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

Evaluation of artificial intelligence-powered screening for sexually transmitted infections-related skin lesions using clinical images and metadata

BACKGROUND

Sexually transmitted infections (STIs) pose a significant global public health challenge. Early diagnosis and treatment reduce STI transmission, but rely on recognising symptoms and care-seeking behaviour of the individual. Digital health software that distinguishes STI skin conditions could improve health-seeking behaviour. We developed and evaluated a deep learning model to differentiate STIs from non-STIs based on clinical images and symptoms.

METHODS

We used 4913 clinical images of genital lesions and metadata from the Melbourne Sexual Health Centre collected during 2010-2023. We developed two binary classification models to distinguish STIs from non-STIs: (1) a convolutional neural network (CNN) using images only and (2) an integrated model combining both CNN and fully connected neural network (FCN) using images and metadata. We evaluated the model performance by the area under the ROC curve (AUC) and assessed metadata contributions to the Image-only model.

RESULTS

Our study included 1583 STI and 3330 non-STI images. Common STI diagnoses were syphilis (34.6%), genital warts (24.5%) and herpes (19.4%), while most non-STIs (80.3%) were conditions such as dermatitis, lichen sclerosis and balanitis. In both STI and non-STI groups, the most frequently observed groups were 25-34 years (48.6% and 38.2%, respectively) and heterosexual males (60.3% and 45.9%, respectively). The Image-only model showed a reasonable performance with an AUC of 0.859 (SD 0.013). The Image + Metadata model achieved a significantly higher AUC of 0.893 (SD 0.018) compared to the Image-only model (p < 0.01). Out of 21 metadata, the integration of demographic and dermatological metadata led to the most significant improvement in model performance, increasing AUC by 6.7% compared to the baseline Image-only model.

CONCLUSIONS

The Image + Metadata model outperformed the Image-only model in distinguishing STIs from other skin conditions. Using it as a screening tool in a clinical setting may require further development and evaluation with larger datasets.

Using an interactive web application to identify pre-exposure prophylaxis adherence among men who have sex with men

Background

Men who have sex with men (MSM) are at a high risk for HIV infection. While pre-exposure prophylaxis (PrEP) is an effective oral preventive strategy, its success is largely dependent on consistent medication adherence.

Objective

The aim of this study was to develop the machine learning web application and evaluate the performance in predicting PrEP adherence.

Methods

The PrEP prospective cohort study of the MSM population conducted in Western China from 2019 to 2023, and we collected adherence data and personal characteristics data from 747 MSM. Predictor variables were screened and the performance of several machine learning methods in predicting nonadherent behaviors were compared.

Results

A total of 11 candidate variables were screened that predicted nonadherent behaviors. We developed and evaluated five machine learning models that performed well in predicting adherence. Attitudes of male sexual partners, self-efficacy, HIV testing, number of male sexual partners, and risk perception were the most important predictors of adherence. The optimal prediction model was displayed in a shiny web application for online calculation of the probability of occurrence of nonadherent behaviors among MSM.

Conclusions

Machine learning performed well in predicting nonadherent behaviors among MSM. An interactive and intuitive web application can help identify individuals who may have nonadherent behaviors, resulting in improved medication adherence and increased prevention efficacy.

Predicting sexually transmitted infections among men who have sex with men in Zimbabwe using deep learning and ensemble machine learning models

