Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Patrick Kaggwa"'
Autor:
Juliet Nabbuye Sekandi, Adenike McDonald, Damalie Nakkonde, Sarah Zalwango, Vicent Kasiita, Patrick Kaggwa, Robert Kakaire, Lynn Atuyambe, Esther Buregyeya
Publikováno v:
JMIR Formative Research, Vol 7, p e46203 (2023)
BackgroundIn tuberculosis (TB) control, nonadherence to treatment persists as a barrier. The traditional method of ensuring adherence, that is, directly observed therapy, faces significant challenges that hinder its widespread adoption. Digital adher
Externí odkaz:
https://doaj.org/article/7c7e99416fce47e3b534b70a2c54a50d
Publikováno v:
JMIR AI, Vol 2, p e40167 (2023)
BackgroundArtificial intelligence (AI) applications based on advanced deep learning methods in image recognition tasks can increase efficiency in the monitoring of medication adherence through automation. AI has sparsely been evaluated for the monito
Externí odkaz:
https://doaj.org/article/7c63d8ab3d5248e0b353d9b43ed882db
Autor:
Kenneth Kidonge Katende, Mercy R Amiyo, Sarah Nabukeera, Ian Mugisa, Patrick Kaggwa, Stellah Namatovu, Simon Peter Atwiine, Simon Kasasa
Publikováno v:
PLoS ONE, Vol 17, Iss 9, p e0274112 (2022)
BackgroundTuberculosis (TB) continues to persist with a high disease burden globally. Non-adherence to treatment remains a major problem to TB control. In Uganda, one in every four TB patients does not adhere to their TB medication. The purpose of th
Externí odkaz:
https://doaj.org/article/b93db06010e24ae5819028ddc8e55a85
Autor:
Adenike McDonald, Esther Buregyeya, Robert Kakaire, Juliet Nabbuye Sekandi, Patrick Kaggwa, Lynn Atuyambe, Sarah Zalwango, Damalie Nakkonde
BACKGROUND Nonadherence to treatment remains a barrier to tuberculosis (TB) control. The standard Directly observed therapy (DOT) for monitoring medication to combat adherence has several structural and systemic challenges that limit its implementati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6f29dd313066531c2fdc6b442d215386
https://doi.org/10.2196/preprints.46203
https://doi.org/10.2196/preprints.46203
BACKGROUND Artificial intelligence (AI) applications based on advanced deep learning methods in image recognition tasks can increase efficiency in the monitoring of medication adherence through automation. AI has sparsely been evaluated for the monit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1b8d4765f7ce8678f7ac80e32b6dd5f7
https://doi.org/10.2196/preprints.40167
https://doi.org/10.2196/preprints.40167
Autor:
Kenneth Kidonge Katende, Mercy R. Amiyo, Sarah Nabukeera, Ian Mugisa, Patrick Kaggwa, Stellah Namatovu, Simon Peter Atwiine, Simon Kasasa
Publikováno v:
PloS one. 17(9)
Background Tuberculosis (TB) continues to persist with a high disease burden globally. Non-adherence to treatment remains a major problem to TB control. In Uganda, one in every four TB patients does not adhere to their TB medication. The purpose of t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ddf9069f09bb6db632d00663470c26d
https://pubmed.ncbi.nlm.nih.gov/36084106
https://pubmed.ncbi.nlm.nih.gov/36084106
Autor:
Katende, Kenneth Kidonge1 (AUTHOR) katendeykenneth@yahoo.com, Amiyo, Mercy R.2 (AUTHOR), Nabukeera, Sarah3 (AUTHOR), Mugisa, Ian1 (AUTHOR), Kaggwa, Patrick3 (AUTHOR), Namatovu, Stellah3 (AUTHOR), Atwiine, Simon Peter4 (AUTHOR), Kasasa, Simon3 (AUTHOR)
Publikováno v:
PLoS ONE. 9/9/2022, Vol. 17 Issue 9, p1-16. 16p.