The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Diagnostic Cost Reduction Using Artificial Neural Networks
Abstract
Patients face a multitude of diseases, trauma, and related medical problems that are difficult and costly to diagnose with respect to direct costs, including pulmonary embolism (PE). Advanced decision-making tools such as artificial neural networks (ANNs) improve diagnostic capabilities for these problematic medical conditions. The research in this chapter develops a backpropagation trained ANN diagnostic model to predict the occurrence of PE. Laboratory database values for 292 patients who were determined to be at risk for a PE, with 15% suffering a confirmed PE, are collected and used to evaluate various ANN models’ performance. Results indicate that using ANN diagnostic models enables the leveraging of knowledge gained from standard clinical laboratory tests, significantly improving both overall positive predictive and negative predictive performance.
Related Content
|
Nadia Ouzennou, Mohamed Aboufaras.
© 2025.
8 pages.
|
|
Imane Barakat, Khalid Barkat, Ikram Baha, Hind Boujguenna, Asma Chaoui, Keltoum Boutahar.
© 2025.
28 pages.
|
|
Rquia Laabidi, Mounia Amane, Saloua Lamtali, Samia Boussaa, Latifa Adarmouch.
© 2025.
14 pages.
|
|
Nawal Elansari, Rabab Loufsahi, Fatima Zahra Ghanimi, Samia Boussaa, Mounia Amane.
© 2025.
24 pages.
|
|
Mohammed El Rhanbouri, Mounia Amane, Abdelhafid Benksim, Abdelati Oussous.
© 2025.
44 pages.
|
|
Amina El Fahli, Mounia Amane, Samia Boussaa, Saloua Lamtali.
© 2025.
26 pages.
|
|
El Mahjoub El Harsi, Abdelhafid Benksim, Fatima Ezzahra Kasmaoui, Said Bouthir, Mohamed Cherkaoui.
© 2025.
28 pages.
|
|
|