Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios

dc.contributor.authorHaas, Oliver
dc.contributor.authorMaier, Andreas
dc.contributor.authorRothgang, Eva
dc.date.accessioned2022-10-21
dc.date.available2023-10-16T20:00:33Z
dc.date.created2022
dc.date.issued2022-10-21
dc.description.abstractHIV/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.en
dc.identifier.citationFrontiers in Reproductive Health3 (2021): 756405. <https://www.frontiersin.org/articles/10.3389/frph.2021.756405/full>
dc.identifier.doihttps://doi.org/10.3389/frph.2021.756405
dc.identifier.issn2673-3153
dc.identifier.opus-id20579
dc.identifier.urihttps://open.fau.de/handle/openfau/20579
dc.identifier.urnurn:nbn:de:bvb:29-opus4-205794
dc.language.isoen
dc.publisherFrontiers Media S.A.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.de
dc.subjectHIV
dc.subjectrisk estimation
dc.subjectassociation rules
dc.subjectbias
dc.subjectclinical data
dc.subjectmachine learning
dc.subjectartificial intelligence
dc.subjectincidence rate ratio
dc.subject.ddcDDC Classification::6 Technik, Medizin, angewandte Wissenschaften :: 62 Ingenieurwissenschaften :: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
dc.titleMachine Learning-Based HIV Risk Estimation Using Incidence Rate Ratiosen
dc.typearticle
dcterms.publisherFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
local.date.prevpublished2021-12-02
local.document.articlenumber756405
local.journal.titleFrontiers in Reproductive Health
local.journal.volume3
local.sendToDnbfree*
local.subject.fakultaetTechnische Fakultät
local.subject.importimport
local.subject.sammlungUniversität Erlangen-Nürnberg / Eingespielte Open Access Artikel / Eingespielte Open Access Artikel 2022
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