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When Healthcare Meets
Data Science
Anastasiia Kornilova
http://www.slideshare.net/WebCongress/mars-one-bas-lansdorp
http://www.slideshare.net/WebCongress/mars-one-bas-lansdorp
The Medicine of the Future
http://www.healthbizdecoded.com/2013/05/hies-meeting-the-sustainability-challenge/
http://graphics.wsj.com/infectious-diseases-and-vaccines/
«One or two patient died per week in a
certain smallish town because of the lack
of information flow between the
hospital’s...
60% of US doctors still use
paper medical records
Let’s create our own EHR standard
Patient
gender
Code
Male 0
Female 1
Patient
gender
Code
Male 1
Female 0
Patient
gender
Code
Male M
Female F
Unknown U
Let’...
[Image Source]
There 5 key data standards
ICD - diagnostic, billing, world-wide
CPT - procedures, billing, US-specific, classification
LOIN...
… and a lot of custom standards
Even within one data standard:
ICD-9
174 malignant neoplasm of female breast
174.1 malignant neoplasm of central portion o...
You have to be a doctor to handle them
Problem summary
Standart 1
Standart 2
Standart N
medicine expertise
a lot of (expensive) hours
Knowledge
Standarts are changing
Artificial Intelligence Way
Feed a lot of medical texts to
«medical doctor»
Use NLP power
Make it unsupervised
Key idea:
«Semantically similar words occurs in similar
contents» Harris, 1954
«You shall know a word by the company it
ke...
«It was the year when Udacity, Coursera and edX, the three
leading MOOC companies, took the education world by storm
and p...
Distributed Vectors
Representation
Two layer neural network
Input: text corpus
Output: set of vectors
Group the vectors of...
Predict a word using content
All
you
need
love
is
Resulting vectors
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....
Vectors Relationships
Vectors Relationships
http://nlp.stanford.edu/projects/glove/images/company_ceo.jpg
http://nlp.stanford.edu/projects/glove/images/comparative_superlative.jpg
ICD-9
174 malignant neoplasm of female breast
174.1 malignant neoplasm of central
portion of female breast
ICD-10
C50 mali...
Summary
Links
Efficient Estimation of Word Representation in
Vector Space (Mikolov)
Distributed representation of words and phrases...
Questions?
When Healthcare Meets Data Science (Anastasiia Kornilova Technology Stream)
When Healthcare Meets Data Science (Anastasiia Kornilova Technology Stream)
When Healthcare Meets Data Science (Anastasiia Kornilova Technology Stream)
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When Healthcare Meets Data Science (Anastasiia Kornilova Technology Stream)

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When Healthcare Meets Data Science (Anastasiia Kornilova Technology Stream)

  1. 1. When Healthcare Meets Data Science Anastasiia Kornilova
  2. 2. http://www.slideshare.net/WebCongress/mars-one-bas-lansdorp
  3. 3. http://www.slideshare.net/WebCongress/mars-one-bas-lansdorp
  4. 4. The Medicine of the Future
  5. 5. http://www.healthbizdecoded.com/2013/05/hies-meeting-the-sustainability-challenge/
  6. 6. http://graphics.wsj.com/infectious-diseases-and-vaccines/
  7. 7. «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 ]
  8. 8. 60% of US doctors still use paper medical records
  9. 9. Let’s create our own EHR standard
  10. 10. 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
  11. 11. [Image Source]
  12. 12. 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
  13. 13. … and a lot of custom standards
  14. 14. 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
  15. 15. You have to be a doctor to handle them
  16. 16. Problem summary Standart 1 Standart 2 Standart N medicine expertise a lot of (expensive) hours Knowledge
  17. 17. Standarts are changing
  18. 18. Artificial Intelligence Way Feed a lot of medical texts to «medical doctor» Use NLP power Make it unsupervised
  19. 19. Key idea: «Semantically similar words occurs in similar contents» Harris, 1954 «You shall know a word by the company it keeps», Firth, 1957
  20. 20. «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]
  21. 21. 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)
  22. 22. Predict a word using content All you need love is
  23. 23. Resulting vectors
  24. 24. 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]
  25. 25. Vectors Relationships
  26. 26. Vectors Relationships
  27. 27. http://nlp.stanford.edu/projects/glove/images/company_ceo.jpg
  28. 28. http://nlp.stanford.edu/projects/glove/images/comparative_superlative.jpg
  29. 29. 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
  30. 30. Summary
  31. 31. 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)
  32. 32. Questions?

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