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Herrera CN, Gimenes FRE, Herrera JP, Cavalli R. Development of Automated Triggers in Ambulatory Settings in Brazil: Protocol for a Machine Learning-Based Design Thinking Study. JMIR Res Protoc 2024; 13:e55466. [PMID: 39133913 PMCID: PMC11347893 DOI: 10.2196/55466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/22/2024] [Accepted: 06/17/2024] [Indexed: 08/30/2024] Open
Abstract
BACKGROUND The use of technologies has had a significant impact on patient safety and the quality of care and has increased globally. In the literature, it has been reported that people die annually due to adverse events (AEs), and various methods exist for investigating and measuring AEs. However, some methods have a limited scope, data extraction, and the need for data standardization. In Brazil, there are few studies on the application of trigger tools, and this study is the first to create automated triggers in ambulatory care. OBJECTIVE This study aims to develop a machine learning (ML)-based automated trigger for outpatient health care settings in Brazil. METHODS A mixed methods research will be conducted within a design thinking framework and the principles will be applied in creating the automated triggers, following the stages of (1) empathize and define the problem, involving observations and inquiries to comprehend both the user and the challenge at hand; (2) ideation, where various solutions to the problem are generated; (3) prototyping, involving the construction of a minimal representation of the best solutions; (4) testing, where user feedback is obtained to refine the solution; and (5) implementation, where the refined solution is tested, changes are assessed, and scaling is considered. Furthermore, ML methods will be adopted to develop automated triggers, tailored to the local context in collaboration with an expert in the field. RESULTS This protocol describes a research study in its preliminary stages, prior to any data gathering and analysis. The study was approved by the members of the organizations within the institution in January 2024 and by the ethics board of the University of São Paulo and the institution where the study will take place. in May 2024. As of June 2024, stage 1 commenced with data gathering for qualitative research. A separate paper focused on explaining the method of ML will be considered after the outcomes of stages 1 and 2 in this study. CONCLUSIONS After the development of automated triggers in the outpatient setting, it will be possible to prevent and identify potential risks of AEs more promptly, providing valuable information. This technological innovation not only promotes advances in clinical practice but also contributes to the dissemination of techniques and knowledge related to patient safety. Additionally, health care professionals can adopt evidence-based preventive measures, reducing costs associated with AEs and hospital readmissions, enhancing productivity in outpatient care, and contributing to the safety, quality, and effectiveness of care provided. Additionally, in the future, if the outcome is successful, there is the potential to apply it in all units, as planned by the institutional organization. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/55466.
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Affiliation(s)
- Claire Nierva Herrera
- Fundamental of Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto, Brazil
| | | | | | - Ricardo Cavalli
- Faculty of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil
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Silva AFD, Figueiredo K, Falcão IWS, Costa FAR, da Rocha Seruffo MC, de Moraes CCG. Study of machine learning techniques for outcome assessment of leptospirosis patients. Sci Rep 2024; 14:13929. [PMID: 38886357 DOI: 10.1038/s41598-024-62254-1] [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: 03/28/2023] [Accepted: 05/15/2024] [Indexed: 06/20/2024] Open
Abstract
Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry out specific laboratory tests. In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. Using the records contained in the government National System of Aggressions and Notification (SINAN, in portuguese) from 2007 to 2017, for the state of Pará, Brazil, where the temporal attributes of health care, symptoms (headache, vomiting, jaundice, calf pain) and clinical evolution (renal failure and respiratory changes) were used. In the performance evaluation of the selected models, it was observed that the Random Forest exhibited an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.29 for the validation database. So, this result considers the best attributes pointed out by experiment 10: time first symptoms medical attention, time first symptoms ELISA sample collection, medical attention hospital admission time, headache, calf pain, vomiting, jaundice, renal insufficiency, and respiratory alterations. The contribution of this article is the confirmation that artificial intelligence, using the Decision Tree model algorithm, depicting the best choice as the final model to be used in future data for the prediction of human leptospirosis cases, helping in the diagnosis and course of the disease, aiming to avoid the evolution to death.
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Affiliation(s)
- Andreia Ferreira da Silva
- Laboratory of Zoonoses and Public Health - Federal University of Para, Av. dos Universitários - Jaderlândia, Belém, PA, 68746-360, Brazil
| | - Karla Figueiredo
- Department of Informatics and Computer Science, Institute of Mathematics and Statistics, Rio de Janeiro State University, Rua São Francisco Xavier, 524, Rio de Janeiro, 20550-013, Brazil
| | - Igor W S Falcão
- Federal University of Para, R. Augusto Corrêa, 1 - Guamá, Belém, 66075-110, Brazil
| | - Fernando A R Costa
- Federal University of Para, R. Augusto Corrêa, 1 - Guamá, Belém, 66075-110, Brazil
| | | | - Carla Cristina Guimarães de Moraes
- Laboratory of Zoonoses and Public Health - Federal University of Para, Av. dos Universitários - Jaderlândia, Belém, PA, 68746-360, Brazil.
