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Cocoros NM, Eberhardt K, Nguyen VT, Brown CM, DeMaria A, Madoff LC, Randall LM, Klompas M. Electronic Health Record-Based Algorithm for Monitoring Respiratory Virus-Like Illness. Emerg Infect Dis 2024; 30:1096-1103. [PMID: 38781684 PMCID: PMC11138993 DOI: 10.3201/eid3006.230473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024] Open
Abstract
Viral respiratory illness surveillance has traditionally focused on single pathogens (e.g., influenza) and required fever to identify influenza-like illness (ILI). We developed an automated system applying both laboratory test and syndrome criteria to electronic health records from 3 practice groups in Massachusetts, USA, to monitor trends in respiratory viral-like illness (RAVIOLI) across multiple pathogens. We identified RAVIOLI syndrome using diagnosis codes associated with respiratory viral testing or positive respiratory viral assays or fever. After retrospectively applying RAVIOLI criteria to electronic health records, we observed annual winter peaks during 2015-2019, predominantly caused by influenza, followed by cyclic peaks corresponding to SARS-CoV-2 surges during 2020-2024, spikes in RSV in mid-2021 and late 2022, and recrudescent influenza in late 2022 and 2023. RAVIOLI rates were higher and fluctuations more pronounced compared with traditional ILI surveillance. RAVIOLI broadens the scope, granularity, sensitivity, and specificity of respiratory viral illness surveillance compared with traditional ILI surveillance.
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Soe NN, Latt PM, Yu Z, Lee D, Kim CM, Tran D, Ong JJ, Ge Z, Fairley CK, Zhang L. Clinical features-based machine learning models to separate sexually transmitted infections from other skin diagnoses. J Infect 2024; 88:106128. [PMID: 38452934 DOI: 10.1016/j.jinf.2024.106128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/22/2024] [Accepted: 02/13/2024] [Indexed: 03/09/2024]
Abstract
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.
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Affiliation(s)
- Nyi Nyi Soe
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia; Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Phyu Mon Latt
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia; Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Zhen Yu
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia; Monash e-Research Centre, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Centre, Monash University, Melbourne, Australia
| | - David Lee
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia
| | - Cham-Mill Kim
- Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Australia
| | - Daniel Tran
- Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Australia
| | - Jason J Ong
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia; Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Zongyuan Ge
- Monash e-Research Centre, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Centre, Monash University, Melbourne, Australia
| | - Christopher K Fairley
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia; Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Lei Zhang
- Clinical Medical Research Center, Children's Hospital of Nanjing Medical University, Nanjing, China; Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia; Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
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Albuquerque G, Fernandes F, Barbalho IMP, Barros DMS, Morais PSG, Morais AHF, Santos MM, Galvão-Lima LJ, Sales-Moioli AIL, Santos JPQ, Gil P, Henriques J, Teixeira C, Lima TS, Coutinho KD, Pinto TKB, Valentim RAM. Computational methods applied to syphilis: where are we, and where are we going? Front Public Health 2023; 11:1201725. [PMID: 37680278 PMCID: PMC10481400 DOI: 10.3389/fpubh.2023.1201725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 08/07/2023] [Indexed: 09/09/2023] Open
Abstract
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.
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Affiliation(s)
- Gabriela Albuquerque
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Felipe Fernandes
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Ingridy M. P. Barbalho
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Daniele M. S. Barros
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Philippi S. G. Morais
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Antônio H. F. Morais
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Marquiony M. Santos
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Leonardo J. Galvão-Lima
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Ana Isabela L. Sales-Moioli
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - João Paulo Q. Santos
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Paulo Gil
- Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, Coimbra, Portugal
| | - Jorge Henriques
- Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, Coimbra, Portugal
| | - César Teixeira
- Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, Coimbra, Portugal
| | - Thaisa Santos Lima
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
- Ministry of Health, Esplanada dos Ministérios, Brasília, Brazil
| | - Karilany D. Coutinho
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Talita K. B. Pinto
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Ricardo A. M. Valentim
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
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Yao H, Zhang X. A comprehensive review for machine learning based human papillomavirus detection in forensic identification with multiple medical samples. Front Microbiol 2023; 14:1232295. [PMID: 37529327 PMCID: PMC10387549 DOI: 10.3389/fmicb.2023.1232295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 06/30/2023] [Indexed: 08/03/2023] Open
Abstract
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.
