Predicting the diagnosis of HIV and sexually transmitted infections among men who have sex with men using machine learning approaches

Bao, Yining; Medland, Nicholas; Fairley, Christopher K; Wu, Jinrong; Shang, Xianwen; Chow, Eric P F; Xu, Xianglong; Ge, Zongyuan; Zhuang, Xun; Zhang, Lei

doi:10.1016/j.jinf.2020.11.007

Cited by 45 publications

(45 citation statements)

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“…We extracted routinely collected data from the EHR, including self-reported questions at the first visit for each visit interval (described below). The feature selection was informed by the literature review, expert opinion, and previous work [ 25 ]. This baseline predictor data for modelling included gender, age (≥18 years old), country of birth, sexual practices (e.g., had sex with a sex partner in the last 12 months, number of sex partners in the last 12 months), condom use with sex partners in the last 12 months, pre-exposure prophylaxis (PrEP) use, presenting with STI symptoms, living with HIV (for STI prediction), and reported sexual contact with partners with an STI (gonorrhoea, chlamydia, or syphilis) (summarised in Table 1 and Table S2 ).…”

Section: Methodsmentioning

confidence: 99%

A Machine-Learning-Based Risk-Prediction Tool for HIV and Sexually Transmitted Infections Acquisition over the Next 12 Months

Chow

et al. 2022

JCM

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Background: More than one million people acquire sexually transmitted infections (STIs) every day globally. It is possible that predicting an individual’s future risk of HIV/STIs could contribute to behaviour change or improve testing. We developed a series of machine learning models and a subsequent risk-prediction tool for predicting the risk of HIV/STIs over the next 12 months. Methods: Our data included individuals who were re-tested at the clinic for HIV (65,043 consultations), syphilis (56,889 consultations), gonorrhoea (60,598 consultations), and chlamydia (63,529 consultations) after initial consultations at the largest public sexual health centre in Melbourne from 2 March 2015 to 31 December 2019. We used the receiver operating characteristic (AUC) curve to evaluate the model’s performance. The HIV/STI risk-prediction tool was delivered via a web application. Results: Our risk-prediction tool had an acceptable performance on the testing datasets for predicting HIV (AUC = 0.72), syphilis (AUC = 0.75), gonorrhoea (AUC = 0.73), and chlamydia (AUC = 0.67) acquisition. Conclusions: Using machine learning techniques, our risk-prediction tool has acceptable reliability in predicting HIV/STI acquisition over the next 12 months. This tool may be used on clinic websites or digital health platforms to form part of an intervention tool to increase testing or reduce future HIV/STI risk.

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Section: Methodsmentioning

confidence: 99%

A Machine-Learning-Based Risk-Prediction Tool for HIV and Sexually Transmitted Infections Acquisition over the Next 12 Months

Chow

et al. 2022

JCM

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“…In Denmark, Ahlstrom et al ( 36 ) applied various machine learning methods in electronic registry data to predict HIV status and found that the RF algorithm also performed slightly better (AUC = 0.89). More recently, Bao et al ( 37 ) developed four machine learning models and evaluated their performance in predicting HIV diagnosis based on a cohort of MSM in Australia, and he proposed that Machine learning approaches outperformed the multivariable logistic regression model, with the gradient boosting machine achieving the highest performance (AUC = 0.76). Our study complements these machine learning studies applied to HIV infection prediction, all of which effectively illustrate that machine learning can be used as an effective method for detecting HIV infection among MSM.…”

Section: Discussionmentioning

confidence: 99%

Application of machine learning algorithms in predicting HIV infection among men who have sex with men: Model development and validation

