1
|
Bohm BC, Borges FEDM, Silva SCM, Soares AT, Ferreira DD, Belo VS, Lignon JS, Bruhn FRP. Utilization of machine learning for dengue case screening. BMC Public Health 2024; 24:1573. [PMID: 38862945 PMCID: PMC11167742 DOI: 10.1186/s12889-024-19083-8] [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: 11/28/2023] [Accepted: 06/07/2024] [Indexed: 06/13/2024] Open
Abstract
Dengue causes approximately 10.000 deaths and 100 million symptomatic infections annually worldwide, making it a significant public health concern. To address this, artificial intelligence tools like machine learning can play a crucial role in developing more effective strategies for control, diagnosis, and treatment. This study identifies relevant variables for the screening of dengue cases through machine learning models and evaluates the accuracy of the models. Data from reported dengue cases in the states of Rio de Janeiro and Minas Gerais for the years 2016 and 2019 were obtained through the National Notifiable Diseases Surveillance System (SINAN). The mutual information technique was used to assess which variables were most related to laboratory-confirmed dengue cases. Next, a random selection of 10,000 confirmed cases and 10,000 discarded cases was performed, and the dataset was divided into training (70%) and testing (30%). Machine learning models were then tested to classify the cases. It was found that the logistic regression model with 10 variables (gender, age, fever, myalgia, headache, vomiting, nausea, back pain, rash, retro-orbital pain) and the Decision Tree and Multilayer Perceptron (MLP) models achieved the best results in decision metrics, with an accuracy of 98%. Therefore, a tree-based model would be suitable for building an application and implementing it on smartphones. This resource would be available to healthcare professionals such as doctors and nurses.
Collapse
Affiliation(s)
- Bianca Conrad Bohm
- Laboratory of Veterinary Epidemiology, Postgraduate Program in Veterinary, Federal University of Pelotas (UFPel), Capão do Leão, RS, Brazil.
| | | | - Suellen Caroline Matos Silva
- Laboratory of Veterinary Epidemiology, Postgraduate Program in Veterinary, Federal University of Pelotas (UFPel), Capão do Leão, RS, Brazil
| | - Alessandra Talaska Soares
- Laboratory of Veterinary Epidemiology, Graduate Program in Microbiology and Parasitology, Federal University of Pelotas, Capão do Leão, Rio Grande do Sul, Brazil
| | | | - Vinícius Silva Belo
- Federal University of São, João del-Rei, Midwest Dona Lindu campus, Divinópolis, Minas Gerais, Brazil
| | - Julia Somavilla Lignon
- Laboratory of Veterinary Epidemiology, Postgraduate Program in Veterinary, Federal University of Pelotas (UFPel), Capão do Leão, RS, Brazil
| | - Fábio Raphael Pascoti Bruhn
- Laboratory of Veterinary Epidemiology, Preventive Veterinary Department, Federal University of Pelotas,, Capão do Leão, Rio Grande do Sul, Brazil
| |
Collapse
|
2
|
Hernández Bautista PF, Cabrera Gaytán DA, Santacruz Tinoco CE, Vallejos Parás A, Alvarado Yaah JE, Martínez Miguel B, Anguiano Hernández YM, Arriaga Nieto L, Moctezuma Paz A, Jaimes Betancourt L, Pérez Andrade Y, Orozco OC, Valle Alvarado G, Rivera Mahey MG. Retrospective Analysis of Severe Dengue by Dengue Virus Serotypes in a Population with Social Security, Mexico 2023. Viruses 2024; 16:769. [PMID: 38793650 PMCID: PMC11125731 DOI: 10.3390/v16050769] [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: 03/13/2024] [Revised: 05/06/2024] [Accepted: 05/11/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Risk factors for severe dengue manifestations have been attributed to various factors, including specific serotypes, sex, and age. Mexico has seen the re-emergence of DENV-3, which has not circulated in a decade. OBJECTIVE To describe dengue serotypes by age, sex, and their association with disease severity in dengue-positive serum samples from epidemiological surveillance system units. MATERIALS AND METHODS A descriptive analysis was conducted to evaluate the frequency of dengue severity by sex, age, disease quarter, geographical location, and dengue virus serotypes. The study was conducted using laboratory samples from confirmed dengue cases through RT-qPCR from the epidemiological surveillance laboratory network of the Mexican Social Security Institute, Mexico. Simple frequencies and proportions were calculated using the z-test for proportional differences between groups. Bivariate analysis with adjusted Chi2 was performed, and binary logistic regression models were constructed using the forward Wald method considering the model's predictive capacity. The measure of association was the odds ratio, with 95% confidence intervals. Statistical significance was set to an alpha level of <0.05. RESULTS In 2023, 10,441 samples were processed for dengue RT-qPCR at the IMSS, with a predominance of serotype DENV-3 (64.4%). The samples were mostly from women (52.0%) and outpatient cases (63.3%). The distribution of dengue severity showed significant variations by age, with a lower proportion of severe cases in young children and a higher proportion in the 5- to 14-year-old group. Hospitalizations increased significantly with severity. Warm regions had more cases overall and severity. Cases were most frequent from July to September. While DENV-2 was associated with severity, DENV-4 was not. Binary regression identified higher risk in women, age extremes, and DENV-2, with an overall predictive model of 58.5%. CONCLUSIONS Women, age groups at the extremes of life, and the DENV-2 serotype presented severe risk of dengue in a population with social security in Mexico during 2023.