There is a substantial increase in sexually transmitted infections (STIs) among men who have sex with men (MSM) globally. Unprotected sexual practices, multiple sex partners, criminalization, stigmatisation, fear of discrimination, substance use, poor access to care, and lack of early STI screening tools are among the contributing factors. Therefore, this study applied multilayer perceptron (MLP), extremely randomized trees (ExtraTrees) and XGBoost machine learning models to predict STIs among MSM using bio-behavioural survey (BBS) data in Zimbabwe. Data were collected from 1538 MSM in Zimbabwe. The dataset was split into training and testing sets using the ratio of 80% and 20%, respectively. The synthetic minority oversampling technique (SMOTE) was applied to address class imbalance. Using a stepwise logistic regression model, the study revealed several predictors of STIs among MSM such as age, cohabitation with sex partners, education status and employment status. The results show that MLP performed better than STI predictive models (XGBoost and ExtraTrees) and achieved accuracy of 87.54%, recall of 97.29%, precision of 89.64%, F1-Score of 93.31% and AUC of 66.78%. XGBoost also achieved an accuracy of 86.51%, recall of 96.51%, precision of 89.25%, F1-Score of 92.74% and AUC of 54.83%. ExtraTrees recorded an accuracy of 85.47%, recall of 95.35%, precision of 89.13%, F1-Score of 92.13% and AUC of 60.21%. These models can be effectively used to identify highly at-risk MSM, for STI surveillance and to further develop STI infection screening tools to improve health outcomes of MSM.

Data mining and machine learning in HIV infection risk research: An overview and recommendations

In the contemporary era, the applications of data mining and machine learning have permeated extensively into medical research, significantly contributing to areas such as HIV studies. By reviewing 38 articles published in the past 15 years, the study presents a roadmap based on seven different aspects, utilizing various machine learning techniques for both novice researchers and experienced researchers seeking to comprehend the current state of the art in this area. While traditional regression modeling techniques have been commonly used, researchers are increasingly adopting more advanced fully supervised machine learning and deep learning techniques, which often outperform the traditional methods in predictive performance. Additionally, the study identifies nine new open research issues and outlines possible future research plans to enhance the outcomes of HIV infection risk research. This review is expected to be an insightful guide for researchers, illuminating current practices and suggesting advancements in the field.

Clinical features-based machine learning models to separate sexually transmitted infections from other skin diagnoses

INTRODUCTION

Many sexual health services are overwhelmed and cannot cater for all the individuals who present with sexually transmitted infections (STIs). Digital health software that separates STIs from non-STIs could improve the efficiency of clinical services. We developed and evaluated a machine learning model that predicts whether patients have an STI based on their clinical features.

METHODS

We manually extracted 25 demographic features and clinical features from 1315 clinical records in the electronic health record system at Melbourne Sexual Health Center. We examined 16 machine learning models to predict a binary outcome of an STI or a non-STI diagnosis. We evaluated the models' performance with the area under the ROC curve (AUC), accuracy and F1-scores.

RESULTS

Our study included 1315 consultations, of which 36.8% (484/1315) were diagnosed with STIs and 63.2% (831/1315) had non-STI conditions. The study population predominantly consisted of heterosexual men (49.5%, 651/1315), followed by gay, bisexual and other men who have sex with men (GBMSM) (25.7%), women (21.6%) and unknown gender (3.2%). The median age was 31 years (intra-quartile range (IQR) 26-39). The top 5 performing models were CatBoost (AUC 0.912), Random Forest (AUC 0.917), LightGBM (AUC 0.907), Gradient Boosting (AUC 0.905) and XGBoost (AUC 0.900). The best model, CatBoost, achieved an accuracy of 0.837, sensitivity of 0.776, specificity of 0.831, precision of 0.782 and F1-score of 0.778. The key important features were lesion duration, type of skin lesions, age, gender, history of skin disorders, number of lesions, dysuria duration, anorectal pain and itchiness.

CONCLUSIONS

Our best model demonstrates a reasonable performance in distinguishing STIs from non-STIs. However, to be clinically useful, more detailed information such as clinical images, may be required to reach sufficient accuracy.