Jiang

et al. 2022

Front. Public Health

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BackgroundContinuously growing of HIV incidence among men who have sex with men (MSM), as well as the low rate of HIV testing of MSM in China, demonstrates a need for innovative strategies to improve the implementation of HIV prevention. The use of machine learning algorithms is an increasing tendency in disease diagnosis prediction. We aimed to develop and validate machine learning models in predicting HIV infection among MSM that can identify individuals at increased risk of HIV acquisition for transmission-reduction interventions.MethodsWe extracted data from MSM sentinel surveillance in Zhejiang province from 2018 to 2020. Univariate logistic regression was used to select significant variables in 2018–2019 data (P < 0.05). After data processing and feature selection, we divided the model development data into two groups by stratified random sampling: training data (70%) and testing data (30%). The Synthetic Minority Oversampling Technique (SMOTE) was applied to solve the problem of unbalanced data. The evaluation metrics of model performance were comprised of accuracy, precision, recall, F-measure, and the area under the receiver operating characteristic curve (AUC). Then, we explored three commonly-used machine learning algorithms to compare with logistic regression (LR), including decision tree (DT), support vector machines (SVM), and random forest (RF). Finally, the four models were validated prospectively with 2020 data from Zhejiang province.ResultsA total of 6,346 MSM were included in model development data, 372 of whom were diagnosed with HIV. In feature selection, 12 variables were selected as model predicting indicators. Compared with LR, the algorithms of DT, SVM, and RF improved the classification prediction performance in SMOTE-processed data, with the AUC of 0.778, 0.856, 0.887, and 0.942, respectively. RF was the best-performing algorithm (accuracy = 0.871, precision = 0.960, recall = 0.775, F-measure = 0.858, and AUC = 0.942). And the RF model still performed well on prospective validation (AUC = 0.846).ConclusionMachine learning models are substantially better than conventional LR model and RF should be considered in prediction tools of HIV infection in Chinese MSM. Further studies are needed to optimize and promote these algorithms and evaluate their impact on HIV prevention of MSM.

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“…We used the CV and grid search technique to evaluate and optimise the model hyperparameters to avoid overfitting and improve generalizability. The model and hyperparameters with the largest AUC in internal tenfold cross-validation and grid search were selected as the training outer-loop model and tested on the threefold outer-loop test fold 38 , 39 .…”

Section: Methodsmentioning

confidence: 99%

Using machine learning approaches to predict timely clinic attendance and the uptake of HIV/STI testing post clinic reminder messages

Fairley

Chow

et al. 2022

Sci Rep

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Timely and regular testing for HIV and sexually transmitted infections (STI) is important for controlling HIV and STI (HIV/STI) among men who have sex with men (MSM). We established multiple machine learning models (e.g., logistic regression, lasso regression, ridge regression, elastic net regression, support vector machine, k-nearest neighbour, naïve bayes, random forest, gradient boosting machine, XGBoost, and multi-layer perceptron) to predict timely (i.e., within 30 days) clinic attendance and HIV/STI testing uptake after receiving a reminder message via short message service (SMS) or email). Our study used 3044 clinic consultations among MSM within 12 months after receiving an email or SMS reminder at the Melbourne Sexual Health Centre between April 11, 2019, and April 30, 2020. About 29.5% [899/3044] were timely clinic attendance post reminder messages, and 84.6% [761/899] had HIV/STI testing. The XGBoost model performed best in predicting timely clinic attendance [mean [SD] AUC 62.8% (3.2%); F1 score 70.8% (1.2%)]. The elastic net regression model performed best in predicting HIV/STI testing within 30 days [AUC 82.7% (6.3%); F1 score 85.3% (1.8%)]. The machine learning approach is helpful in predicting timely clinic attendance and HIV/STI re-testing. Our predictive models could be incorporated into clinic websites to inform sexual health care or follow-up service.

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Predicting the diagnosis of HIV and sexually transmitted infections among men who have sex with men using machine learning approaches

Cited by 45 publications

References 50 publications

A Machine-Learning-Based Risk-Prediction Tool for HIV and Sexually Transmitted Infections Acquisition over the Next 12 Months

A Machine-Learning-Based Risk-Prediction Tool for HIV and Sexually Transmitted Infections Acquisition over the Next 12 Months

Application of machine learning algorithms in predicting HIV infection among men who have sex with men: Model development and validation

Using machine learning approaches to predict timely clinic attendance and the uptake of HIV/STI testing post clinic reminder messages

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