The document discusses the intersection of healthcare and data science, emphasizing the importance of standardized medical data to improve patient care. It highlights challenges such as the reliance on paper records and the complexity of medical coding systems. The proposal includes the use of artificial intelligence and natural language processing to enhance medical understanding and streamline information flow.

1. When Healthcare Meets
Data Science
Anastasiia Kornilova

2. http://www.slideshare.net/WebCongress/mars-one-bas-lansdorp

3. http://www.slideshare.net/WebCongress/mars-one-bas-lansdorp

4. The Medicine of the Future

5. http://www.healthbizdecoded.com/2013/05/hies-meeting-the-sustainability-challenge/

6. http://graphics.wsj.com/infectious-diseases-and-vaccines/

8. «One or two patient died per week in a
certain smallish town because of the lack
of information flow between the
hospital’s emergency room and the
nearby mental health clinic»
[«Doing Data Science», O’Neil ]

9. 60% of US doctors still use
paper medical records

10. Let’s create our own EHR standard

11. Patient
gender
Code
Male 0
Female 1
Patient
gender
Code
Male 1
Female 0
Patient
gender
Code
Male M
Female F
Unknown U
Let’s code gender
Standart A
Standart B
Standart C
x
x

12. [Image Source]

13. There 5 key data standards
ICD - diagnostic, billing, world-wide
CPT - procedures, billing, US-specific, classification
LOINC - lab tests and observations, world-wide
NDC - medication, US-specific, classification
SNOMED - medicine

14. … and a lot of custom standards

15. Even within one data standard:
ICD-9
174 malignant neoplasm of female breast
174.1 malignant neoplasm of central portion of female breast
ICD-10
C50 malignant neoplasm of breast
C50.1 malignant neoplasm of central portion of breast
C50.111 malignant neoplasm of central portion of right female
breast
C50.111 malignant neoplasm of central portion of left female
breast

16. You have to be a doctor to handle them

17. Problem summary
Standart 1
Standart 2
Standart N
medicine expertise
a lot of (expensive) hours
Knowledge

18. Standarts are changing

19. Artificial Intelligence Way
Feed a lot of medical texts to
«medical doctor»
Use NLP power
Make it unsupervised

20. Key idea:
«Semantically similar words occurs in similar
contents» Harris, 1954
«You shall know a word by the company it
keeps», Firth, 1957

21. «It was the year when Udacity, Coursera and edX, the three
leading MOOC companies, took the education world by storm
and promised a lot» [Huffington Post]
«Many places offer MOOCs, and many more will. But
Coursera, Udacity and edX are the leading
providers.» [NYTimes]

22. Distributed Vectors
Representation
Two layer neural network
Input: text corpus
Output: set of vectors
Group the vectors of similar
words together in vector
space (detects similarities
matematically)

23. Predict a word using content
All
you
need
love
is

24. Resulting vectors

25. All
you
need
is
love
[0.2, 0.11, 087, 0.9, … , 0.2]
[0.1, 0,98, 01, 0.26, …, 0.82]
[0.7, 0.22, 0.3, 0.1, …, 0.45]
[0.5, 0.21, 0,67, 0.82,…, 0.49]
[0.6, 034, 0.21, 0.45,…, 0.2]

26. Vectors Relationships

27. Vectors Relationships

28. http://nlp.stanford.edu/projects/glove/images/company_ceo.jpg

29. http://nlp.stanford.edu/projects/glove/images/comparative_superlative.jpg

32. ICD-9
174 malignant neoplasm of female breast
174.1 malignant neoplasm of central
portion of female breast
ICD-10
C50 malignant neoplasm of breast
C50.1 malignant neoplasm of central portion of
breast
C50.111 malignant neoplasm of central portion
of right female breast
C50.111 malignant neoplasm of central portion
of left female breast

33. Summary

34. Links
Efficient Estimation of Word Representation in
Vector Space (Mikolov)
Distributed representation of words and phrases and
their compositionality (Mikolov)
word2vec Parameter Learning Explaining (Rong)

35. Questions?