Lightweight federated learning for STIs/HIV prediction

This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible infections/human immunodeficiency virus (STIs/HIV). The approach utilised Federated Learning (FL) to construct a model from multiple clinics and key stakeholders. FL ensured that only models were shared between clinics, minimising the risk of personal information leakage. Additionally, an algorithm was explored on the FL manager side to construct a global model that aligns with the communication status of the system. Our proposed method introduced Random Forest Federated Learning for assessing the risk of STIs/HIV, incorporating a flexible aggregation process that can be adjusted to accommodate the capacious communication system. Experimental results demonstrated the significant potential of a solution for estimating STIs/HIV risk. In comparison with recent studies, our approach yielded superior results in terms of AUC (0.97) and accuracy ( 93 % ). Despite these promising findings, a limitation of the study lies in the experiment for man's data, due to the self-reported nature of the data and sensitive content. which may be subject to participant bias. Future research could check the performance of the proposed framework in partnership with high-risk populations (e.g., men who have sex with men) to provide a more comprehensive understanding of the proposed framework's impact and ultimately aim to improve health outcomes/health service optimisation.

Identifying Individuals at High Risk for HIV and Sexually Transmitted Infections With an Artificial Intelligence-Based Risk Assessment Tool

Background

We have previously developed an artificial intelligence-based risk assessment tool to identify the individual risk of HIV and sexually transmitted infections (STIs) in a sexual health clinical setting. Based on this tool, this study aims to determine the optimal risk score thresholds to identify individuals at high risk for HIV/STIs.

Methods

Using 2008-2022 data from 216 252 HIV, 227 995 syphilis, 262 599 gonorrhea, and 320 355 chlamydia consultations at a sexual health center, we applied MySTIRisk machine learning models to estimate infection risk scores. Optimal cutoffs for determining high-risk individuals were determined using Youden's index.

Results

The HIV risk score cutoff for high risk was 0.56, with 86.0% sensitivity (95% CI, 82.9%-88.7%) and 65.6% specificity (95% CI, 65.4%-65.8%). Thirty-five percent of participants were classified as high risk, which accounted for 86% of HIV cases. The corresponding cutoffs were 0.49 for syphilis (sensitivity, 77.6%; 95% CI, 76.2%-78.9%; specificity, 78.1%; 95% CI, 77.9%-78.3%), 0.52 for gonorrhea (sensitivity, 78.3%; 95% CI, 77.6%-78.9%; specificity, 71.9%; 95% CI, 71.7%-72.0%), and 0.47 for chlamydia (sensitivity, 68.8%; 95% CI, 68.3%-69.4%; specificity, 63.7%; 95% CI, 63.5%-63.8%). High-risk groups identified using these thresholds accounted for 78% of syphilis, 78% of gonorrhea, and 69% of chlamydia cases. The odds of positivity were significantly higher in the high-risk group than otherwise across all infections: 11.4 (95% CI, 9.3-14.8) times for HIV, 12.3 (95% CI, 11.4-13.3) for syphilis, 9.2 (95% CI, 8.8-9.6) for gonorrhea, and 3.9 (95% CI, 3.8-4.0) for chlamydia.

Conclusions

Risk scores generated by the AI-based risk assessment tool MySTIRisk, together with Youden's index, are effective in determining high-risk subgroups for HIV/STIs. The thresholds can aid targeted HIV/STI screening and prevention.

Building gender-specific sexually transmitted infection risk prediction models using CatBoost algorithm and NHANES data

BACKGROUND AND AIMS

Sexually transmitted infections (STIs) are a significant global public health challenge due to their high incidence rate and potential for severe consequences when early intervention is neglected. Research shows an upward trend in absolute cases and DALY numbers of STIs, with syphilis, chlamydia, trichomoniasis, and genital herpes exhibiting an increasing trend in age-standardized rate (ASR) from 2010 to 2019. Machine learning (ML) presents significant advantages in disease prediction, with several studies exploring its potential for STI prediction. The objective of this study is to build males-based and females-based STI risk prediction models based on the CatBoost algorithm using data from the National Health and Nutrition Examination Survey (NHANES) for training and validation, with sub-group analysis performed on each STI. The female sub-group also includes human papilloma virus (HPV) infection.

