:59] ToddSchneider: I have to get to another meeting. Thank you.

[13:02] RaviSharma: please upload speaker slides, thanks

[13:06] RaviSharma: thanks to speakers

== Resources ==
The following is an outline of the slide content, not including the images.

1. Explainability of Medical AI through Domain Knowledge

* Ugur Kursuncu and Manas Gaur
with Krishnaprasad Thirunarayan and Amit Sheth
* Kno.e.sis Research Center
** Department of Computer Science and Engineering
** Wright State University, Dayton, Ohio USA

2. Why AI systems in Medical Systems

* Growing need for clinical expertise
* Need for rapid and accurate analysis for growing healthcare big data including Patient Generated Health Data and Precision Medicine data
** Improve productivity, efficiency, workflow, accuracy and speed, both for doctors and for patients
** Patient empowerment through smart (actionable) health data

3. Why Explainability in Medical AI Systems

* Trust in AI systems by clinicians and other stakeholders
* Major healthcare consequences
* Legal requirements; need to adhere to guidelines/protocols
* More significant for some specific medical fields, such as mental health

4. Patient-Doctor Relationship

* Cultural and political reason for ownership of personal data
* Privacy concerns for personal data:
** Two stages for permission to use: model creation, personal health decision-making
** Incomplete data due to privacy concerns
* How would AI systems treat patients?
* For personalized healthcare: Researchers or analyzers or doctors need such personal data to provide explainable decisions supported by AI systems

5. How will AI assist humans in medical domain?

* Intelligent assistants through conversational AI (chatbots)
* Multimodal personal data
** Text, voice, image, sensors, demographics
* Help physician burnouts
* Legal implications

6. Challenges

* Common ground and understanding between machines and humans.
** Forming cognitive associations
* Big Multimodal data
* Ultimate goal: Recommending or Acting?

7. Problem: Reasoning over the outcome

* How were the conclusions arrived at?
* If some unintuitive/erroneous conclusions were obtained, how can we trace back and reason about them?

8. A Mental Health Use Case

* Clinician
** Previsit
** In-Visit
** Post-Visit

* Patient
** Recommendations on:
*** Cost
*** Location
*** Relevance to disease

* Big multimodal data for humans!
** Capacity
** Performance
** Efficiency
** Explainability

* Explainability is required as to how data is relevant and significant with respect to the patient situation

9. Explainability vs Interpretability

* Explainability is the combination of interpretability and traceability via a knowledge graph

10. A Mental Health Use Case

* From Patient to Social Media to Clinician to Healthcare

11. A Mental Health Use Case

* From Patient to Clinician via a Black box AI system

12. A Mental Health Use Case

* What is the severity level of suicide risk of a patient?
* ML can be applied to a variety of input data: Text, image, network, sensor, knowledge

13. Explainability with Knowledge

* Explainability through incorporation of knowledge graphs in machine learning processes
** Knowledge enhancement before model is trained
** Knowledge harnessing after model is trained
** Knowledge infusing while model is trained

14. Explanation through knowledge enhancement

15. Relevant Research: Explaining the prediction of mental health disorders (CIKM 2018)

16. Relevant Research: Explaining the prediction of mental health disorders (CIKM 2018)

* Explanation through word features that is created through Semantic Encoding and Decoding Optimization technique
** Semantic encoding of personal data into knowledge space
** Semantic decoding of knowledge into personal data space

17. Relevant Research: Explaining the prediction of mental health disorders (CIKM 2018)

18. Relevant Research: Explaining the prediction of mental health disorders (CIKM 2018)

19. Relevant Research: Explaining the prediction of severity of suicide risk (WWW 2019)

20. Relevant Research: Explaining the prediction of severity of suicide risk (WWW 2019)

* Progression of users through severity levels of suicide risk

21. Explanation through Knowledge Harvesting

22. Relevant Research: Explaining the prediction wisdom of crowd (WebInt 2018)

23. Explanation through Knowledge Infusion

24. Explanation through Knowledge Infusion

* Learning what specific medical knowledge is more important as the information is processed by the model
* Measuring the importance of such infused knowledge
* Specific functions and how they can be operationalized for explainability
** Knowledge-Aware Loss Function (K-LF)
** Knowledge-Modulation Function (K-MF)

25. Evaluation

* ROC & AUC
** Assessments of true positives and false positive rates, to properly measure feature importance
* Inverse Probability Estimates
** Estimate the counterfactual or potential outcome if all patients in dataset were assigned either label or have close estimated probabilities
* PRM: Perceived Risk Measure
** The ratio of disagreement between the predicted and actual outcomes summed over disagreements between the annotators multiplied by a reduction factor that reduces the penalty if the prediction matches any other annotator

26. Evaluation

27. Mental Health Ontology

* Extensively used in this research
* Built based on DSM-5, which is the main guideline documentation for psychiatrists
* Includes: SNOMED-CT, Drug Abuse Ontology and Slang terms

28. Key Takeaways

* Medical explainability is a necessity to form trust for medical community
* Three ways of explainability with knowledge
* Interpretability and traceability are necessary and sufficient conditions for explainability
* Infusing knowledge would further enhance the reasoning capabilities

29. Questions?

== Previous Meetings ==
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== Next Meetings ==
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mainlabel=-|sort=has date |order=asc| format=ul| limit=3}}

[[Category:OntologySummit2019]]
[[Category:Icom_conf_Conference]]
[[Category:Occurrence| ]]

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imported>KennethBaclawski: /* Resources */