Collapse
Affiliation(s)
- Porfirio Felipe Hernández Bautista
- Coordinación de Calidad de Insumos y Laboratorios Especializados, Instituto Mexicano del Seguro Social, Ciudad de México 07760, Mexico; (P.F.H.B.); (C.E.S.T.); (J.E.A.Y.); (B.M.M.); (Y.M.A.H.)
| | - David Alejandro Cabrera Gaytán
- Coordinación de Calidad de Insumos y Laboratorios Especializados, Instituto Mexicano del Seguro Social, Ciudad de México 07760, Mexico; (P.F.H.B.); (C.E.S.T.); (J.E.A.Y.); (B.M.M.); (Y.M.A.H.)
| | - Clara Esperanza Santacruz Tinoco
- Coordinación de Calidad de Insumos y Laboratorios Especializados, Instituto Mexicano del Seguro Social, Ciudad de México 07760, Mexico; (P.F.H.B.); (C.E.S.T.); (J.E.A.Y.); (B.M.M.); (Y.M.A.H.)
| | - Alfonso Vallejos Parás
- Coordinación de Vigilancia Epidemiológica, Instituto Mexicano del Seguro Social, Ciudad de México 03100, Mexico; (L.A.N.); (Y.P.A.); (O.C.O.); (G.V.A.); (M.G.R.M.)
| | - Julio Elias Alvarado Yaah
- Coordinación de Calidad de Insumos y Laboratorios Especializados, Instituto Mexicano del Seguro Social, Ciudad de México 07760, Mexico; (P.F.H.B.); (C.E.S.T.); (J.E.A.Y.); (B.M.M.); (Y.M.A.H.)
| | - Bernardo Martínez Miguel
- Coordinación de Calidad de Insumos y Laboratorios Especializados, Instituto Mexicano del Seguro Social, Ciudad de México 07760, Mexico; (P.F.H.B.); (C.E.S.T.); (J.E.A.Y.); (B.M.M.); (Y.M.A.H.)
| | - Yu Mei Anguiano Hernández
- Coordinación de Calidad de Insumos y Laboratorios Especializados, Instituto Mexicano del Seguro Social, Ciudad de México 07760, Mexico; (P.F.H.B.); (C.E.S.T.); (J.E.A.Y.); (B.M.M.); (Y.M.A.H.)
| | - Lumumba Arriaga Nieto
- Coordinación de Vigilancia Epidemiológica, Instituto Mexicano del Seguro Social, Ciudad de México 03100, Mexico; (L.A.N.); (Y.P.A.); (O.C.O.); (G.V.A.); (M.G.R.M.)
| | - Alejandro Moctezuma Paz
- Coordinación de Investigación en Salud, Instituto Mexicano del Seguro Social, Ciudad de México 06720, Mexico;
| | - Leticia Jaimes Betancourt
- Unidad de Medicina Familiar No. 7, Instituto Mexicano del Seguro Social, Ciudad de México 14370, Mexico;
| | - Yadira Pérez Andrade
- Coordinación de Vigilancia Epidemiológica, Instituto Mexicano del Seguro Social, Ciudad de México 03100, Mexico; (L.A.N.); (Y.P.A.); (O.C.O.); (G.V.A.); (M.G.R.M.)
| | - Oscar Cruz Orozco
- Coordinación de Vigilancia Epidemiológica, Instituto Mexicano del Seguro Social, Ciudad de México 03100, Mexico; (L.A.N.); (Y.P.A.); (O.C.O.); (G.V.A.); (M.G.R.M.)
| | - Gabriel Valle Alvarado
- Coordinación de Vigilancia Epidemiológica, Instituto Mexicano del Seguro Social, Ciudad de México 03100, Mexico; (L.A.N.); (Y.P.A.); (O.C.O.); (G.V.A.); (M.G.R.M.)
| | - Mónica Grisel Rivera Mahey
- Coordinación de Vigilancia Epidemiológica, Instituto Mexicano del Seguro Social, Ciudad de México 03100, Mexico; (L.A.N.); (Y.P.A.); (O.C.O.); (G.V.A.); (M.G.R.M.)
| |
Collapse
|
3
|
Hoyos W, Hoyos K, Ruíz R. Using Computational Simulations Based on Fuzzy Cognitive Maps to Detect Dengue Complications. Diagnostics (Basel) 2024; 14:533. [PMID: 38473004 PMCID: PMC10931136 DOI: 10.3390/diagnostics14050533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 03/14/2024] Open
Abstract
Dengue remains a globally prevalent and potentially fatal disease, affecting millions of people worldwide each year. Early and accurate detection of dengue complications is crucial to improving clinical outcomes and reducing the burden on healthcare systems. In this study, we explore the use of computational simulations based on fuzzy cognitive maps (FCMs) to improve the detection of dengue complications. We propose an innovative approach that integrates clinical data into a computational model that mimics the decision-making process of a medical expert. Our method uses FCMs to model complexity and uncertainty in dengue. The model was evaluated in simulated scenarios with each of the dengue classifications. These maps allow us to represent and process vague and fuzzy information effectively, capturing relationships that often go unnoticed in conventional approaches. The results of the simulations show the potential of our approach to detecting dengue complications. This innovative strategy has the potential to transform the way clinical management of dengue is approached. This research is a starting point for further development of complication detection approaches for events of public health concern, such as dengue.
Collapse
Affiliation(s)
- William Hoyos
- Grupo de Investigación en Ingeniería Sostenible e Inteligente, Universidad Cooperativa de Colombia, Montería 230002, Colombia
- Grupo de Investigación en I+D+I en TIC, Universidad EAFIT, Medellín 050022, Colombia
| | - Kenia Hoyos
- Laboratorio Clínico Humano, Clínica Salud Social, Sincelejo 700001, Colombia;
| | - Rander Ruíz
- Grupo de Investigación Interdisciplinario del Bajo Cauca y Sur de Córdoba, Universidad de Antioquia, Campus Caucasia, Caucasia 052410, Colombia;
| |
Collapse
|
4
|
Ansari MS, Jain D, Budhiraja S. Machine-learning prediction models for any blood component transfusion in hospitalized dengue patients. Hematol Transfus Cell Ther 2023:S2531-1379(23)02584-1. [PMID: 37996385 DOI: 10.1016/j.htct.2023.09.2365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 04/17/2023] [Accepted: 09/05/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND Blood component transfusions are a common and often necessary medical practice during the epidemics of dengue. Transfusions are required for patients when they developed severe dengue fever or thrombocytopenia of 10×109/L or less. This study therefore investigated the risk factors, performance and effectiveness of eight different machine-learning algorithms to predict blood component transfusion requirements in confirmed dengue cases admitted to hospital. The objective was to study the risk factors that can help to predict blood component transfusion needs. METHODS Eight predictive models were developed based on retrospective data from a private group of hospitals in India. A python package SHAP (SHapley Additive exPlanations) was used to explain the output of the "XGBoost" model. RESULTS Sixteen vital variables were finally selected as having the most significant effects on blood component transfusion prediction. The XGBoost model presented significantly better predictive performance (area under the curve: 0.793; 95 % confidence interval: 0.699-0.795) than the other models. CONCLUSION Predictive modelling techniques can be utilized to streamline blood component preparation procedures and can help in the triage of high-risk patients and readiness of caregivers to provide blood component transfusions when required. This study demonstrates the potential of multilayer algorithms to reasonably predict any blood component transfusion needs which may help healthcare providers make more informed decisions regarding patient care.