METHODS

The study utilized data from the National Health and Nutrition Examination Survey (NHANES) program to build males-based and females-based STI risk prediction models using the CatBoost algorithm. Data was collected from 12,053 participants aged 18 to 59 years old, with general demographic characteristics and sexual behavior questionnaire responses included as features. The Adaptive Synthetic Sampling Approach (ADASYN) algorithm was used to address data imbalance, and 15 machine learning algorithms were evaluated before ultimately selecting the CatBoost algorithm. The SHAP method was employed to enhance interpretability by identifying feature importance in the model's STIs risk prediction.

RESULTS

The CatBoost classifier achieved AUC values of 0.9995, 0.9948, 0.9923, and 0.9996 and 0.9769 for predicting chlamydia, genital herpes, genital warts, gonorrhea, and overall STIs infections among males. The CatBoost classifier achieved AUC values of 0.9971, 0.972, 0.9765, 1, 0.9485 and 0.8819 for predicting chlamydia, genital herpes, genital warts, gonorrhea, HPV and overall STIs infections among females. The characteristics of having sex with new partner/year, times having sex without condom/year, and the number of female vaginal sex partners/lifetime have been identified as the top three significant predictors for the overall risk of male STIs. Similarly, ever having anal sex with a man, age and the number of male vaginal sex partners/lifetime have been identified as the top three significant predictors for the overall risk of female STIs.

CONCLUSIONS

This study demonstrated the effectiveness of the CatBoost classifier in predicting STI risks among both male and female populations. The SHAP algorithm revealed key predictors for each infection, highlighting consistent demographic characteristics and sexual behaviors across different STIs. These insights can guide targeted prevention strategies and interventions to alleviate the impact of STIs on public health.

Comprehensive Overview of Treponema pallidum Outer Membrane Proteins

Treponema pallidum, the causative agent of syphilis, is a sexually transmitted microorganism that exhibits remarkable motility capabilities, allowing it to affect various systems. Despite its structural resemblance to gram-negative bacteria due to its dual-membrane, T. pallidum possesses a lower abundance of outer membrane proteins (OMPs), which enables it to effectively conceal itself. This review presents a comprehensive analysis of the clinical diagnostic potential associated with the OMPs of T. pallidum. Furthermore, the known OMPs in T. pallidum that are responsible for mediating host interactions have been progressively elucidated. This review aims to shed light on the pathogenesis of syphilis, encompassing aspects such as vascular inflammation, chancre self-healing, neuroinvasion, and reinfection. Additionally, this review offers a detailed overview of the current state and prospects of development in the field of syphilis vaccines, with the ultimate goal of establishing a foundation for understanding the pathogenesis and implementing effective prevention strategies against syphilis.

An environment-wide association study for the identification of non-invasive factors for type 2 diabetes mellitus: Analysis based on the Henan Rural Cohort study

AIM

To explore the influencing factors of Type 2 diabetes mellitus (T2DM) in the rural population of Henan Province and evaluate the predictive ability of non-invasive factors to T2DM.

METHODS

A total of 30,020 participants from the Henan Rural Cohort Study in China were included in this study. The dataset was randomly divided into a training set and a testing set with a 50:50 split for validation purposes. We used logistic regression analysis to investigate the association between 56 factors and T2DM in the training set (false discovery rate < 5 %) and significant factors were further validated in the testing set (P < 0.05). Gradient Boosting Machine (GBM) model was used to determine the ability of the non-invasive variables to classify T2DM individuals accurately and the importance ranking of these variables.

RESULTS

The overall population prevalence of T2DM was 9.10 %. After adjusting for age, sex, educational level, marital status, and body measure index (BMI), we identified 13 non-invasive variables and 6 blood biochemical indexes associated with T2DM in the training and testing dataset. The top three factors according to the GBM importance ranking were pulse pressure (PP), urine glucose (UGLU), and waist-to-hip ratio (WHR). The GBM model achieved a receiver operating characteristic (AUC) curve of 0.837 with non-invasive variables and 0.847 for the full model.

