NLP Structured Data Investigation on Non-Text
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1) The document discusses using an unsupervised machine learning technique called Word2Vec, normally used in natural language processing, to analyze structured medical data. 2) Word2Vec learns vector representations of words based on their co-occurrence with other words. The author proposes treating medical concepts like vitals, labs, diagnoses from patient encounters as "words" to learn their relationships. 3) A demo is shown applying Word2Vec to a dataset of 197,340 clinical records from 10,000 patients to explore connections between medical concepts.
![Word2Vec
Word2Vec is a vectorization model created by Google [1] that
attempts to learn relationships between words automatically given a
large corpus of sentences.
• Gives us a way to find similar words by finding near neighbors in the
vector space with cosine similarity.
1
http://radimrehurek.com/2014/12/making-sense-of-word2vec/
Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015](https://image.slidesharecdn.com/nlponnontextualdata-151028214835-lva1-app6891/85/NLP-Structured-Data-Investigation-on-Non-Text-6-320.jpg)
![Word2Vec
Word2Vec is a vectorization model created by Google [1] that
attempts to learn relationships between words automatically given a
large corpus of sentences.
• Gives us a way to find similar words by finding near neighbors in the
vector space with cosine similarity.
• Uses a neural network to learn vector representations.
1
http://radimrehurek.com/2014/12/making-sense-of-word2vec/
Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015](https://image.slidesharecdn.com/nlponnontextualdata-151028214835-lva1-app6891/85/NLP-Structured-Data-Investigation-on-Non-Text-7-320.jpg)
![Word2Vec
Word2Vec is a vectorization model created by Google [1] that
attempts to learn relationships between words automatically given a
large corpus of sentences.
• Gives us a way to find similar words by finding near neighbors in the
vector space with cosine similarity.
• Uses a neural network to learn vector representations.
• Work by Pennington, Socher, and Manning [2] shows that the
word2vec model is equivalent to a word co-occurance matrix
weighting based on window distance and lowering the dimension by
matrix factorization.
1
http://radimrehurek.com/2014/12/making-sense-of-word2vec/
Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015](https://image.slidesharecdn.com/nlponnontextualdata-151028214835-lva1-app6891/85/NLP-Structured-Data-Investigation-on-Non-Text-8-320.jpg)
![Word2Vec
Word2Vec is a vectorization model created by Google [1] that
attempts to learn relationships between words automatically given a
large corpus of sentences.
• Gives us a way to find similar words by finding near neighbors in the
vector space with cosine similarity.
• Uses a neural network to learn vector representations.
• Work by Pennington, Socher, and Manning [2] shows that the
word2vec model is equivalent to a word co-occurance matrix
weighting based on window distance and lowering the dimension by
matrix factorization.
Takeaway: The technique boils down, intuitively, to a riff on word
co-occurence. See here1 for more.
1
http://radimrehurek.com/2014/12/making-sense-of-word2vec/
Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015](https://image.slidesharecdn.com/nlponnontextualdata-151028214835-lva1-app6891/85/NLP-Structured-Data-Investigation-on-Non-Text-9-320.jpg)
![Bibliography
[1] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean.
Efficient estimation of word representations in vector space. CoRR,
abs/1301.3781, 2013.
[2] Jeffrey Pennington, Richard Socher, and Christopher Manning.
Glove: Global vectors for word representation. In Proceedings of
the 2014 Conference on Empirical Methods in Natural Language
Processing (EMNLP), pages 1532–1543. Association for
Computational Linguistics, 2014.
Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015](https://image.slidesharecdn.com/nlponnontextualdata-151028214835-lva1-app6891/85/NLP-Structured-Data-Investigation-on-Non-Text-15-320.jpg)
NLP Structured Data Investigation on Non-Text
- 1. NLP Structured Data Investigation on Non-Text Casey Stella @casey_stella 2015 Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015
- 2. Table of Contents Preliminaries Borrowing from NLP Demo Questions Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015
- 3. Introduction • I’m a Principal Architect at Hortonworks • I work primarily doing Data Science in the Hadoop Ecosystem • Prior to this, I’ve spent my time and had a lot of fun ◦ Doing data mining on medical data at Explorys using the Hadoop ecosystem ◦ Doing signal processing on seismic data at Ion Geophysical using MapReduce ◦ Being a graduate student in the Math department at Texas A&M in algorithmic complexity theory Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015
- 4. Domain Challenges in Data Science A data scientist has to merge analytical skills with domain expertise. • Often we’re thrown into places where we have insufficient domain experience. • Gaining this expertise can be challenging and time-consuming. • Unsupervised machine learning techniques can be very useful to understand complex data relationships. Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015
- 5. Domain Challenges in Data Science A data scientist has to merge analytical skills with domain expertise. • Often we’re thrown into places where we have insufficient domain experience. • Gaining this expertise can be challenging and time-consuming. • Unsupervised machine learning techniques can be very useful to understand complex data relationships. We’ll use an unsupervised structure learning algorithm borrowed from NLP to look at medical data. Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015
- 6. Word2Vec Word2Vec is a vectorization model created by Google [1] that attempts to learn relationships between words automatically given a large corpus of sentences. • Gives us a way to find similar words by finding near neighbors in the vector space with cosine similarity. 1 http://radimrehurek.com/2014/12/making-sense-of-word2vec/ Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015
- 7. Word2Vec Word2Vec is a vectorization model created by Google [1] that attempts to learn relationships between words automatically given a large corpus of sentences. • Gives us a way to find similar words by finding near neighbors in the vector space with cosine similarity. • Uses a neural network to learn vector representations. 1 http://radimrehurek.com/2014/12/making-sense-of-word2vec/ Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015
- 8. Word2Vec Word2Vec is a vectorization model created by Google [1] that attempts to learn relationships between words automatically given a large corpus of sentences. • Gives us a way to find similar words by finding near neighbors in the vector space with cosine similarity. • Uses a neural network to learn vector representations. • Work by Pennington, Socher, and Manning [2] shows that the word2vec model is equivalent to a word co-occurance matrix weighting based on window distance and lowering the dimension by matrix factorization. 1 http://radimrehurek.com/2014/12/making-sense-of-word2vec/ Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015
- 9. Word2Vec Word2Vec is a vectorization model created by Google [1] that attempts to learn relationships between words automatically given a large corpus of sentences. • Gives us a way to find similar words by finding near neighbors in the vector space with cosine similarity. • Uses a neural network to learn vector representations. • Work by Pennington, Socher, and Manning [2] shows that the word2vec model is equivalent to a word co-occurance matrix weighting based on window distance and lowering the dimension by matrix factorization. Takeaway: The technique boils down, intuitively, to a riff on word co-occurence. See here1 for more. 1 http://radimrehurek.com/2014/12/making-sense-of-word2vec/ Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015
- 10. Clinical Data as Sentences Clinical encounters form a sort of sentence over time. For a given encounter: • Vitals are measured (e.g. height, weight, BMI). • Labs are performed and results are recorded (e.g. blood tests). • Procedures are performed. • Diagnoses are made (e.g. Diabetes). • Drugs are prescribed. Each of these can be considered clinical “words” and the encounter forms a clinical “sentence”. Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015
- 11. Clinical Data as Sentences Clinical encounters form a sort of sentence over time. For a given encounter: • Vitals are measured (e.g. height, weight, BMI). • Labs are performed and results are recorded (e.g. blood tests). • Procedures are performed. • Diagnoses are made (e.g. Diabetes). • Drugs are prescribed. Each of these can be considered clinical “words” and the encounter forms a clinical “sentence”. Idea: We can use word2vec to investigate connections between these clinical concepts. Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015
- 12. Demo As part of a Kaggle competition2, Practice Fusion, a digital electronic medical records provider released depersonalized clinical records of 10,000 patients. I ingested and preprocessed these records into 197,340 clinical “sentences” using Pig and Hive. 2 https://www.kaggle.com/c/pf2012-diabetes Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015
- 13. Demo As part of a Kaggle competition2, Practice Fusion, a digital electronic medical records provider released depersonalized clinical records of 10,000 patients. I ingested and preprocessed these records into 197,340 clinical “sentences” using Pig and Hive. MLLib from Spark now contains an implementation of word2vec, so let’s use pyspark and IPython Notebook to explore this dataset on Hadoop. 2 https://www.kaggle.com/c/pf2012-diabetes Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015
- 14. Questions Thanks for your attention! Questions? • Code & scripts for this talk available on my github presentation page.3 • Find me at http://caseystella.com • Twitter handle: @casey_stella • Email address: cstella@hortonworks.com 3 http://github.com/cestella/presentations/ Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015
- 15. Bibliography [1] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. CoRR, abs/1301.3781, 2013. [2] Jeffrey Pennington, Richard Socher, and Christopher Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543. Association for Computational Linguistics, 2014. Casey Stella@casey_stella (Hortonworks)NLP Structured Data Investigation on Non-Text 2015