Collapse
Affiliation(s)
- Md Shahid Ansari
- Department of Clinical Data Analytics, Max Super Speciality Hospital, New Delhi, India
| | - Dinesh Jain
- Department of Clinical Data Analytics, Max Super Speciality Hospital, New Delhi, India.
| | - Sandeep Budhiraja
- Department of Internal Medicine, Max Super Speciality Hospital, New Delhi, India
| |
Collapse
|
5
|
Zargari Marandi R, Leung P, Sigera C, Murray DD, Weeratunga P, Fernando D, Rodrigo C, Rajapakse S, MacPherson CR. Development of a machine learning model for early prediction of plasma leakage in suspected dengue patients. PLoS Negl Trop Dis 2023; 17:e0010758. [PMID: 36913411 PMCID: PMC10035900 DOI: 10.1371/journal.pntd.0010758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 03/23/2023] [Accepted: 02/24/2023] [Indexed: 03/14/2023] Open
Abstract
BACKGROUND At least a third of dengue patients develop plasma leakage with increased risk of life-threatening complications. Predicting plasma leakage using laboratory parameters obtained in early infection as means of triaging patients for hospital admission is important for resource-limited settings. METHODS A Sri Lankan cohort including 4,768 instances of clinical data from N = 877 patients (60.3% patients with confirmed dengue infection) recorded in the first 96 hours of fever was considered. After excluding incomplete instances, the dataset was randomly split into a development and a test set with 374 (70%) and 172 (30%) patients, respectively. From the development set, five most informative features were selected using the minimum description length (MDL) algorithm. Random forest and light gradient boosting machine (LightGBM) were used to develop a classification model using the development set based on nested cross validation. An ensemble of the learners via average stacking was used as the final model to predict plasma leakage. RESULTS Lymphocyte count, haemoglobin, haematocrit, age, and aspartate aminotransferase were the most informative features to predict plasma leakage. The final model achieved the area under the receiver operating characteristics curve, AUC = 0.80 with positive predictive value, PPV = 76.9%, negative predictive value, NPV = 72.5%, specificity = 87.9%, and sensitivity = 54.8% on the test set. CONCLUSION The early predictors of plasma leakage identified in this study are similar to those identified in several prior studies that used non-machine learning based methods. However, our observations strengthen the evidence base for these predictors by showing their relevance even when individual data points, missing data and non-linear associations were considered. Testing the model on different populations using these low-cost observations would identify further strengths and limitations of the presented model.
Collapse
Affiliation(s)
- Ramtin Zargari Marandi
- Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Preston Leung
- Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | | | - Daniel Dawson Murray
- Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | | | | | - Chaturaka Rodrigo
- Viral Immunology Systems Program (VISP), Kirby Institute, UNSW Sydney, Sydney, Australia
| | | | - Cameron Ross MacPherson
- Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| |
Collapse
|
6
|
Random forest model can predict the prognosis of hospital-acquired Klebsiella pneumoniae infection as well as traditional logistic regression model. PLoS One 2022; 17:e0278123. [PMID: 36445863 PMCID: PMC9707746 DOI: 10.1371/journal.pone.0278123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 10/06/2022] [Indexed: 12/03/2022] Open
Abstract
OBJECTIVE To explore if random forest (RF) model can predict the prognosis of hospital-acquired Klebsiella pneumoniae infection as well as traditional logistic regression(LR) model. METHODS A total of 254 cases of hospital-acquired Klebsiella pneumoniae infection in a tertiary hospital in Beijing from January 2016 to December 2020 were retrospectively collected. Appropriate influencing factors were selected by referring to relevant articles from the aspects of basic clinical information and contact history before infection, and divided into a training set and a test set. Both the RF and LR models were trained by the training set, and using testing set to compare these two models. RESULTS The prediction accuracy of the LR model was 87.0%, the true positive rate of the LR model was 94.7%; the false negative rate of the LR model was 5.3%; the false positive rate of the LR model was 35%; the true negative rate of the LR model was 65%; the sensitivity of the LR model for the prognosis prediction of hospital-acquired Klebsiella pneumoniae infection was 94.7%; and the specificity was 65%. The prediction accuracy of the RF model was 89.6%; the true positive rate of the RF model was 92.1%; the false negative rate of the RF model was 7.9%; the false positive rate of the RF model was 21.4%; the true negative rate of the RF model was 78.6%; the sensitivity of the RF model for the prognosis prediction of hospital-acquired Klebsiella pneumoniae infection was 92.1%; and the specificity was 78.6%. ROC curve shows that the area under curve(AUC) of the LR model was 0.91, and that of the RF model was 0.95. CONCLUSION The RF model has higher specificity, sensitivity, and accuracy for the prognostic prediction of hospital-acquired Klebsiella pneumoniae infection than the LR model and has greater clinical application prospects.