CONCLUSIONS

Our findings demonstrate that non-invasive variables that can be easily measured and quickly obtained may be used to predict T2DM risk in rural populations in Henan Province.

Computational methods applied to syphilis: where are we, and where are we going?

Syphilis is an infectious disease that can be diagnosed and treated cheaply. Despite being a curable condition, the syphilis rate is increasing worldwide. In this sense, computational methods can analyze data and assist managers in formulating new public policies for preventing and controlling sexually transmitted infections (STIs). Computational techniques can integrate knowledge from experiences and, through an inference mechanism, apply conditions to a database that seeks to explain data behavior. This systematic review analyzed studies that use computational methods to establish or improve syphilis-related aspects. Our review shows the usefulness of computational tools to promote the overall understanding of syphilis, a global problem, to guide public policy and practice, to target better public health interventions such as surveillance and prevention, health service delivery, and the optimal use of diagnostic tools. The review was conducted according to PRISMA 2020 Statement and used several quality criteria to include studies. The publications chosen to compose this review were gathered from Science Direct, Web of Science, Springer, Scopus, ACM Digital Library, and PubMed databases. Then, studies published between 2015 and 2022 were selected. The review identified 1,991 studies. After applying inclusion, exclusion, and study quality assessment criteria, 26 primary studies were included in the final analysis. The results show different computational approaches, including countless Machine Learning algorithmic models, and three sub-areas of application in the context of syphilis: surveillance (61.54%), diagnosis (34.62%), and health policy evaluation (3.85%). These computational approaches are promising and capable of being tools to support syphilis control and surveillance actions.

A comprehensive review for machine learning based human papillomavirus detection in forensic identification with multiple medical samples

Human papillomavirus (HPV) is a sexually transmitted virus. Cervical cancer is one of the highest incidences of cancer, almost all patients are accompanied by HPV infection. In addition, the occurrence of a variety of cancers is also associated with HPV infection. HPV vaccination has gained widespread popularity in recent years with the increase in public health awareness. In this context, HPV testing not only needs to be sensitive and specific but also needs to trace the source of HPV infection. Through machine learning and deep learning, information from medical examinations can be used more effectively. In this review, we discuss recent advances in HPV testing in combination with machine learning and deep learning.

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

Background

Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care.

Methods

We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data.

Results

Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health.

Conclusions

Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.

Predicting HIV infection in the decade (2005-2015) pre-COVID-19 in Zimbabwe: A supervised classification-based machine learning approach

The burden of HIV and related diseases have been areas of great concern pre and post the emergence of COVID-19 in Zimbabwe. Machine learning models have been used to predict the risk of diseases, including HIV accurately. Therefore, this paper aimed to determine common risk factors of HIV positivity in Zimbabwe between the decade 2005 to 2015. The data were from three two staged population five-yearly surveys conducted between 2005 and 2015. The outcome variable was HIV status. The prediction model was fit by adopting 80% of the data for learning/training and 20% for testing/prediction. Resampling was done using the stratified 5-fold cross-validation procedure repeatedly. Feature selection was done using Lasso regression, and the best combination of selected features was determined using Sequential Forward Floating Selection. We compared six algorithms in both sexes based on the F1 score, which is the harmonic mean of precision and recall. The overall HIV prevalence for the combined dataset was 22.5% and 15.3% for females and males, respectively. The best-performing algorithm to identify individuals with a higher likelihood of HIV infection was XGBoost, with a high F1 score of 91.4% for males and 90.1% for females based on the combined surveys. The results from the prediction model identified six common features associated with HIV, with total number of lifetime sexual partners and cohabitation duration being the most influential variables for females and males, respectively. In addition to other risk reduction techniques, machine learning may aid in identifying those who might require Pre-exposure prophylaxis, particularly women who experience intimate partner violence. Furthermore, compared to traditional statistical approaches, machine learning uncovered patterns in predicting HIV infection with comparatively reduced uncertainty and, therefore, crucial for effective decision-making.

