Fog-Enabled Modular Deep Learning Platform for Textual Data Mining in Healthcare for Pathology Detection in Burkina Faso

Domain:

healthcarenatural language processing

Record type:

paper

Creator:

ConSad

Publisher:

IOS

Host:

In this paper, we propose an architecture for a deep-learning based medical diagnosis support platform in Burkina Faso. This model is built by merging the diagnosis and treatment guide with models derived from textual data recovered via optical character recognition (OCR) on handwritten prescriptions and data from electronic health records. Through simulation, we compared two architectures adapted to the Burkinabe context – a fog-based architecture and cloud-based architecture – and the validated one is the solution best suited to the organization of the country’s health system.

Visit

doi.org

Licenses

https://creativecommons.org/licenses/by-nc/4.0/

Artificial Intelligence System for Automated Breast Cancer Detection in Pathology in Burkina Faso: Methodology Overview

The introduction of artificial intelligence (AI) in breast cancer diagnosis in Burkina Faso represen

Fog-Based Deep Learning for Real-Time Cold Chain Temperature Prediction Using IoT Data

Abstract A third of the food produced globally and in South Africa is lost or wasted annua

IoT-Enabled Deep Learning System for Strawberry Cultivation Viability and Disease Detection in Senegal: Ka-Nguessa

AMHARIC FAKE ACCOUNT DETECTION USING TEXTUAL INFORMATION: - DEEP LEARNING APPROACH

DEEP LEARNING ENABLED ELECTRICITY CONSUMPTION PREDICTION SYSTEM FOR HOUSEHOLDS IN OFFA USING SMART METER DATA

Modern electrical power systems in developing sub-Saharan urb

A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning

We introduce Deep500: the first customizable benchmarking infrastructure that enables fair compariso