Collapse
|
7
|
Ghetia C, Bhatt P, Mukhopadhyay C. Association of dengue virus non-structural-1 protein with disease severity: a brief review. Trans R Soc Trop Med Hyg 2022; 116:986-995. [PMID: 36125197 DOI: 10.1093/trstmh/trac087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 10/19/2021] [Accepted: 08/31/2022] [Indexed: 01/19/2023] Open
Abstract
Dengue virus (DENV) was discovered by P. M. Ashburn and Charles F. Craig in 1907. Evidence of dengue-like illness was observed before 1907 and DENV epidemics have been reported from different parts of the world since then, with increased morbidity rates every year. DENV typically causes a febrile illness that ranges from mild asymptomatic infection to fatal dengue haemorrhagic fever (DHF) and/or dengue shock syndrome (DSS). Host mechanisms through which mild infection progresses to the fatal forms are still unknown. Few factors have been associated to aid severe disease acquisition, DENV non-structural 1 (NS1) protein being one of them. NS1 is a highly conserved glycoprotein among the Flavivirus and is often used as a biomarker for dengue diagnosis. This review focuses on assessing the role of NS1 in severe dengue. In this review, hospital-based studies on the association of dengue NS1 with severe dengue from all over the world have been assessed and analysed and the majority of the studies positively correlate high NS1 levels with DHF/DSS acquisition. The review also discusses a few experimental studies on NS1 that have shown it contributes to dengue pathogenesis. This review assesses the role of NS1 and disease severity from hospital-based studies and aims to provide better insights on the kinetics and dynamics of DENV infection with respect to NS1 for a better understanding of the role of NS1 in dengue.
Collapse
Affiliation(s)
- Charmi Ghetia
- Manipal Institute of Virology, Manipal Academy of Higher Education, Manipal-576104, Karnataka, India
| | - Puneet Bhatt
- Manipal Institute of Virology, Manipal Academy of Higher Education, Manipal-576104, Karnataka, India
| | - Chiranjay Mukhopadhyay
- Manipal Institute of Virology, Manipal Academy of Higher Education, Manipal-576104, Karnataka, India
| |
Collapse
|
8
|
Limothai U, Jantarangsi N, Suphavejkornkij N, Tachaboon S, Dinhuzen J, Chaisuriyong W, Trongkamolchai S, Wanpaisitkul M, Chulapornsiri C, Tiawilai A, Tiawilai T, Tantawichien T, Thisyakorn U, Srisawat N. Discovery and validation of circulating miRNAs for the clinical prognosis of severe dengue. PLoS Negl Trop Dis 2022; 16:e0010836. [PMID: 36251659 PMCID: PMC9576100 DOI: 10.1371/journal.pntd.0010836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 09/21/2022] [Indexed: 11/06/2022] Open
Abstract
Background Early prognostic markers of severe dengue may improve case management and reduce dengue-related mortalities. This study aimed to identify circulating microRNAs (miRNAs) as biomarkers for predicting severe dengue. Methodology Serum samples from dengue-infected patients were collected on the first day of admission. Patients were followed up for 14 days after admission to determine the final diagnosis. Participants were divided into non-severe and severe dengue, as defined by WHO 2009 criteria. Circulating microtranscriptome analysis was performed using NanoString miRNA Expression Assay. The expression level of candidate miRNAs were then validated by quantitative reverse transcription-PCR method. Principal findings The discovery cohort (N = 19) lead to the identification of 37 differentially expressed miRNAs between the two groups. Six up-regulated candidate miRNAs were selected and further validated in the larger cohort (N = 135). MiR574-5p and miR1246 displayed the highest diagnostic performance in discriminating between severe from non-severe dengue (ROC-AUC = 0.83). Additionally, miR574-5p and miR1246 had high sensitivity and high negative predictive value for detecting severe dengue. Multivariate analysis suggested that serum miR574-5p was an independent predictor of severe dengue (odds ratio 3.30, 95% CI 1.81–6.04; p<0.001). Conclusion Our study indicated that circulating miRNAs, especially miR-574-5p and miR-1246, might be a promising diagnostic and prognostic biomarker for severe dengue upon hospital admission, especially when using these biomarkers on days 1 to 2 before the onset of severe dengue complications. Dengue infection, a mosquito-borne disease, is an expanding global problem. It has a broad clinical spectrum that includes severe and non-severe clinical manifestations with a high risk of death. Identifying early prognostic markers of severe complications may improve case management and reduce dengue-related mortalities. The circulating microRNA (miRNA) profile has been widely used to identify potential biomarkers against viral infections. Our data revealed that the circulating miRNA expression pattern of severe dengue patients was significantly different from the non-severe group. In addition, circulating miRNAs, especially miR-574-5p and miR-1246, could be promising diagnostic and prognostic biomarkers for severe dengue. These data have implications for developing biomarkers for clinical use and could improve risk prediction in dengue patients.