Exploring Current Practice, Knowledge, and Challenges of Sexually Transmitted Infection/HIV Management and Pre-Exposure Prophylaxis Among Japanese Health Care Professionals: A Cross-Sectional Web Survey

We conducted a web-based survey targeting physicians in specialties of treating sexually transmitted infection (STI) and/or human immunodeficiency virus (HIV) patients to understand the current STI/HIV care practices and their acceptability of and barriers to the prescription of pre-exposure prophylaxis (PrEP) in Japan. A descriptive analysis was used to summarize survey responses. Univariate and multivariable logistic regression were performed to identify factors associated with willingness to prescribe PrEP. Of 316 survey respondents, 57 were specialized in HIV, 90 STI/Urology/Proctology, 55 Obstetrics/Gynecology, and 114 General Practice/Internal Medicine/Dermatology. Proportion of HIV-specialized physicians who interview the patients about risk behaviors tended to be higher than other physician groups (84.2% vs. 54.8%, 47.3%, and 50.9%, respectively), and 53 - 75% of non-HIV-specialized physicians reported that they were incapable of making decisions on HIV medications. Higher PrEP knowledge enhanced the willingness to recommend and prescribe PrEP drugs (odds ratio: 2.31, 95% confidence interval: 1.30-4.10, p = 0.0044), and 45.4% physicians with no PrEP knowledge raised the concern of incapability to respond and manage when an individual is infected with HIV. Educational opportunities on management and prevention measures for both STI and HIV may encourage non-HIV-specialized physicians to be involved in HIV care and to enhance initiation of HIV tests and adoption of PrEP.

Predicting the HIV/AIDS Knowledge among the Adolescent and Young Adult Population in Peru: Application of Quasi-Binomial Logistic Regression and Machine Learning Algorithms

Inadequate knowledge is one of the principal obstacles for preventing HIV/AIDS spread. Worldwide, it is reported that adolescents and young people have a higher vulnerability of being infected. Thus, the need to understand youths' knowledge towards HIV/AIDS becomes crucial. This study aimed to identify the determinants and develop a predictive model to estimate HIV/AIDS knowledge among this target population in Peru. Data from the 2019 DHS Survey were used. The software RStudio and RapidMiner were used for quasi-binomial logistic regression and computational model building, respectively. Five classification algorithms were considered for model development and their performance was assessed using accuracy, sensitivity, specificity, FPR, FNR, Cohen's kappa, F1 score and AUC. The results revealed an association between 14 socio-demographic, economic and health factors and HIV/AIDS knowledge. The accuracy levels were estimated between 59.47 and 64.30%, with the random forest model showing the best performance (64.30%). Additionally, the best classifier showed that the gender of the respondent, area of residence, wealth index, region of residence, interviewee's age, highest educational level, ethnic self-perception, having heard about HIV/AIDS in the past, the performance of an HIV/AIDS screening test and mass media access have a major influence on HIV/AIDS knowledge prediction. The results suggest the usefulness of the associations found and the random forest model as a predictor of knowledge of HIV/AIDS and may aid policy makers to guide and reinforce the planning and implementation of healthcare strategies.

Predicting Risky Sexual Behavior Among College Students Through Machine Learning Approaches: Cross-sectional Analysis of Individual Data From 1264 Universities in 31 Provinces in China

BACKGROUND

Risky sexual behavior (RSB), the most direct risk factor for sexually transmitted infections (STIs), is common among college students. Thus, identifying relevant risk factors and predicting RSB are important to intervene and prevent RSB among college students.

OBJECTIVE

We aim to establish a predictive model for RSB among college students to facilitate timely intervention and the prevention of RSB to help limit STI contraction.

