Automated machine learning for endemic active tuberculosis prediction from multiplex serological data

Abstract Serological diagnosis of active tuberculosis (TB) is enhanced by detection of multiple antibodies due to variable immune responses among patients. Clinical interpretation of these complex datasets requires development of suitable algorithms, a time consuming and tedious undertaking addresse...

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Autores principales:	Hooman H. Rashidi, Luke T. Dang, Samer Albahra, Resmi Ravindran, Imran H. Khan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4053b9a7594e4b98a4c70d5817cb61b7
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spelling	oai:doaj.org-article:4053b9a7594e4b98a4c70d5817cb61b72021-12-02T17:19:16ZAutomated machine learning for endemic active tuberculosis prediction from multiplex serological data10.1038/s41598-021-97453-72045-2322https://doaj.org/article/4053b9a7594e4b98a4c70d5817cb61b72021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97453-7https://doaj.org/toc/2045-2322Abstract Serological diagnosis of active tuberculosis (TB) is enhanced by detection of multiple antibodies due to variable immune responses among patients. Clinical interpretation of these complex datasets requires development of suitable algorithms, a time consuming and tedious undertaking addressed by the automated machine learning platform MILO (Machine Intelligence Learning Optimizer). MILO seamlessly integrates data processing, feature selection, model training, and model validation to simultaneously generate and evaluate thousands of models. These models were then further tested for generalizability on out-of-sample secondary and tertiary datasets. Out of 31 antigens evaluated, a 23-antigen model was the most robust on both the secondary dataset (TB vs healthy) and the tertiary dataset (TB vs COPD) with sensitivity of 90.5% and respective specificities of 100.0% and 74.6%. MILO represents a user-friendly, end-to-end solution for automated generation and deployment of optimized models, ideal for applications where rapid clinical implementation is critical such as emerging infectious diseases.Hooman H. RashidiLuke T. DangSamer AlbahraResmi RavindranImran H. KhanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hooman H. Rashidi Luke T. Dang Samer Albahra Resmi Ravindran Imran H. Khan Automated machine learning for endemic active tuberculosis prediction from multiplex serological data
description	Abstract Serological diagnosis of active tuberculosis (TB) is enhanced by detection of multiple antibodies due to variable immune responses among patients. Clinical interpretation of these complex datasets requires development of suitable algorithms, a time consuming and tedious undertaking addressed by the automated machine learning platform MILO (Machine Intelligence Learning Optimizer). MILO seamlessly integrates data processing, feature selection, model training, and model validation to simultaneously generate and evaluate thousands of models. These models were then further tested for generalizability on out-of-sample secondary and tertiary datasets. Out of 31 antigens evaluated, a 23-antigen model was the most robust on both the secondary dataset (TB vs healthy) and the tertiary dataset (TB vs COPD) with sensitivity of 90.5% and respective specificities of 100.0% and 74.6%. MILO represents a user-friendly, end-to-end solution for automated generation and deployment of optimized models, ideal for applications where rapid clinical implementation is critical such as emerging infectious diseases.
format	article
author	Hooman H. Rashidi Luke T. Dang Samer Albahra Resmi Ravindran Imran H. Khan
author_facet	Hooman H. Rashidi Luke T. Dang Samer Albahra Resmi Ravindran Imran H. Khan
author_sort	Hooman H. Rashidi
title	Automated machine learning for endemic active tuberculosis prediction from multiplex serological data
title_short	Automated machine learning for endemic active tuberculosis prediction from multiplex serological data
title_full	Automated machine learning for endemic active tuberculosis prediction from multiplex serological data
title_fullStr	Automated machine learning for endemic active tuberculosis prediction from multiplex serological data
title_full_unstemmed	Automated machine learning for endemic active tuberculosis prediction from multiplex serological data
title_sort	automated machine learning for endemic active tuberculosis prediction from multiplex serological data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4053b9a7594e4b98a4c70d5817cb61b7
work_keys_str_mv	AT hoomanhrashidi automatedmachinelearningforendemicactivetuberculosispredictionfrommultiplexserologicaldata AT luketdang automatedmachinelearningforendemicactivetuberculosispredictionfrommultiplexserologicaldata AT sameralbahra automatedmachinelearningforendemicactivetuberculosispredictionfrommultiplexserologicaldata AT resmiravindran automatedmachinelearningforendemicactivetuberculosispredictionfrommultiplexserologicaldata AT imranhkhan automatedmachinelearningforendemicactivetuberculosispredictionfrommultiplexserologicaldata
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Automated machine learning for endemic active tuberculosis prediction from multiplex serological data

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