Artificial intelligence in differentiating tropical infections: A step ahead.
Background and objective Differentiating tropical infections are difficult due to its homogenous nature of clinical and laboratorial presentations among them. Sophisticated differential tests and prediction tools are better ways to tackle this issue. Here, we aimed to develop a clinician assisted de...
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ftdoajarticles:oai:doaj.org/article:be17f0a9cfe042c0b46b3da537d40c52 2023-05-15T15:13:05+02:00 Artificial intelligence in differentiating tropical infections: A step ahead. Shreelaxmi Shenoy Asha K Rajan Muhammed Rashid Viji Pulikkel Chandran Pooja Gopal Poojari Vijayanarayana Kunhikatta Dinesh Acharya Sreedharan Nair Muralidhar Varma Girish Thunga 2022-06-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0010455 https://doaj.org/article/be17f0a9cfe042c0b46b3da537d40c52 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0010455 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0010455 https://doaj.org/article/be17f0a9cfe042c0b46b3da537d40c52 PLoS Neglected Tropical Diseases, Vol 16, Iss 6, p e0010455 (2022) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2022 ftdoajarticles https://doi.org/10.1371/journal.pntd.0010455 2022-12-30T22:21:56Z Background and objective Differentiating tropical infections are difficult due to its homogenous nature of clinical and laboratorial presentations among them. Sophisticated differential tests and prediction tools are better ways to tackle this issue. Here, we aimed to develop a clinician assisted decision making tool to differentiate the common tropical infections. Methodology A cross sectional study through 9 item self-administered questionnaire were performed to understand the need of developing a decision making tool and its parameters. The most significant differential parameters among the identified infections were measured through a retrospective study and decision tree was developed. Based on the parameters identified, a multinomial logistic regression model and a machine learning model were developed which could better differentiate the infection. Results A total of 40 physicians involved in the management of tropical infections were included for need analysis. Dengue, malaria, leptospirosis and scrub typhus were the common tropical infections in our settings. Sodium, total bilirubin, albumin, lymphocytes and platelets were the laboratory parameters; and abdominal pain, arthralgia, myalgia and urine output were the clinical presentation identified as better predictors. In multinomial logistic regression analysis with dengue as a reference revealed a predictability of 60.7%, 62.5% and 66% for dengue, malaria and leptospirosis, respectively, whereas, scrub typhus showed only 38% of predictability. The multi classification machine learning model observed to have an overall predictability of 55-60%, whereas a binary classification machine learning algorithms showed an average of 79-84% for one vs other and 69-88% for one vs one disease category. Conclusion This is a first of its kind study where both statistical and machine learning approaches were explored simultaneously for differentiating tropical infections. Machine learning techniques in healthcare sectors will aid in early detection and better patient ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 16 6 e0010455 |
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Directory of Open Access Journals: DOAJ Articles |
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English |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Shreelaxmi Shenoy Asha K Rajan Muhammed Rashid Viji Pulikkel Chandran Pooja Gopal Poojari Vijayanarayana Kunhikatta Dinesh Acharya Sreedharan Nair Muralidhar Varma Girish Thunga Artificial intelligence in differentiating tropical infections: A step ahead. |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Background and objective Differentiating tropical infections are difficult due to its homogenous nature of clinical and laboratorial presentations among them. Sophisticated differential tests and prediction tools are better ways to tackle this issue. Here, we aimed to develop a clinician assisted decision making tool to differentiate the common tropical infections. Methodology A cross sectional study through 9 item self-administered questionnaire were performed to understand the need of developing a decision making tool and its parameters. The most significant differential parameters among the identified infections were measured through a retrospective study and decision tree was developed. Based on the parameters identified, a multinomial logistic regression model and a machine learning model were developed which could better differentiate the infection. Results A total of 40 physicians involved in the management of tropical infections were included for need analysis. Dengue, malaria, leptospirosis and scrub typhus were the common tropical infections in our settings. Sodium, total bilirubin, albumin, lymphocytes and platelets were the laboratory parameters; and abdominal pain, arthralgia, myalgia and urine output were the clinical presentation identified as better predictors. In multinomial logistic regression analysis with dengue as a reference revealed a predictability of 60.7%, 62.5% and 66% for dengue, malaria and leptospirosis, respectively, whereas, scrub typhus showed only 38% of predictability. The multi classification machine learning model observed to have an overall predictability of 55-60%, whereas a binary classification machine learning algorithms showed an average of 79-84% for one vs other and 69-88% for one vs one disease category. Conclusion This is a first of its kind study where both statistical and machine learning approaches were explored simultaneously for differentiating tropical infections. Machine learning techniques in healthcare sectors will aid in early detection and better patient ... |
format |
Article in Journal/Newspaper |
author |
Shreelaxmi Shenoy Asha K Rajan Muhammed Rashid Viji Pulikkel Chandran Pooja Gopal Poojari Vijayanarayana Kunhikatta Dinesh Acharya Sreedharan Nair Muralidhar Varma Girish Thunga |
author_facet |
Shreelaxmi Shenoy Asha K Rajan Muhammed Rashid Viji Pulikkel Chandran Pooja Gopal Poojari Vijayanarayana Kunhikatta Dinesh Acharya Sreedharan Nair Muralidhar Varma Girish Thunga |
author_sort |
Shreelaxmi Shenoy |
title |
Artificial intelligence in differentiating tropical infections: A step ahead. |
title_short |
Artificial intelligence in differentiating tropical infections: A step ahead. |
title_full |
Artificial intelligence in differentiating tropical infections: A step ahead. |
title_fullStr |
Artificial intelligence in differentiating tropical infections: A step ahead. |
title_full_unstemmed |
Artificial intelligence in differentiating tropical infections: A step ahead. |
title_sort |
artificial intelligence in differentiating tropical infections: a step ahead. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2022 |
url |
https://doi.org/10.1371/journal.pntd.0010455 https://doaj.org/article/be17f0a9cfe042c0b46b3da537d40c52 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 16, Iss 6, p e0010455 (2022) |
op_relation |
https://doi.org/10.1371/journal.pntd.0010455 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0010455 https://doaj.org/article/be17f0a9cfe042c0b46b3da537d40c52 |
op_doi |
https://doi.org/10.1371/journal.pntd.0010455 |
container_title |
PLOS Neglected Tropical Diseases |
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16 |
container_issue |
6 |
container_start_page |
e0010455 |
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1766343688217165824 |