Progress and challenges for the application of machine learning for neglected tropical diseases [version 3; peer review: 1 approved, 2 approved with reservations]
Neglected tropical diseases (NTDs) continue to affect the livelihood of individuals in countries in the Southeast Asia and Western Pacific region. These diseases have been long existing and have caused devastating health problems and economic decline to people in low- and middle-income (developing)...
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F1000 Research Ltd
2025-07-01
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| Online Access: | https://f1000research.com/articles/12-287/v3 |
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| author | Rahmad Akbar Norfarhan Mohd-Assaad ChungYuen Khew |
| author_facet | Rahmad Akbar Norfarhan Mohd-Assaad ChungYuen Khew |
| author_sort | Rahmad Akbar |
| collection | DOAJ |
| description | Neglected tropical diseases (NTDs) continue to affect the livelihood of individuals in countries in the Southeast Asia and Western Pacific region. These diseases have been long existing and have caused devastating health problems and economic decline to people in low- and middle-income (developing) countries. An estimated 1.7 billion of the world’s population suffer one or more NTDs annually, this puts approximately one in five individuals at risk for NTDs. In addition to health and social impact, NTDs inflict significant financial burden to patients, close relatives, and are responsible for billions of dollars lost in revenue from reduced labor productivity in developing countries alone. There is an urgent need to better improve the control and eradication or elimination efforts towards NTDs. This can be achieved by utilizing machine learning tools to better the surveillance, prediction and detection program, and combat NTDs through the discovery of new therapeutics against these pathogens. This review surveys the current application of machine learning tools for NTDs and the challenges to elevate the state-of-the-art of NTDs surveillance, management, and treatment. |
| format | Article |
| id | doaj-art-111a98d649414d70b9614bde19b078be |
| institution | DOAJ |
| issn | 2046-1402 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj-art-111a98d649414d70b9614bde19b078be2025-08-20T02:40:07ZengF1000 Research LtdF1000Research2046-14022025-07-011210.12688/f1000research.129064.3184903Progress and challenges for the application of machine learning for neglected tropical diseases [version 3; peer review: 1 approved, 2 approved with reservations]Rahmad Akbar0https://orcid.org/0000-0002-6692-0876Norfarhan Mohd-Assaad1https://orcid.org/0000-0002-7543-5805ChungYuen Khew2https://orcid.org/0000-0002-1061-4378Department of Immunology, University of Oslo, Oslo, Oslo, 0372, NorwayInstitute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, MalaysiaDepartment of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, MalaysiaNeglected tropical diseases (NTDs) continue to affect the livelihood of individuals in countries in the Southeast Asia and Western Pacific region. These diseases have been long existing and have caused devastating health problems and economic decline to people in low- and middle-income (developing) countries. An estimated 1.7 billion of the world’s population suffer one or more NTDs annually, this puts approximately one in five individuals at risk for NTDs. In addition to health and social impact, NTDs inflict significant financial burden to patients, close relatives, and are responsible for billions of dollars lost in revenue from reduced labor productivity in developing countries alone. There is an urgent need to better improve the control and eradication or elimination efforts towards NTDs. This can be achieved by utilizing machine learning tools to better the surveillance, prediction and detection program, and combat NTDs through the discovery of new therapeutics against these pathogens. This review surveys the current application of machine learning tools for NTDs and the challenges to elevate the state-of-the-art of NTDs surveillance, management, and treatment.https://f1000research.com/articles/12-287/v3Neglected Tropical Diseases Machine Learning Drug Development Drug Discovery.eng |
| spellingShingle | Rahmad Akbar Norfarhan Mohd-Assaad ChungYuen Khew Progress and challenges for the application of machine learning for neglected tropical diseases [version 3; peer review: 1 approved, 2 approved with reservations] F1000Research Neglected Tropical Diseases Machine Learning Drug Development Drug Discovery. eng |
| title | Progress and challenges for the application of machine learning for neglected tropical diseases [version 3; peer review: 1 approved, 2 approved with reservations] |
| title_full | Progress and challenges for the application of machine learning for neglected tropical diseases [version 3; peer review: 1 approved, 2 approved with reservations] |
| title_fullStr | Progress and challenges for the application of machine learning for neglected tropical diseases [version 3; peer review: 1 approved, 2 approved with reservations] |
| title_full_unstemmed | Progress and challenges for the application of machine learning for neglected tropical diseases [version 3; peer review: 1 approved, 2 approved with reservations] |
| title_short | Progress and challenges for the application of machine learning for neglected tropical diseases [version 3; peer review: 1 approved, 2 approved with reservations] |
| title_sort | progress and challenges for the application of machine learning for neglected tropical diseases version 3 peer review 1 approved 2 approved with reservations |
| topic | Neglected Tropical Diseases Machine Learning Drug Development Drug Discovery. eng |
| url | https://f1000research.com/articles/12-287/v3 |
| work_keys_str_mv | AT rahmadakbar progressandchallengesfortheapplicationofmachinelearningforneglectedtropicaldiseasesversion3peerreview1approved2approvedwithreservations AT norfarhanmohdassaad progressandchallengesfortheapplicationofmachinelearningforneglectedtropicaldiseasesversion3peerreview1approved2approvedwithreservations AT chungyuenkhew progressandchallengesfortheapplicationofmachinelearningforneglectedtropicaldiseasesversion3peerreview1approved2approvedwithreservations |