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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.
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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
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Ren H, An C, Fu W, Wu J, Yao W, Yu J, Liang P. Prediction of local tumor progression after microwave ablation for early-stage hepatocellular carcinoma with machine learning. J Cancer Res Ther 2023; 19:978-987. [PMID: 37675726 DOI: 10.4103/jcrt.jcrt_319_23] [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: 09/08/2023]
Abstract
Objectives Local tumor progression (LTP) is a major constraint for achieving technical success in microwave ablation (MWA) for the treatment of early-stage hepatocellular carcinoma (EHCC). This study aims to develop machine learning (ML)-based predictive models for LTP after initial MWA in EHCC. Materials and Methods A total of 607 treatment-naïve EHCC patients (mean ± standard deviation [SD] age, 57.4 ± 10.8 years) with 934 tumors according to the Milan criteria who subsequently underwent MWA between August 2009 and January 2016 were enrolled. During the same period, 299 patients were assigned to the external validation datasets. To identify risk factors of LTP after MWA, clinicopathological data and ablation parameters were collected. Predictive models were developed according to 21 variables using four ML algorithms and evaluated based on the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CIs). Results After a median follow-up time of 28.7 months (range, 7.6-110.5 months), 6.9% (42/607) of patients had confirmed LTP in the training dataset. The tumor size and number were significantly related to LTP. The AUCs of the four models ranged from 0.791 to 0.898. The best performance (AUC: 0.898, 95% CI: [0.842 0.954]; SD: 0.028) occurred when nine variables were introduced to the CatBoost algorithm. According to the feature selection algorithms, the top six predictors were tumor number, albumin and alpha-fetoprotein, tumor size, age, and international normalized ratio. Conclusions Out of the four ML models, the CatBoost model performed best, and reasonable and precise ablation protocols will significantly reduce LTP.
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Affiliation(s)
- He Ren
- Department of Ultrasound, The Sixth Medical Center of PLA General Hospital; Department of Ultrasound, The Fifth Medical Center of PLA General Hospital, Beijing, China
| | - Chao An
- Department of Ultrasound, The Fifth Medical Center of PLA General Hospital, Beijing, China
| | - Wanxi Fu
- Department of Ultrasound, The Sixth Medical Center of PLA General Hospital, Beijing, China
| | - Jingyan Wu
- Department of Medical Image, Yangfangdian Community Healthcare Centre, Beijing, China
| | - Wenhuan Yao
- Department of Ultrasound, The Sixth Medical Center of PLA General Hospital, Beijing, China
| | - Jie Yu
- Department of Ultrasound, The Fifth Medical Center of PLA General Hospital, Beijing, China
| | - Ping Liang
- Department of Ultrasound, The Fifth Medical Center of PLA General Hospital, Beijing, China
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do Nascimento CF, Batista AFDM, Duarte YAO, Chiavegatto Filho ADP. Early identification of older individuals at risk of mobility decline with machine learning. Arch Gerontol Geriatr 2022; 100:104625. [DOI: 10.1016/j.archger.2022.104625] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 01/09/2022] [Accepted: 01/19/2022] [Indexed: 12/20/2022]
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Couto RC, Pedrosa TMG, Seara LM, Couto CS, Couto VS, Giacomin K, de Abreu ACC. Covid-19 vaccination priorities defined on machine learning. Rev Saude Publica 2022; 56:11. [PMID: 35319671 PMCID: PMC9586439 DOI: 10.11606/s1518-8787.2022056004045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 11/09/2021] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE Defining priority vaccination groups is a critical factor to reduce mortality rates. METHODS We sought to identify priority population groups for covid-19 vaccination, based on in-hospital risk of death, by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older), with RT-PCR-confirmed for covid-19, who were hospitalized in any of the 336 Brazilian hospitals considered in this study, from March 19th, 2020, to March 22nd, 2021. Independent variables encompassed age, sex, and chronic health conditions grouped into 179 large categories. Primary outcome was hospital discharge or in-hospital death. Priority population groups for vaccination were formed based on the different levels of in-hospital risk of death due to covid-19, from the ML model developed by taking into consideration the independent variables. All analysis were carried out in Python programming language (version 3.7) and R programming language (version 4.05). RESULTS Patients' mean age was of 60.5 ± 16.8 years (mean ± SD), mean in-hospital mortality rate was 17.9%, and the mean number of comorbidities per patient was 1.97 ± 1.85 (mean ± SD). The predictive model of in-hospital death presented area under the Receiver Operating Characteristic Curve (AUC - ROC) equal to 0.80. The investigated population was grouped into eleven (11) different risk categories, based on the variables chosen by the ML model developed in this study. CONCLUSIONS The use of ML for defining population priorities groups for vaccination, based on risk of in-hospital death, can be easily applied by health system managers.
