dc.contributor.author |
Daramola, Olawande
|
|
dc.contributor.author |
Nyasulu, Peter
|
|
dc.contributor.author |
Mashamba‑Thompson, Tivani Phosa
|
|
dc.contributor.author |
Moser, Thomas
|
|
dc.contributor.author |
Broomhead, Sean
|
|
dc.contributor.author |
Hamid, Ameera
|
|
dc.contributor.author |
Naidoo, Jaishree
|
|
dc.contributor.author |
Whati, Lindiwe
|
|
dc.contributor.author |
Kotze, Maritha J.
|
|
dc.contributor.author |
Stroetmann, Karl
|
|
dc.contributor.author |
Osamor, Victor Chukwudi
|
|
dc.date.accessioned |
2022-06-10T04:22:13Z |
|
dc.date.available |
2022-06-10T04:22:13Z |
|
dc.date.issued |
2021-09-23 |
|
dc.description.abstract |
A conceptual artificial intelligence (AI)-enabled framework is presented in this study
involving triangulation of various diagnostic methods for management of coronavirus disease 2019
(COVID-19) and its associated comorbidities in resource-limited settings (RLS). The proposed AIenabled
framework will afford capabilities to harness low-cost polymerase chain reaction (PCR)-based
molecular diagnostics, radiological image-based assessments, and end-user provided information
for the detection of COVID-19 cases and management of symptomatic patients. It will support selfdata
capture, clinical risk stratification, explanation-based intelligent recommendations for patient
triage, disease diagnosis, patient treatment, contact tracing, and case management. This will enable
communication with end-users in local languages through cheap and accessible means, such as
WhatsApp/Telegram, social media, and SMS, with careful consideration of the need for personal
data protection. The objective of the AI-enabled framework is to leverage multimodal diagnostics
of COVID-19 and associated comorbidities in RLS for the diagnosis and management of COVID-19
cases and general support for pandemic recovery. We intend to test the feasibility of implementing
the proposed framework through community engagement in sub-Saharan African (SSA) countries
where many people are living with pre-existing comorbidities. A multimodal approach to disease
diagnostics enabling access to point-of-care testing is required to reduce fragmentation of essential
services across the continuum of COVID-19 care. |
en_US |
dc.description.department |
School of Health Systems and Public Health (SHSPH) |
en_US |
dc.description.librarian |
am2022 |
en_US |
dc.description.sponsorship |
The APC was funded by NRF, South Africa and implementation of PSGT by the Technology Innovation Agency of South Africa. |
en_US |
dc.description.uri |
https://www.mdpi.com/journal/informatics |
en_US |
dc.identifier.citation |
Daramola, O.; Nyasulu, P.;
Mashamba-Thompson, T.; Moser, T.;
Broomhead, S.; Hamid, A.; Naidoo, J.;
Whati, L.; Kotze, M.J.; Stroetmann, K.;
et al. Towards AI-Enabled
Multimodal Diagnostics and
Management of COVID-19 and
Comorbidities in Resource-Limited
Settings. Informatics 2021, 8, 63.
https://DOI.org/10.3390/informatics8040063. |
en_US |
dc.identifier.issn |
2227-9709 |
|
dc.identifier.other |
10.3390/informatics8040063 |
|
dc.identifier.uri |
https://repository.up.ac.za/handle/2263/85775 |
|
dc.language.iso |
en |
en_US |
dc.publisher |
MDPI |
en_US |
dc.rights |
© 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license. |
en_US |
dc.subject |
Multimodal diagnostics |
en_US |
dc.subject |
Diagnostics |
en_US |
dc.subject |
Machine learning |
en_US |
dc.subject |
Explainable AI |
en_US |
dc.subject |
Point-of-care |
en_US |
dc.subject |
COVID-19 pandemic |
en_US |
dc.subject |
Coronavirus disease 2019 (COVID-19) |
en_US |
dc.subject |
Artificial intelligence (AI) |
en_US |
dc.subject |
Resource-limited settings (RLS) |
en_US |
dc.subject |
Polymerase chain reaction (PCR) |
en_US |
dc.subject |
Sub-Saharan Africa (SSA) |
en_US |
dc.title |
Towards AI-enabled multimodal diagnostics and management of COVID-19 and comorbidities in resource-limited settings |
en_US |
dc.type |
Article |
en_US |