Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review
Abstract Introduction Diarrhea is still a significant global public health problem. There are currently no systematic evaluation of the modeling areas and approaches to predict diarrheal illness outcomes. This paper reviews existing research efforts in predictive modeling of infectious diarrheal ill...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Learning Health Systems |
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| Online Access: | https://doi.org/10.1002/lrh2.10382 |
| _version_ | 1797351454144462848 |
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| author | Billy Ogwel Vincent Mzazi Bryan O. Nyawanda Gabriel Otieno Richard Omore |
| author_facet | Billy Ogwel Vincent Mzazi Bryan O. Nyawanda Gabriel Otieno Richard Omore |
| author_sort | Billy Ogwel |
| collection | DOAJ |
| description | Abstract Introduction Diarrhea is still a significant global public health problem. There are currently no systematic evaluation of the modeling areas and approaches to predict diarrheal illness outcomes. This paper reviews existing research efforts in predictive modeling of infectious diarrheal illness in pediatric populations. Methods We conducted a systematic review via a PubMed search for the period 1990–2021. A comprehensive search query was developed through an iterative process and literature on predictive modeling of diarrhea was retrieved. The following filters were applied to the search results: human subjects, English language, and children (birth to 18 years). We carried out a narrative synthesis of the included publications. Results Our literature search returned 2671 articles. After manual evaluation, 38 of these articles were included in this review. The most common research topic among the studies were disease forecasts 14 (36.8%), vaccine‐related predictions 9 (23.7%), and disease/pathogen detection 5 (13.2%). Majority of these studies were published between 2011 and 2020, 28 (73.7%). The most common technique used in the modeling was machine learning 12 (31.6%) with various algorithms used for the prediction tasks. With change in the landscape of diarrheal etiology after rotavirus vaccine introduction, many open areas (disease forecasts, disease detection, and strain dynamics) remain for pathogen‐specific predictive models among etiological agents that have emerged as important. Additionally, the outcomes of diarrheal illness remain under researched. We also observed lack of consistency in the reporting of results of prediction models despite the available guidelines highlighting the need for common data standards and adherence to guidelines on reporting of predictive models for biomedical research. Conclusions Our review identified knowledge gaps and opportunities in predictive modeling for diarrheal illness, and limitations in existing attempts whilst advancing some precursory thoughts on how to address them, aiming to invigorate future research efforts in this sphere. |
| first_indexed | 2024-03-08T13:00:41Z |
| format | Article |
| id | doaj.art-c636e3d71d0c49f6becf636eac5aa2a5 |
| institution | Directory Open Access Journal |
| issn | 2379-6146 |
| language | English |
| last_indexed | 2024-03-08T13:00:41Z |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Learning Health Systems |
| spelling | doaj.art-c636e3d71d0c49f6becf636eac5aa2a52024-01-19T10:27:42ZengWileyLearning Health Systems2379-61462024-01-0181n/an/a10.1002/lrh2.10382Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic reviewBilly Ogwel0Vincent Mzazi1Bryan O. Nyawanda2Gabriel Otieno3Richard Omore4Kenya Medical Research Institute, Center for Global Health Research (KEMRI‐CGHR) Kisumu KenyaDepartment of Information Systems University of South Africa Pretoria South AfricaKenya Medical Research Institute, Center for Global Health Research (KEMRI‐CGHR) Kisumu KenyaDepartment of Computing United States International University Nairobi KenyaKenya Medical Research Institute, Center for Global Health Research (KEMRI‐CGHR) Kisumu KenyaAbstract Introduction Diarrhea is still a significant global public health problem. There are currently no systematic evaluation of the modeling areas and approaches to predict diarrheal illness outcomes. This paper reviews existing research efforts in predictive modeling of infectious diarrheal illness in pediatric populations. Methods We conducted a systematic review via a PubMed search for the period 1990–2021. A comprehensive search query was developed through an iterative process and literature on predictive modeling of diarrhea was retrieved. The following filters were applied to the search results: human subjects, English language, and children (birth to 18 years). We carried out a narrative synthesis of the included publications. Results Our literature search returned 2671 articles. After manual evaluation, 38 of these articles were included in this review. The most common research topic among the studies were disease forecasts 14 (36.8%), vaccine‐related predictions 9 (23.7%), and disease/pathogen detection 5 (13.2%). Majority of these studies were published between 2011 and 2020, 28 (73.7%). The most common technique used in the modeling was machine learning 12 (31.6%) with various algorithms used for the prediction tasks. With change in the landscape of diarrheal etiology after rotavirus vaccine introduction, many open areas (disease forecasts, disease detection, and strain dynamics) remain for pathogen‐specific predictive models among etiological agents that have emerged as important. Additionally, the outcomes of diarrheal illness remain under researched. We also observed lack of consistency in the reporting of results of prediction models despite the available guidelines highlighting the need for common data standards and adherence to guidelines on reporting of predictive models for biomedical research. Conclusions Our review identified knowledge gaps and opportunities in predictive modeling for diarrheal illness, and limitations in existing attempts whilst advancing some precursory thoughts on how to address them, aiming to invigorate future research efforts in this sphere.https://doi.org/10.1002/lrh2.10382diarrheamachine learningpediatricpredictive modeling |
| spellingShingle | Billy Ogwel Vincent Mzazi Bryan O. Nyawanda Gabriel Otieno Richard Omore Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review Learning Health Systems diarrhea machine learning pediatric predictive modeling |
| title | Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review |
| title_full | Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review |
| title_fullStr | Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review |
| title_full_unstemmed | Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review |
| title_short | Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review |
| title_sort | predictive modeling for infectious diarrheal disease in pediatric populations a systematic review |
| topic | diarrhea machine learning pediatric predictive modeling |
| url | https://doi.org/10.1002/lrh2.10382 |
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