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Affiliation(s)
- Renato Camargos Couto
- Fundação Lucas MachadoFaculdade de Ciências Médicas de Minas GeraisBelo HorizonteMGBrasilFundação Lucas Machado. Faculdade de Ciências Médicas de Minas Gerais. Belo Horizonte, MG, Brasil
| | - Tania Moreira Grillo Pedrosa
- Fundação Lucas MachadoFaculdade de Ciências Médicas de Minas GeraisBelo HorizonteMGBrasilFundação Lucas Machado. Faculdade de Ciências Médicas de Minas Gerais. Belo Horizonte, MG, Brasil
| | - Luciana Moreira Seara
- Instituto de Acreditação e Gestão em SaúdeDepartamento de Tecnologia da InformaçãoBelo HorizonteMGBrasilInstituto de Acreditação e Gestão em Saúde. Departamento de Tecnologia da Informação. Belo Horizonte, MG, Brasil
| | - Carolina Seara Couto
- Instituto de Assistência Médica ao Servidor Público Estadual de São Paulo.Hospital do Servidor Público EstadualPrograma de Residência MédicaSão PauloSPBrasilInstituto de Assistência Médica ao Servidor Público Estadual de São Paulo. Hospital do Servidor Público Estadual. Programa de Residência Médica. São Paulo, SP, Brasil
| | - Vitor Seara Couto
- Instituto de Acreditação e Gestão em SaúdeDepartamento de Tecnologia da InformaçãoBelo HorizonteMGBrasilInstituto de Acreditação e Gestão em Saúde. Departamento de Tecnologia da Informação. Belo Horizonte, MG, Brasil
| | - Karla Giacomin
- Centro Internacional de LongevidadeBelo HorizonteMGBrasilCentro Internacional de Longevidade. Belo Horizonte, MG, Brasil
| | - Ana Claudia Couto de Abreu
- Instituto de Acreditação e Gestão em SaúdeDepartamento de Tecnologia da InformaçãoBelo HorizonteMGBrasilInstituto de Acreditação e Gestão em Saúde. Departamento de Tecnologia da Informação. Belo Horizonte, MG, Brasil
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Ambriola Oku AY, Zimeo Morais GA, Arantes Bueno AP, Fujita A, Sato JR. Potential Confounders in the Analysis of Brazilian Adolescent's Health: A Combination of Machine Learning and Graph Theory. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 17:ijerph17010090. [PMID: 31877700 PMCID: PMC6981403 DOI: 10.3390/ijerph17010090] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 12/09/2019] [Accepted: 12/16/2019] [Indexed: 12/20/2022]
Abstract
The prevalence of health problems during childhood and adolescence is high in developing countries such as Brazil. Social inequality, violence, and malnutrition have strong impact on youth health. To better understand these issues we propose to combine machine-learning methods and graph analysis to build predictive networks applied to the Brazilian National Student Health Survey (PenSE 2015) data, a large dataset that consists of questionnaires filled by the students. By using a combination of gradient boosting machines and centrality hub metric, it was possible to identify potential confounders to be considered when conducting association analyses among variables. The variables were ranked according to their hub centrality to predict the other variables from a directed weighted-graph perspective. The top five ranked confounder variables were “gender”, “oral health care”, “intended education level”, and two variables associated with nutrition habits—“eat while watching TV” and “never eat fast-food”. In conclusion, although causal effects cannot be inferred from the data, we believe that the proposed approach might be a useful tool to obtain novel insights on the association between variables and to identify general factors related to health conditions.
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Affiliation(s)
- Amanda Yumi Ambriola Oku
- Center of Mathematics, Computing and Cognition—Universidade Federal do ABC, Santo André CEP 09210-580, Brazil
| | | | - Ana Paula Arantes Bueno
- Center of Mathematics, Computing and Cognition—Universidade Federal do ABC, Santo André CEP 09210-580, Brazil
| | - André Fujita
- Institute of Mathematics and Statistics—University of São Paulo, São Paulo CEP 05508-090, Brazil
| | - João Ricardo Sato
- Center of Mathematics, Computing and Cognition—Universidade Federal do ABC, Santo André CEP 09210-580, Brazil
- Correspondence:
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