Disease Modeling with a Forest Deep Neural Network Utilizing NLP and a Virtualized Clinical Semantic Network

We present a novel classifier, the Forest Deep Neural Network (fDNN), and apply it to disease modeling. The fDNN architecture leverages a supervised forest for feature detection, enabling the algorithm to build complex neural networks with a large number of independent features from sparse feature r...

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Bibliographic Details
Published in2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) pp. 935 - 942
Main Authors Rahman, Fuad, Rahman, Abrar, Azad Rabby, AKM Shahariar, Rifat, Md Jamiur Rahman, Banik, Mridul, Islam, Md. Majedul, Islam, Aminul, Aziz, Nor Azriah, Meyer, Rick, Kriak, John, Goldblatt, Sidney
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2021
Subjects
Deep learning
Disease modeling
FDNN
Feature extraction
Forestry
Influenza
NLP
Predictive models
Semantics
Training
vCSN
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Summary:We present a novel classifier, the Forest Deep Neural Network (fDNN), and apply it to disease modeling. The fDNN architecture leverages a supervised forest for feature detection, enabling the algorithm to build complex neural networks with a large number of independent features from sparse feature representations while limiting overfitting. We apply the fDNN to model influenza-more specifically, to predict the occurrence of influenza in a cohort of anonymized real-world patients, aided by Natural Language Processing (NLP) and a virtualized Clinical Semantic Network (vCSN). We report accuracy rates in the 90% range, which is extremely high for the problems of disease modeling and applications of NLP.
ISSN:2375-0197
DOI:10.1109/ICTAI52525.2021.00150