This is a poster I did to show my internship work for a computing symposium. The content is about medical document classification using neural network.

1. Application of Manual Document Classification Algorithm
Ran Zhou with Mentor: Peter Merrill
Data
The data we are using for training the document classification
algorithm have the following features:
• Five types of document instances
• Binary labels – Yes/No
• Two data formats
• PDF files
o Get the text part by optical character recognition (OCR)
o Get the labels from the curated database through a link variable
• Curated Database
• Relatively small data size
2018 DUKE RESEARCH COMPUTING
SYMPOSIUM POSTER COMPETITIONIntroduction
Manual document classification is a tedious process of manually
classifying documents instances, which includes two steps:
• Some instances are identified in need of being labeled/classified
• The document instances are labeled/classified according to
rigorous, predefined criteria
Acknowledgements
Leadership Team:
Renato Lopes, MD,MHS,PhD
Schuyler Jones, MD
Michael Pencina, PhD
Larry Carin, PhD
Lisa Wruck, PhD
Shelley Rusincovitch, MM
References
Dinghan Shen et al, On the use of word embeddings alone to
represent natural language sequences, 2017(submitted to ICLR)
Contact Information
Ran Zhou
Master student in statistical science department at Duke
E-mail: rz69@duke.edu
Goal
The goal is to find an algorithm to simulate the manual classification
process. The algorithm will be able to:
• Take text data as the input and automatically give a classification
label as the output.
• Reduce the mistakes created by manual classification and
improve the accuracy.
• Improve the efficiency of the classification process by replacing
manual classification process by machine.
Figure 4. Data Components
Figure 3. Curated Database Summary
• Total instance count: 11000
• Average text length: 44 (min:1, max:1117)
Figure 2. PDF File Summary
• Total instance count: 11033
• Average text length: 1274 (min: 3, max: 24984)
Model
“At the DCRI, our values honor our
history and guide our actions and daily
decisions.… “ (An example text input of
the algorithm)
Yes / NoNeural Network
Figure 1. Classification Algorithm
Figure 5. Simple Word Embedding Based Model (SWEM)
e_1
e_2
E =
e_84
w_1
w_2
w_84
E = Yes
Word Embedding Simple Neural Network
Classification Results
Figure 7. Individual Instance TypeFigure 6. All Instance Type
Conclusion
 The model highly improves the classification accuracy and the computational efficiency comparing to the
traditional neural network model, recurrent neural network(RNN) and convolutional neural network(CNN).
 The classification results for all instance types and individual instance type are much better than random guess
and existing algorithms for similar problems.
 Next, we will test the model on the data from the PDF files to further improve the accuracy.
Type 1 Type 2 Type 3 Type 4 Type 5
2082 3950 3013 689 1299
Type 1 Type 2 Type 3 Type 4 Type 5
2045 3951 3003 689 1312
Yes No Yes No Yes No Sub 1 Yes Sub 2 Yes No /
833 1212 2993 958 291 2712 369 107 213 /
Matt Wilson, RN
Team Members:
Ricardo Henao, PhD
Peter Merrill, PhD
Ran Zhou, BS
Lynn Perkins, MBA
SWEM is a neural network algorithm good at capturing semantic meaning of both individual words and their
compositions simply by a special embedding method. Different from traditional neural network models for capturing
semantic meaning of sentences, SWEM does not require large memory to train a large amount of extra layers.
SWEM is also shown to be able to give more robust results for small data.