Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios

HIV/AIDS is an ongoing global pandemic, with an estimated 39 million infected worldwide. Early detection is anticipated to help improve outcomes and prevent further infections. Point-of-care diagnostics make HIV/AIDS diagnoses available both earlier and to a broader population. Wide-spread and autom...

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Autores principales:	Oliver Haas, Andreas Maier, Eva Rothgang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	HIV risk estimation association rules bias clinical data machine learning Reproduction QH471-489 Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/1cb6aaa04f004d548bf2faf1f47a3090
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spelling	oai:doaj.org-article:1cb6aaa04f004d548bf2faf1f47a30902021-12-02T10:08:54ZMachine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios2673-315310.3389/frph.2021.756405https://doaj.org/article/1cb6aaa04f004d548bf2faf1f47a30902021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/frph.2021.756405/fullhttps://doaj.org/toc/2673-3153HIV/AIDS is an ongoing global pandemic, with an estimated 39 million infected worldwide. Early detection is anticipated to help improve outcomes and prevent further infections. Point-of-care diagnostics make HIV/AIDS diagnoses available both earlier and to a broader population. Wide-spread and automated HIV risk estimation can offer objective guidance. This supports providers in making an informed decision when considering patients with high HIV risk for HIV testing or pre-exposure prophylaxis (PrEP). We propose a novel machine learning method that allows providers to use the data from a patient's previous stays at the clinic to estimate their HIV risk. All features available in the clinical data are considered, making the set of features objective and independent of expert opinions. The proposed method builds on association rules that are derived from the data. The incidence rate ratio (IRR) is determined for each rule. Given a new patient, the mean IRR of all applicable rules is used to estimate their HIV risk. The method was tested and validated on the publicly available clinical database MIMIC-IV, which consists of around 525,000 hospital stays that included a stay at the intensive care unit or emergency department. We evaluated the method using the area under the receiver operating characteristic curve (AUC). The best performance with an AUC of 0.88 was achieved with a model consisting of 53 rules. A threshold value of 0.66 leads to a sensitivity of 98% and a specificity of 53%. The rules were grouped into drug abuse, psychological illnesses (e.g., PTSD), previously known associations (e.g., pulmonary diseases), and new associations (e.g., certain diagnostic procedures). In conclusion, we propose a novel HIV risk estimation method that builds on existing clinical data. It incorporates a wide range of features, leading to a model that is independent of expert opinions. It supports providers in making informed decisions in the point-of-care diagnostics process by estimating a patient's HIV risk.Oliver HaasOliver HaasAndreas MaierEva RothgangFrontiers Media S.A.articleHIVrisk estimationassociation rulesbiasclinical datamachine learningReproductionQH471-489Medicine (General)R5-920ENFrontiers in Reproductive Health, Vol 3 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	HIV risk estimation association rules bias clinical data machine learning Reproduction QH471-489 Medicine (General) R5-920
spellingShingle	HIV risk estimation association rules bias clinical data machine learning Reproduction QH471-489 Medicine (General) R5-920 Oliver Haas Oliver Haas Andreas Maier Eva Rothgang Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios
description	HIV/AIDS is an ongoing global pandemic, with an estimated 39 million infected worldwide. Early detection is anticipated to help improve outcomes and prevent further infections. Point-of-care diagnostics make HIV/AIDS diagnoses available both earlier and to a broader population. Wide-spread and automated HIV risk estimation can offer objective guidance. This supports providers in making an informed decision when considering patients with high HIV risk for HIV testing or pre-exposure prophylaxis (PrEP). We propose a novel machine learning method that allows providers to use the data from a patient's previous stays at the clinic to estimate their HIV risk. All features available in the clinical data are considered, making the set of features objective and independent of expert opinions. The proposed method builds on association rules that are derived from the data. The incidence rate ratio (IRR) is determined for each rule. Given a new patient, the mean IRR of all applicable rules is used to estimate their HIV risk. The method was tested and validated on the publicly available clinical database MIMIC-IV, which consists of around 525,000 hospital stays that included a stay at the intensive care unit or emergency department. We evaluated the method using the area under the receiver operating characteristic curve (AUC). The best performance with an AUC of 0.88 was achieved with a model consisting of 53 rules. A threshold value of 0.66 leads to a sensitivity of 98% and a specificity of 53%. The rules were grouped into drug abuse, psychological illnesses (e.g., PTSD), previously known associations (e.g., pulmonary diseases), and new associations (e.g., certain diagnostic procedures). In conclusion, we propose a novel HIV risk estimation method that builds on existing clinical data. It incorporates a wide range of features, leading to a model that is independent of expert opinions. It supports providers in making informed decisions in the point-of-care diagnostics process by estimating a patient's HIV risk.
format	article
author	Oliver Haas Oliver Haas Andreas Maier Eva Rothgang
author_facet	Oliver Haas Oliver Haas Andreas Maier Eva Rothgang
author_sort	Oliver Haas
title	Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios
title_short	Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios
title_full	Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios
title_fullStr	Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios
title_full_unstemmed	Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios
title_sort	machine learning-based hiv risk estimation using incidence rate ratios
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/1cb6aaa04f004d548bf2faf1f47a3090
work_keys_str_mv	AT oliverhaas machinelearningbasedhivriskestimationusingincidencerateratios AT oliverhaas machinelearningbasedhivriskestimationusingincidencerateratios AT andreasmaier machinelearningbasedhivriskestimationusingincidencerateratios AT evarothgang machinelearningbasedhivriskestimationusingincidencerateratios
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Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios

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