Collapse
Affiliation(s)
- Umaporn Limothai
- Excellence Center for Critical Care Nephrology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand,Center of Excellence in Critical Care Nephrology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand,Tropical Medicine Cluster, Chulalongkorn University, Bangkok, Thailand
| | | | | | - Sasipha Tachaboon
- Excellence Center for Critical Care Nephrology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand,Center of Excellence in Critical Care Nephrology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand,Tropical Medicine Cluster, Chulalongkorn University, Bangkok, Thailand
| | - Janejira Dinhuzen
- Excellence Center for Critical Care Nephrology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand,Center of Excellence in Critical Care Nephrology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand,Tropical Medicine Cluster, Chulalongkorn University, Bangkok, Thailand
| | - Watchadaporn Chaisuriyong
- Excellence Center for Critical Care Nephrology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand,Center of Excellence in Critical Care Nephrology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand,Tropical Medicine Cluster, Chulalongkorn University, Bangkok, Thailand
| | | | | | | | | | | | - Terapong Tantawichien
- Tropical Medicine Cluster, Chulalongkorn University, Bangkok, Thailand,Division of Infectious Diseases, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Usa Thisyakorn
- Tropical Medicine Cluster, Chulalongkorn University, Bangkok, Thailand
| | - Nattachai Srisawat
- Excellence Center for Critical Care Nephrology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand,Center of Excellence in Critical Care Nephrology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand,Tropical Medicine Cluster, Chulalongkorn University, Bangkok, Thailand,Division of Nephrology, Department of Medicine, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Bangkok, Thailand,Center for Critical Care Nephrology, The CRISMA Center, Department of Critical Care Medicine, University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania, United States of America,Academy of Science, Royal Society of Thailand, Bangkok, Thailand,* E-mail:
| |
Collapse
|
9
|
An autonomous cycle of data analysis tasks for the clinical management of dengue. Heliyon 2022; 8:e10846. [PMID: 36203901 PMCID: PMC9529583 DOI: 10.1016/j.heliyon.2022.e10846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 08/11/2022] [Accepted: 09/26/2022] [Indexed: 11/23/2022] Open
Abstract
Dengue is the most widespread vector-borne disease worldwide. Timely diagnosis and treatment of dengue is the main objective of medical professionals to decrease mortality rates. In this paper, we propose an autonomous cycle that integrates data analysis tasks to support decision-making in the clinical management of dengue. Particularly, the autonomous cycle supports dengue diagnosis and treatment. The proposed system was built using machine learning techniques for classification tasks (artificial neural networks and support vector machines) and evolutionary techniques (a genetic algorithm) for prescription tasks (treatment). The system was quantitatively evaluated using dengue-patient datasets reported by healthcare institutions. Our system was compared with previous works using qualitative criteria. The proposed system has the ability to classify a patient's clinical picture and recommend the best treatment option. In particular, the classification of dengue was done with 98% accuracy and a genetic algorithm recommends treatment options for particular patients. Finally, our system is flexible and easily adaptable, which will allow the addition of new tasks for dengue analysis.
Collapse
|
10
|
Shi M, Lin J, Wei W, Qin Y, Meng S, Chen X, Li Y, Chen R, Yuan Z, Qin Y, Huang J, Liang B, Liao Y, Ye L, Liang H, Xie Z, Jiang J. Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi, China. PLoS Negl Trop Dis 2022; 16:e0010388. [PMID: 35507586 PMCID: PMC9067679 DOI: 10.1371/journal.pntd.0010388] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 04/02/2022] [Indexed: 12/03/2022] Open
Abstract
Objective Talaromycosis is a serious regional disease endemic in Southeast Asia. In China, Talaromyces marneffei (T. marneffei) infections is mainly concentrated in the southern region, especially in Guangxi, and cause considerable in-hospital mortality in HIV-infected individuals. Currently, the factors that influence in-hospital death of HIV/AIDS patients with T. marneffei infection are not completely clear. Existing machine learning techniques can be used to develop a predictive model to identify relevant prognostic factors to predict death and appears to be essential to reducing in-hospital mortality. Methods We prospectively enrolled HIV/AIDS patients with talaromycosis in the Fourth People’s Hospital of Nanning, Guangxi, from January 2012 to June 2019. Clinical features were selected and used to train four different machine learning models (logistic regression, XGBoost, KNN, and SVM) to predict the treatment outcome of hospitalized patients, and 30% internal validation was used to evaluate the performance of models. Machine learning model performance was assessed according to a range of learning metrics, including area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) tool was used to explain the model. Results A total of 1927 HIV/AIDS patients with T. marneffei infection were included. The average in-hospital mortality rate was 13.3% (256/1927) from 2012 to 2019. The most common complications/coinfections were pneumonia (68.9%), followed by oral candida (47.5%), and tuberculosis (40.6%). Deceased patients showed higher CD4/CD8 ratios, aspartate aminotransferase (AST) levels, creatinine levels, urea levels, uric acid (UA) levels, lactate dehydrogenase (LDH) levels, total bilirubin levels, creatine kinase levels, white blood-cell counts (WBC) counts, neutrophil counts, procaicltonin levels and C-reactive protein (CRP) levels and lower CD3+ T-cell count, CD8+ T-cell count, and lymphocyte counts, platelet (PLT), high-density lipoprotein cholesterol (HDL), hemoglobin (Hb) levels than those of surviving patients. The predictive XGBoost model exhibited 0.71 sensitivity, 0.99 specificity, and 0.97 AUC in the training dataset, and our outcome prediction model provided robust discrimination in the testing dataset, showing an AUC of 0.90 with 0.69 sensitivity and 0.96 specificity. The other three models were ruled out due to poor performance. Septic shock and respiratory failure were the most important predictive features, followed by uric acid, urea, platelets, and the AST/ALT ratios. Conclusion The XGBoost machine learning model is a good predictor in the hospitalization outcome of HIV/AIDS patients with T. marneffei infection. The model may have potential application in mortality prediction and high-risk factor identification in the talaromycosis population. Talaromyces marneffei can cause a fatal deeply disseminated fungal infection- talaromycosis. It is widely distributed in Southeast Asia and spreading globally, the disease is insidious and responsible for significant deaths. Clinicians need easy-to-use tools to make decisions on which patients are at a higher risk of dying after infecting T. marneffei. In this study, conducted in Southern China, we have evolved XGBoost machine learning model. 15 clinical indicators and laboratory measures were used to estimate a patient’s risk of dying in the hospital due to the T. marneffei infection. The study showed that the machine learning model has good predictive ability when tested in an internal testing population of patients. We expect that the model could help clinicians assess a patient’s risk of death in just the time of admission to help decide on early treatment timing of high-risk patients who are likely to die.
