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Reseach Article

Cohort Search, Representation and Prediction: Application to Medical Data

by Adebayo Salaudeen, Ahmad Abubakar, Ganiyu Saheed
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 13
Year of Publication: 2019
Authors: Adebayo Salaudeen, Ahmad Abubakar, Ganiyu Saheed
10.5120/ijca2019919511

Adebayo Salaudeen, Ahmad Abubakar, Ganiyu Saheed . Cohort Search, Representation and Prediction: Application to Medical Data. International Journal of Computer Applications. 177, 13 ( Oct 2019), 1-8. DOI=10.5120/ijca2019919511

@article{ 10.5120/ijca2019919511,
author = { Adebayo Salaudeen, Ahmad Abubakar, Ganiyu Saheed },
title = { Cohort Search, Representation and Prediction: Application to Medical Data },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2019 },
volume = { 177 },
number = { 13 },
month = { Oct },
year = { 2019 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number13/30954-2019919511/ },
doi = { 10.5120/ijca2019919511 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:45:44.228503+05:30
%A Adebayo Salaudeen
%A Ahmad Abubakar
%A Ganiyu Saheed
%T Cohort Search, Representation and Prediction: Application to Medical Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 13
%P 1-8
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we are interested in cohort search, representation, and prediction. Information retrieval and text mining technique were proposed based on Term Frequency Inverse Document Frequency (TF.IDF) to extract important terms. Also, a formal and algorithmic model was formulated to compute: readable, concise cohorts of patients and find similarities between patient trajectories. Finally, Patient health trajectories were analyzed using a Deep Learning architecture from intensive experimental processes based on two parallel Minimal Gated Recurrent Unit networks, working in a bi-directional manner. The obtained result shows an improvement in the performance of computer-aided medicine and serves as a guide in designing artificial neural networks used in prediction tasks.

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Index Terms

Computer Science
Information Sciences

Keywords

TF-IDF EMR Cohort Neural Networks Deep Learning Patient Trajectory