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Affiliation(s)
- Huanchun Yao
- Department of Cancer, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xinglong Zhang
- Department of Hematology, The Fourth Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
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Xu X, Chow EPF, Fairley CK, Chen M, Aguirre I, Goller J, Hocking J, Carvalho N, Zhang L, Ong JJ. Determinants and prediction of Chlamydia trachomatis re-testing and re-infection within 1 year among heterosexuals with chlamydia attending a sexual health clinic. Front Public Health 2023; 10:1031372. [PMID: 36711362 PMCID: PMC9880158 DOI: 10.3389/fpubh.2022.1031372] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 12/23/2022] [Indexed: 01/14/2023] Open
Abstract
Background Chlamydia trachomatis (chlamydia) is one of the most common sexually transmitted infections (STI) globally, and re-infections are common. Current Australian guidelines recommend re-testing for chlamydia 3 months after treatment to identify possible re-infection. Patient-delivered partner therapy (PDPT) has been proposed to control chlamydia re-infection among heterosexuals. We aimed to identify determinants and the prediction of chlamydia re-testing and re-infection within 1 year among heterosexuals with chlamydia to identify potential PDPT candidates. Methods Our baseline data included 5,806 heterosexuals with chlamydia aged ≥18 years and 2,070 re-tested for chlamydia within 1 year of their chlamydia diagnosis at the Melbourne Sexual Health Center from January 2, 2015, to May 15, 2020. We used routinely collected electronic health record (EHR) variables and machine-learning models to predict chlamydia re-testing and re-infection events. We also used logistic regression to investigate factors associated with chlamydia re-testing and re-infection. Results About 2,070 (36%) of 5,806 heterosexuals with chlamydia were re-tested for chlamydia within 1 year. Among those retested, 307 (15%) were re-infected. Multivariable logistic regression analysis showed that older age (≥35 years old), female, living with HIV, being a current sex worker, patient-delivered partner therapy users, and higher numbers of sex partners were associated with an increased chlamydia re-testing within 1 year. Multivariable logistic regression analysis also showed that younger age (18-24 years), male gender, and living with HIV were associated with an increased chlamydia re-infection within 1 year. The XGBoost model was the best model for predicting chlamydia re-testing and re-infection within 1 year among heterosexuals with chlamydia; however, machine learning approaches and these self-reported answers from clients did not provide a good predictive value (AUC < 60.0%). Conclusion The low rate of chlamydia re-testing and high rate of chlamydia re-infection among heterosexuals with chlamydia highlights the need for further interventions. Better targeting of individuals more likely to be re-infected is needed to optimize the provision of PDPT and encourage the test of re-infection at 3 months.
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Affiliation(s)
- Xianglong Xu
- Department of Epidemiology and Health Statistics, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China,Melbourne Sexual Health Centre, The Alfred, Melbourne, VIC, Australia,Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Eric P. F. Chow
- Melbourne Sexual Health Centre, The Alfred, Melbourne, VIC, Australia,Central Clinical School, Monash University, Melbourne, VIC, Australia,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Christopher K. Fairley
- Melbourne Sexual Health Centre, The Alfred, Melbourne, VIC, Australia,Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Marcus Chen
- Melbourne Sexual Health Centre, The Alfred, Melbourne, VIC, Australia,Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Ivette Aguirre
- Melbourne Sexual Health Centre, The Alfred, Melbourne, VIC, Australia
| | - Jane Goller
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Jane Hocking
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Natalie Carvalho
- Centre for Health Policy, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Lei Zhang
- Melbourne Sexual Health Centre, The Alfred, Melbourne, VIC, Australia,Central Clinical School, Monash University, Melbourne, VIC, Australia,China Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, Shaanxi, China,*Correspondence: Lei Zhang ✉
| | - Jason J. Ong
- Melbourne Sexual Health Centre, The Alfred, Melbourne, VIC, Australia,Central Clinical School, Monash University, Melbourne, VIC, Australia,Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom,Jason J. Ong ✉
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Xu X, Yu Z, Ge Z, Chow EPF, Bao Y, Ong JJ, Li W, Wu J, Fairley CK, Zhang L. 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. J Med Internet Res 2022; 24:e37850. [PMID: 36006685 PMCID: PMC9459839 DOI: 10.2196/37850] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 04/13/2022] [Accepted: 07/28/2022] [Indexed: 12/05/2022] Open
Abstract
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.
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Affiliation(s)
- Xianglong Xu
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.,Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.,China Australia Joint Research Center for Infectious Diseases, Xi'an Jiaotong University Health Science Centre, Xi'an, China
| | - Zhen Yu
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.,China Australia Joint Research Center for Infectious Diseases, Xi'an Jiaotong University Health Science Centre, Xi'an, China.,Monash e-Research Centre, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Centre, Monash University, Melbourne, Australia
| | - Zongyuan Ge
- Monash e-Research Centre, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Centre, Monash University, Melbourne, Australia
| | - Eric P F Chow
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.,Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Yining Bao
- China Australia Joint Research Center for Infectious Diseases, Xi'an Jiaotong University Health Science Centre, Xi'an, China
| | - Jason J Ong
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.,Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.,China Australia Joint Research Center for Infectious Diseases, Xi'an Jiaotong University Health Science Centre, Xi'an, China
| | - Wei Li
- School of Public Health, Southeast University, Nanjing, China
| | - Jinrong Wu
- Research Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia
| | - Christopher K Fairley
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.,Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.,China Australia Joint Research Center for Infectious Diseases, Xi'an Jiaotong University Health Science Centre, Xi'an, China
| | - Lei Zhang
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.,Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.,China Australia Joint Research Center for Infectious Diseases, Xi'an Jiaotong University Health Science Centre, Xi'an, China
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