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The code repository for the prototypes included in the eBook "Inspired EHRs - Designing for Clinicians" (inspiredEHRs.gov). The code of the prototypes is made available under the Apache 2.0 open source license. This license agreement allows anyone to freely use the code and ideas presented in this book, subject to the conditions listed at http://opensource.org/licenses/Apache-2.0.

  • Updated Aug 13, 2019
  • 124 commits
  • JavaScript

Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use them for predicting the values of a certain field, given that we have information regarding the other fields. Most specifically, in this study, we look at the Electronic Health Records (EHRs) that are compiled by hospitals. These EHRs are convenient means of accessing data of individual patients, but there processing as a whole still remains a task. However, EHRs that are composed of coherent, well-tabulated structures lend themselves quite well to the application to machine language, via the usage of classifiers. In this study, we look at a Blood Transfusion Service Center Data Set (Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan). We used scikit-learn machine learning in python. From Support Vector Machines(SVM), we use Support Vector Classification(SVC), from the linear model we import Perceptron. We also used the K.neighborsclassifier and the decision tree classifiers. We segmented the database into the 2 parts. Using the first, we trained the classifiers and the next part was used to verify if the classifier prediction matched that of the actual values.

  • Updated May 2, 2019
  • 12 commits
  • Python
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