Oleksandr Gurbych: Future of precision medicine with AI technologies Data Science Online Camp 2021 Website - https://dscamp.org/ Youtube - https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ/featured FB - https://www.facebook.com/Data-Science-Camp-103012708431833

1. by Oleksandr Gurbych, CEO of blackthorn.ai
AI in Personalised Medicine
Preventing hospital readmissions

2. Content
1. ML Roadmap
2. Business Case
3. Solutions
3.1. Preventing Hospital Readmissions
3.2. Selecting Optimal Care Setting
3.3. MLOps
4. Big Picture

3. Section 1
ML Roadmap

4. ML Roadmap
Best practices

5. Section 2
Business Case

6. Admission
and
readmission
Business
Case

7. Business
Case
Readmissions
and
3rd party
care
providers

8. Section 3
Preventing Hospital
Readmissions

9. Preventing Readmissions
Solution concept

10. Features
Group Name
Predictor
variables
Continuous: median [1st-3rd quartile]
Categorical: largest category (%)
Other
Age
Sex
Number previous admissions
Length of stay
Emergency admissions
Medical / Surgical
Severity of illness
1
1
2
1
1
2
4
56 [39–69]
Male (51%)
0 [0–1]
3 [1–7]
Non-ER (69%)
Medical (55%)
SoI 1 Low (50%)
MDC Major Diagnostics Category 25 MDC 05 diseases & disorders of the circulatory system (12%)
DRG APR-DRG 253 APRDRG 115 other ear, nose, mouth, throat & cranial/facial diagnoses (4%)
Diagnosis
ICD-10 CM diagnosis chapter
ICD-10 CM diagnosis category
ICD-10 CM diagnosis diagnosis
23
1276
4502
I10 essential (primary) hypertension (15%)
Z79 long term (current) drug therapy (20%)
21 factors influencing health status and contact with health services (61%)
Procedure
Procedure chapter
Procedure
13
1564
0 medical and surgical (59%)
3E03329 introduction of other anti-infective into peripheral vein, percutaneous approach (10%)
Preventing Readmissions

11. Section 4
MLOps

12. MLOPs
Best practices

13. MLOPs
Deployment Infrastructure

14. Section 5
Solution - Big Picture

15. Solution
Preventing
hospital
readmissions
&
optimising
care
settings
choice

16. Takeaways
1. Predicted risks of hospital readmissions
2. Implemented recommender system for care settings
3. Automated all the stages of the machine learning work
fl
ow:
- data collection
- feature preparation
- training
- validation
- inference
- monitoring
- feedback loop

17. Thank you!