Collapse
Affiliation(s)
- Minjuan Shi
- Guangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical University, Nanning, Guangxi, China
| | - Jianyan Lin
- Fourth People’s Hospital of Nanning, Nanning, Guangxi, China
| | - Wudi Wei
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
| | - Yaqin Qin
- Fourth People’s Hospital of Nanning, Nanning, Guangxi, China
| | - Sirun Meng
- Fourth People’s Hospital of Nanning, Nanning, Guangxi, China
| | - Xiaoyu Chen
- Fourth People’s Hospital of Nanning, Nanning, Guangxi, China
| | - Yueqi Li
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
| | - Rongfeng Chen
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
| | - Zongxiang Yuan
- Guangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical University, Nanning, Guangxi, China
| | - Yingmei Qin
- Fourth People’s Hospital of Nanning, Nanning, Guangxi, China
| | - Jiegang Huang
- Guangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical University, Nanning, Guangxi, China
| | - Bingyu Liang
- Guangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical University, Nanning, Guangxi, China
| | - Yanyan Liao
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
| | - Li Ye
- Guangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical University, Nanning, Guangxi, China
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
- * E-mail: (LY); (HL); (ZX); (JJ)
| | - Hao Liang
- Guangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical University, Nanning, Guangxi, China
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
- * E-mail: (LY); (HL); (ZX); (JJ)
| | - Zhiman Xie
- Fourth People’s Hospital of Nanning, Nanning, Guangxi, China
- * E-mail: (LY); (HL); (ZX); (JJ)
| | - Junjun Jiang
- Guangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical University, Nanning, Guangxi, China
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
- * E-mail: (LY); (HL); (ZX); (JJ)
| |
Collapse
|
11
|
Liu YE, Saul S, Rao AM, Robinson ML, Agudelo Rojas OL, Sanz AM, Verghese M, Solis D, Sibai M, Huang CH, Sahoo MK, Gelvez RM, Bueno N, Estupiñan Cardenas MI, Villar Centeno LA, Rojas Garrido EM, Rosso F, Donato M, Pinsky BA, Einav S, Khatri P. An 8-gene machine learning model improves clinical prediction of severe dengue progression. Genome Med 2022; 14:33. [PMID: 35346346 PMCID: PMC8959795 DOI: 10.1186/s13073-022-01034-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 02/24/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Each year 3-6 million people develop life-threatening severe dengue (SD). Clinical warning signs for SD manifest late in the disease course and are nonspecific, leading to missed cases and excess hospital burden. Better SD prognostics are urgently needed. METHODS We integrated 11 public datasets profiling the blood transcriptome of 365 dengue patients of all ages and from seven countries, encompassing biological, clinical, and technical heterogeneity. We performed an iterative multi-cohort analysis to identify differentially expressed genes (DEGs) between non-severe patients and SD progressors. Using only these DEGs, we trained an XGBoost machine learning model on public data to predict progression to SD. All model parameters were "locked" prior to validation in an independent, prospectively enrolled cohort of 377 dengue patients in Colombia. We measured expression of the DEGs in whole blood samples collected upon presentation, prior to SD progression. We then compared the accuracy of the locked XGBoost model and clinical warning signs in predicting SD. RESULTS We identified eight SD-associated DEGs in the public datasets and built an 8-gene XGBoost model that accurately predicted SD progression in the independent validation cohort with 86.4% (95% CI 68.2-100) sensitivity and 79.7% (95% CI 75.5-83.9) specificity. Given the 5.8% proportion of SD cases in this cohort, the 8-gene model had a positive and negative predictive value (PPV and NPV) of 20.9% (95% CI 16.7-25.6) and 99.0% (95% CI 97.7-100.0), respectively. Compared to clinical warning signs at presentation, which had 77.3% (95% CI 58.3-94.1) sensitivity and 39.7% (95% CI 34.7-44.9) specificity, the 8-gene model led to an 80% reduction in the number needed to predict (NNP) from 25.4 to 5.0. Importantly, the 8-gene model accurately predicted subsequent SD in the first three days post-fever onset and up to three days prior to SD progression. CONCLUSIONS The 8-gene XGBoost model, trained on heterogeneous public datasets, accurately predicted progression to SD in a large, independent, prospective cohort, including during the early febrile stage when SD prediction remains clinically difficult. The model has potential to be translated to a point-of-care prognostic assay to reduce dengue morbidity and mortality without overwhelming limited healthcare resources.
Collapse
Affiliation(s)
- Yiran E. Liu
- grid.168010.e0000000419368956Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, CA Stanford, USA ,grid.168010.e0000000419368956Cancer Biology Graduate Program, School of Medicine, Stanford University, CA Stanford, USA ,grid.168010.e0000000419368956Division of Infectious Diseases and Geographic Medicine, Department of Medicine, School of Medicine, Stanford University, CA Stanford, USA
| | - Sirle Saul
- grid.168010.e0000000419368956Division of Infectious Diseases and Geographic Medicine, Department of Medicine, School of Medicine, Stanford University, CA Stanford, USA
| | - Aditya Manohar Rao
- grid.168010.e0000000419368956Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, CA Stanford, USA ,grid.168010.e0000000419368956Immunology Graduate Program, School of Medicine, Stanford University, CA Stanford, USA
| | - Makeda Lucretia Robinson
- grid.168010.e0000000419368956Division of Infectious Diseases and Geographic Medicine, Department of Medicine, School of Medicine, Stanford University, CA Stanford, USA ,grid.168010.e0000000419368956Department of Pathology, School of Medicine, Stanford University, CA Stanford, USA
| | | | - Ana Maria Sanz
- grid.477264.4Clinical Research Center, Fundación Valle del Lili, Cali, Colombia
| | - Michelle Verghese
- grid.168010.e0000000419368956Department of Pathology, School of Medicine, Stanford University, CA Stanford, USA
| | - Daniel Solis
- grid.168010.e0000000419368956Department of Pathology, School of Medicine, Stanford University, CA Stanford, USA
| | - Mamdouh Sibai
- grid.168010.e0000000419368956Department of Pathology, School of Medicine, Stanford University, CA Stanford, USA
| | - Chun Hong Huang
- grid.168010.e0000000419368956Department of Pathology, School of Medicine, Stanford University, CA Stanford, USA
| | - Malaya Kumar Sahoo
- grid.168010.e0000000419368956Department of Pathology, School of Medicine, Stanford University, CA Stanford, USA
| | - Rosa Margarita Gelvez
- Centro de Atención y Diagnóstico de Enfermedades Infecciosas (CDI), Bucaramanga, Colombia
| | - Nathalia Bueno
- Centro de Atención y Diagnóstico de Enfermedades Infecciosas (CDI), Bucaramanga, Colombia
| | | | | | | | - Fernando Rosso
- grid.477264.4Clinical Research Center, Fundación Valle del Lili, Cali, Colombia ,grid.477264.4Division of Infectious Diseases, Department of Internal Medicine, Fundación Valle del Lili, Cali, Colombia