METHODS

We included a total of 8794 heterosexual Chinese students who self-reported engaging in sexual intercourse from November 2019 to February 2020. We identified RSB among those students and attributed it to 4 dimensions: whether contraception was used, whether the contraceptive method was safe, whether students engaged in casual sex or sex with multiple partners, and integrated RSB (which combined the first 3 dimensions). Overall, 126 predictors were included in this study, including demographic characteristics, daily habits, physical and mental health, relationship status, sexual knowledge, sexual education, sexual attitude, and previous sexual experience. For each type of RSB, we compared 8 machine learning (ML) models: multiple logistic regression (MLR), naive Bayes (BYS), linear discriminant analysis (LDA), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), deep learning (DL), and the ensemble model. The optimal model for both RSB prediction and risk factor identification was selected based on a set of validation indicators. An MLR model was applied to investigate the association between RSB and identified risk factors through ML methods.

RESULTS

In total, 5328 (60.59%) students were found to have previously engaged in RSB. Among them, 3682 (41.87%) did not use contraception every time they had sexual intercourse, 3602 (40.96%) had previously used an ineffective or unsafe contraceptive method, and 1157 (13.16%) had engaged in casual sex or sex with multiple partners. XGBoost achieved the optimal predictive performance on all 4 types of RSB, with the area under the receiver operator characteristic curve (AUROC) reaching 0.78, 0.72, 0.94, and 0.80 for contraceptive use, safe contraceptive method use, engagement in casual sex or with multiple partners, and integrated RSB, respectively. By ensuring the stability of various validation indicators, the 12 most predictive variables were then selected using XGBoost, including the participants' relationship status, sexual knowledge, sexual attitude, and previous sexual experience. Through MLR, RSB was found to be significantly associated with less sexual knowledge, more liberal sexual attitudes, single relationship status, and increased sexual experience.

CONCLUSIONS

RSB is prevalent among college students. The XGBoost model is an effective approach to predict RSB and identify corresponding risk factors. This study presented an opportunity to promote sexual and reproductive health through ML models, which can help targeted interventions aimed at different subgroups and the precise surveillance and prevention of RSB among college students through risk probability prediction.

Predicting HIV Status among Men Who Have Sex with Men in Bulawayo & Harare, Zimbabwe Using Bio-Behavioural Data, Recurrent Neural Networks, and Machine Learning Techniques

HIV and AIDS continue to be major public health concerns globally. Despite significant progress in addressing their impact on the general population and achieving epidemic control, there is a need to improve HIV testing, particularly among men who have sex with men (MSM). This study applied deep and machine learning algorithms such as recurrent neural networks (RNNs), the bagging classifier, gradient boosting classifier, support vector machines, and Naïve Bayes classifier to predict HIV status among MSM using the dataset from the Zimbabwe Ministry of Health and Child Care. RNNs performed better than the bagging classifier, gradient boosting classifier, support vector machines, and Gaussian Naïve Bayes classifier in predicting HIV status. RNNs recorded a high prediction accuracy of 0.98 as compared to the Gaussian Naïve Bayes classifier (0.84), bagging classifier (0.91), support vector machine (0.91), and gradient boosting classifier (0.91). In addition, RNNs achieved a high precision of 0.98 for predicting both HIV-positive and -negative cases, a recall of 1.00 for HIV-negative cases and 0.94 for HIV-positive cases, and an F1-score of 0.99 for HIV-negative cases and 0.96 for positive cases. HIV status prediction models can significantly improve early HIV screening and assist healthcare professionals in effectively providing healthcare services to the MSM community. The results show that integrating HIV status prediction models into clinical software systems can complement indicator condition-guided HIV testing strategies and identify individuals that may require healthcare services, particularly for hard-to-reach vulnerable populations like MSM. Future studies are necessary to optimize machine learning models further to integrate them into primary care. The significance of this manuscript is that it presents results from a study population where very little information is available in Zimbabwe due to the criminalization of MSM activities in the country. For this reason, MSM tends to be a hidden sector of the population, frequently harassed and arrested. In almost all communities in Zimbabwe, MSM issues have remained taboo, and stigma exists in all sectors of society.

