Applying Deep Neural Network to Enhance EMPI Searching Stanley Liang Supervisor: Prof. Jimmy Huang School of information technology

What is EMPI EMPI is the acronym of enterprise master patient index EMPI is a database used in a healthcare organization to maintain the data consistency of a variety of EHR information on the patients seen and managed within its various departments. An identical patient has is likely to have different information regarding the same domain in different information systems. EMPI plays a role as a universal identifier or label of an identical patient is the complex EHR system.

Measure of Entry Errors Incorrect search results are most likely to be caused by entry typos Edit distance, or Levenshtein distance, is a way to measure the dissimilarity of two words by counting the minimum number of changes to make them the same Edit distances is commonly applied to natural language processing for automatic spelling correction. To retrieve the due EHR information, we apply the similar idea to EMPI searching to classify the typos into three categories: Match – no error Minor error – 1 ~ 3 errors Major error – 4 or more errors A deep neural network model is applied in this task. The algorithm can be implemented either by MATLAB or by C#.

Deep Learning Deep learning generally means a neural network having more than two hidden layers for performing either supervised or unsupervised learning Deep learning is commonly applied to pattern recognition in unstructured data, e.g. text, photos, videos, and sound Deep learning is capable of discovering intricate structure in large data sets by the back-propagation algorithm Deep learning provides extremely accurate predictions

The Feedforward-Backpropagation learning Cycle

Experiment Environments MATLAB MATLAB 2016b Neural Network Toolbox 20,000 EMPI Records with random errors C# & Visual Studio Accord.NET Framework

Implementing Deep Learning in EMPI Information Retrieval The manual input queries by the users usually have edit errors. Error in text editing: Stanley  Stanly Logical error: Female Male Date error: 2016-11-02 2012-10-02 Problem: if we use SQL query only, the machine cannot learn because SQL only use equal search and range search. If the patient is in the system but the query is wrong, it is difficult to retrieve the information or at high cost of computing time Task Provide adaptive search augmented by deep learning implicit search with good tolerance of occasional manual entry errors Improve system efficiency

Algorithm Implementation Levenshtein Distance Deep Learning

Data set for testing Category Number of records Label description No error 6,000 [1 0 0] Totally correct Minor error 1,000 [0 1 0] ED=1 in a single column randomly, Total=1 ED=2 in a single column randomly, Total=2 ED=3 in a single column randomly, Total=3 ED=1 in two single columns randomly, Total=2 ED=1 in three single columns randomly, Total=2 ED=1 in a random column and ED=2 in another column randomly, Total=3 Major error 2,000 [0 0 1] ED>=4 in a random column, Total>=4 ED>=4 in total in two random columns, Total>=4 ED>=4 in total in three random columns, Total>=4

Ten-fold cross-validation

Confusion Matrix Green Cells represent correct classification The accuracy is 97.4% in this case Class 1: match Class 2: minor error Class 3: major error

Conclusion The deep neural network classifier has stable performance on this work with the average accuracy at 96.3% We conclude that deep learning provides a good artificial intelligence solution for the EMPI query classification to enhance the overall system performance We expect that artificial intelligence (AI) powered by deep learning will play an important role in a variety of healthcare decision making process

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