| | - Michele Donato
- grid.168010.e0000000419368956Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, CA Stanford, USA ,grid.168010.e0000000419368956Center for Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University, CA Stanford, USA
| | - Benjamin A. Pinsky
- grid.168010.e0000000419368956Division of Infectious Diseases and Geographic Medicine, Department of Medicine, School of Medicine, Stanford University, CA Stanford, USA ,grid.168010.e0000000419368956Department of Pathology, School of Medicine, Stanford University, CA Stanford, USA
| | - Shirit Einav
- grid.168010.e0000000419368956Division of Infectious Diseases and Geographic Medicine, Department of Medicine, School of Medicine, Stanford University, CA Stanford, USA ,grid.168010.e0000000419368956Department of Microbiology and Immunology, School of Medicine, Stanford University, CA Stanford, USA
| | - Purvesh Khatri
- grid.168010.e0000000419368956Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, CA Stanford, USA ,grid.168010.e0000000419368956Center for Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University, CA Stanford, USA
| |
Collapse
|
12
|
Hung SJ, Tsai HP, Wang YF, Ko WC, Wang JR, Huang SW. Assessment of the Risk of Severe Dengue Using Intrahost Viral Population in Dengue Virus Serotype 2 Patients via Machine Learning. Front Cell Infect Microbiol 2022; 12:831281. [PMID: 35223554 PMCID: PMC8866709 DOI: 10.3389/fcimb.2022.831281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
Dengue virus, a positive-sense single-stranded RNA virus, continuously threatens human health. Although several criteria for evaluation of severe dengue have been recently established, the ability to prognose the risk of severe outcomes for dengue patients remains limited. Mutant spectra of RNA viruses, including single nucleotide variants (SNVs) and defective virus genomes (DVGs), contribute to viral virulence and growth. Here, we determine the potency of intrahost viral population in dengue patients with primary infection that progresses into severe dengue. A total of 65 dengue virus serotype 2 infected patients in primary infection including 17 severe cases were enrolled. We utilized deep sequencing to directly define the frequency of SNVs and detection times of DVGs in sera of dengue patients and analyzed their associations with severe dengue. Among the detected SNVs and DVGs, the frequencies of 9 SNVs and the detection time of 1 DVG exhibited statistically significant differences between patients with dengue fever and those with severe dengue. By utilizing the detected frequencies/times of the selected SNVs/DVG as features, the machine learning model showed high average with a value of area under the receiver operating characteristic curve (AUROC, 0.966 ± 0.064). The elevation of the frequency of SNVs at E (nucleotide position 995 and 2216), NS2A (nucleotide position 4105), NS3 (nucleotide position 4536, 4606), and NS5 protein (nucleotide position 7643 and 10067) and the detection times of the selected DVG that had a deletion junction in the E protein region (nucleotide positions of the junction: between 969 and 1022) increased the possibility of dengue patients for severe dengue. In summary, we demonstrated the detected frequencies/times of SNVs/DVG in dengue patients associated with severe disease and successfully utilized them to discriminate severe patients using machine learning algorithm. The identified SNVs and DVGs that are associated with severe dengue will expand our understanding of intrahost viral population in dengue pathogenesis.
Collapse
Affiliation(s)
- Su-Jhen Hung
- National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Tainan, Taiwan
| | - Huey-Pin Tsai
- Department of Pathology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Medical Laboratory Science and Biotechnology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ya-Fang Wang
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Tainan, Taiwan
| | - Wen-Chien Ko
- Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Jen-Ren Wang
- Department of Pathology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Medical Laboratory Science and Biotechnology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Tainan, Taiwan
- Center of Infectious Disease and Signaling Research, National Cheng Kung University, Tainan, Taiwan
| | - Sheng-Wen Huang
- National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Tainan, Taiwan
- *Correspondence: Sheng-Wen Huang,
| |
Collapse
|
13
|
Ming DK, Hernandez B, Sangkaew S, Vuong NL, Lam PK, Nguyet NM, Tam DTH, Trung DT, Tien NTH, Tuan NM, Chau NVV, Tam CT, Chanh HQ, Trieu HT, Simmons CP, Wills B, Georgiou P, Holmes AH, Yacoub S. Applied machine learning for the risk-stratification and clinical decision support of hospitalised patients with dengue in Vietnam. PLOS DIGITAL HEALTH 2022; 1:e0000005. [PMID: 36812518 PMCID: PMC9931311 DOI: 10.1371/journal.pdig.0000005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 11/15/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND Identifying patients at risk of dengue shock syndrome (DSS) is vital for effective healthcare delivery. This can be challenging in endemic settings because of high caseloads and limited resources. Machine learning models trained using clinical data could support decision-making in this context. METHODS We developed supervised machine learning prediction models using pooled data from adult and paediatric patients hospitalised with dengue. Individuals from 5 prospective clinical studies in Ho Chi Minh City, Vietnam conducted between 12th April 2001 and 30th January 2018 were included. The outcome was onset of dengue shock syndrome during hospitalisation. Data underwent random stratified splitting at 80:20 ratio with the former used only for model development. Ten-fold cross-validation was used for hyperparameter optimisation and confidence intervals derived from percentile bootstrapping. Optimised models were evaluated against the hold-out set. FINDINGS The final dataset included 4,131 patients (477 adults and 3,654 children). DSS was experienced by 222 (5.4%) of individuals. Predictors were age, sex, weight, day of illness at hospitalisation, indices of haematocrit and platelets over first 48 hours of admission and before the onset of DSS. An artificial neural network model (ANN) model had best performance with an area under receiver operator curve (AUROC) of 0.83 (95% confidence interval [CI], 0.76-0.85) in predicting DSS. When evaluated against the independent hold-out set this calibrated model exhibited an AUROC of 0.82, specificity of 0.84, sensitivity of 0.66, positive predictive value of 0.18 and negative predictive value of 0.98. INTERPRETATION The study demonstrates additional insights can be obtained from basic healthcare data, when applied through a machine learning framework. The high negative predictive value could support interventions such as early discharge or ambulatory patient management in this population. Work is underway to incorporate these findings into an electronic clinical decision support system to guide individual patient management.
