Big Data Analytics: Third International Conference, BDA by Srinath Srinivasa, Sameep Mehta

By Srinath Srinivasa, Sameep Mehta

This ebook constitutes the refereed convention court cases of the 3rd foreign convention on giant facts Analytics, BDA 2014, held in New Delhi, India, in December 2014. The eleven revised complete papers and six brief papers have been rigorously reviewed and chosen from 35 submissions and canopy issues on media analytics; geospatial huge information; semantics and knowledge versions; seek and retrieval; portraits and visualization; application-specific vast information.

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Garg and N. Chatterjee Fig. 10. Accuracy for Naive Bayes and Maximum Entropy Classifier Rather than computing P(featues) explicitly, we can just calculate the numerator for each label, and normalize them so they sum to one: P (label|f eatures) = P (label) ∗ P (f1 |label) ∗ ... ∗ P (fn |label) l (P (l) ∗ P (f1 |l) ∗ ... ∗ P (fn |l)) (7) The results from training the Naive Bayes classifier are shown below in Fig. 10. 18%. 01%). 33%). We can also note that accuracies for 2-step classifier are marginally lesser than those for corresponding 1-step.

They try various features – unigrams, bigrams and Part-of-Speech and train their classifier on various machine learning algorithms – Naive Bayes, Maximum Entropy and Scalable Vector Machines and compare it against a baseline classifier by counting the number of positive and negative words from a publicly available corpus. They report that Bigrams alone and Part-of-Speech Tagging are not helpful and that Naive Bayes Classifier gives the best results. Pak and Paroubek use a similar distant supervision technique to automatically collect the dataset from the web [4].

3 Modeling EHRs Database Currently non-standardized EHRs schemas are adopted by most of the health organizations but ideally this creates a lot of problems. When some data needs to be communicated for the purpose of knowledge transfer, it will be meaningless until same terminology is followed by both organizations. Suitability of Data Models for Electronic Health Records Database 25 Fig. 4. Proposed Data Mo odeling for storing standardized and non-standardized EHRs Organizations such as op penEHR, CEN, ISO and HL7 [15-19] are working on this problem to provide a comm mon standard schema for storing EHRs.

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