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

Background

Continuously 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.

Methods

We 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.

Results

A 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).

Conclusion

Machine 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.

Web-Based Risk Prediction Tool for an Individual's Risk of HIV and Sexually Transmitted Infections Using Machine Learning Algorithms: Development and External Validation Study

Background

HIV and sexually transmitted infections (STIs) are major global public health concerns. Over 1 million curable STIs occur every day among people aged 15 years to 49 years worldwide. Insufficient testing or screening substantially impedes the elimination of HIV and STI transmission.

Objective

The aim of our study was to develop an HIV and STI risk prediction tool using machine learning algorithms.

Methods

We used clinic consultations that tested for HIV and STIs at the Melbourne Sexual Health Centre between March 2, 2015, and December 31, 2018, as the development data set (training and testing data set). We also used 2 external validation data sets, including data from 2019 as external “validation data 1” and data from January 2020 and January 2021 as external “validation data 2.” We developed 34 machine learning models to assess the risk of acquiring HIV, syphilis, gonorrhea, and chlamydia. We created an online tool to generate an individual’s risk of HIV or an STI.

Results

The important predictors for HIV and STI risk were gender, age, men who reported having sex with men, number of casual sexual partners, and condom use. Our machine learning–based risk prediction tool, named MySTIRisk, performed at an acceptable or excellent level on testing data sets (area under the curve [AUC] for HIV=0.78; AUC for syphilis=0.84; AUC for gonorrhea=0.78; AUC for chlamydia=0.70) and had stable performance on both external validation data from 2019 (AUC for HIV=0.79; AUC for syphilis=0.85; AUC for gonorrhea=0.81; AUC for chlamydia=0.69) and data from 2020-2021 (AUC for HIV=0.71; AUC for syphilis=0.84; AUC for gonorrhea=0.79; AUC for chlamydia=0.69).

Conclusions

Our web-based risk prediction tool could accurately predict the risk of HIV and STIs for clinic attendees using simple self-reported questions. MySTIRisk could serve as an HIV and STI screening tool on clinic websites or digital health platforms to encourage individuals at risk of HIV or an STI to be tested or start HIV pre-exposure prophylaxis. The public can use this tool to assess their risk and then decide if they would attend a clinic for testing. Clinicians or public health workers can use this tool to identify high-risk individuals for further interventions.

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

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.

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

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.

Delayed diagnosis of human immunodeficiency virus infection in people diagnosed with syphilis: A nationwide cohort study from 2011 to 2018 in Japan

Early treatment of HIV infection depends on timely diagnosis, but many persons living with HIV/AIDS (PLWHA) are unaware of their infection. Though many patients seeking medical attention for sexually transmitted diseases have HIV, many patients' HIV co-infection is undiagnosed in Japan. This is the first report to analyze the timing of syphilis infection in PLWHA of all ages through the use of the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB), containing clinical data of the largest group of HIV-positive patients available in Japan. Overall, 1521 PLWHA (male 93.2%) newly diagnosed and started on antiretroviral therapy were identified in 2016, and 646 (42.5%) patients had a diagnosis of syphilis between 2011 and 2018. Although 100 patients were diagnosed with syphilis before their HIV diagnosis, only 17 (17.0%) had been tested for HIV. Over 50 patients per year became infected with syphilis even after their HIV diagnosis (2017, n = 65 (4.3%); 2018, n = 58 (3.8%)). Although early diagnosis of HIV infection is important, most syphilis patients in Japan had not been properly tested for HIV infection. Since a certain number of HIV patients developed syphilis after HIV diagnosis, education for newly diagnosed HIV patients is important.