Collapse
Affiliation(s)
- Damien K. Ming
- Department of Infectious Disease, Imperial College London, United Kingdom
- * E-mail:
| | - Bernard Hernandez
- Centre for Antimicrobial Optimisation, Imperial College London, United Kingdom
- Centre for BioInspired Technology, Imperial College London, United Kingdom
| | - Sorawat Sangkaew
- Centre for Antimicrobial Optimisation, Imperial College London, United Kingdom
| | - Nguyen Lam Vuong
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
- University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Phung Khanh Lam
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
- University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Nguyen Minh Nguyet
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
| | - Dong Thi Hoai Tam
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
| | - Dinh The Trung
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
| | - Nguyen Thi Hanh Tien
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
| | - Nguyen Minh Tuan
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
- Children’s Hospital 1, Ho Chi Minh City, Vietnam
| | - Nguyen Van Vinh Chau
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
- Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
| | - Cao Thi Tam
- Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
| | - Ho Quang Chanh
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
- Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
| | - Huynh Trung Trieu
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
- Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
| | - Cameron P. Simmons
- Institute of Vector Borne Disease, Monash University, Clayton, Australia
| | - Bridget Wills
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, United Kingdom
| | - Pantelis Georgiou
- Centre for Antimicrobial Optimisation, Imperial College London, United Kingdom
- Centre for BioInspired Technology, Imperial College London, United Kingdom
| | - Alison H. Holmes
- Centre for Antimicrobial Optimisation, Imperial College London, United Kingdom
| | - Sophie Yacoub
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, United Kingdom
| | | |
Collapse
|
14
|
Hoyos W, Aguilar J, Toro M. Dengue models based on machine learning techniques: A systematic literature review. Artif Intell Med 2021; 119:102157. [PMID: 34531010 DOI: 10.1016/j.artmed.2021.102157] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 05/08/2021] [Accepted: 08/17/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND Dengue modeling is a research topic that has increased in recent years. Early prediction and decision-making are key factors to control dengue. This Systematic Literature Review (SLR) analyzes three modeling approaches of dengue: diagnostic, epidemic, intervention. These approaches require models of prediction, prescription and optimization. This SLR establishes the state-of-the-art in dengue modeling, using machine learning, in the last years. METHODS Several databases were selected to search the articles. The selection was made based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Sixty-four articles were obtained and analyzed to describe their strengths and limitations. Finally, challenges and opportunities for research on machine-learning for dengue modeling were identified. RESULTS Logistic regression was the most used modeling approach for the diagnosis of dengue (59.1%). The analysis of the epidemic approach showed that linear regression (17.4%) is the most used technique within the spatial analysis. Finally, the most used intervention modeling is General Linear Model with 70%. CONCLUSIONS We conclude that cause-effect models may improve diagnosis and understanding of dengue. Models that manage uncertainty can also be helpful, because of low data-quality in healthcare. Finally, decentralization of data, using federated learning, may decrease computational costs and allow model building without compromising data security.
Collapse
Affiliation(s)
- William Hoyos
- Grupo de Investigaciones Microbiológicas y Biomédicas de Córdoba, Universidad de Córdoba, Montería, Colombia; Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia.
| | - Jose Aguilar
- Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia; Centro de Estudios en Microelectrónica y Sistemas Distribuidos, Universidad de Los Andes, Mérida, Venezuela; Universidad de Alcalá, Depto. de Automática, Alcalá de Henares, Spain
| | - Mauricio Toro
- Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia
| |
Collapse
|
15
|
Different Profiles of Cytokines, Chemokines and Coagulation Mediators Associated with Severity in Brazilian Patients Infected with Dengue Virus. Viruses 2021; 13:v13091789. [PMID: 34578370 PMCID: PMC8473164 DOI: 10.3390/v13091789] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/02/2021] [Accepted: 09/02/2021] [Indexed: 12/27/2022] Open
Abstract
The incidence of dengue in Latin America has increased dramatically during the last decade. Understanding the pathogenic mechanisms in dengue is crucial for the identification of biomarkers for the triage of patients. We aimed to characterize the profile of cytokines (IFN-γ, TNF-α, IL-1β, IL-6, IL-18 and IL-10), chemokines (CXCL8/IL-8, CCL2/MCP-1 and CXCL10/IP-10) and coagulation mediators (Fibrinogen, D-dimer, Tissue factor-TF, Tissue factor pathway inhibitor-TFPI and Thrombomodulin) during the dengue-4 epidemic in Brazil. Laboratory-confirmed dengue cases had higher levels of TNF-α (p < 0.001), IL-6 (p = 0.005), IL-10 (p < 0.001), IL-18 (p = 0.001), CXCL8/IL-8 (p < 0.001), CCL2/MCP-1 (p < 0.001), CXCL10/IP-10 (p = 0.001), fibrinogen (p = 0.037), D-dimer (p = 0.01) and TFPI (p = 0.042) and lower levels of TF (p = 0.042) compared to healthy controls. A principal component analysis (PCA) distinguished between two profiles of mediators of inflammation and coagulation: protective (TNF-α, IL-1β and CXCL8/IL-8) and pathological (IL-6, TF and TFPI). Lastly, multivariate logistic regression analysis identified high aspartate aminotransferase-to-platelet ratio index (APRI) as independent risk factors associated with severity (adjusted OR: 1.33; 95% CI 1.03–1.71; p = 0.027), the area under the receiver operating characteristics curve (AUC) was 0.775 (95% CI 0.681–0.869) and an optimal cutoff value was 1.4 (sensitivity: 76%; specificity: 79%), so it could be a useful marker for the triage of patients attending primary care centers.
Collapse
|