ANDRE FILIPE DE MORAES BATISTA

ANDRE FILIPE DE MORAES BATISTA

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Cybersecurity Monitoring/Mapping of USA Healthcare (All Hospitals) - Magnified Vulnerability due to Shared IT Infrastructure, Market Concentration, & Geographical Distribution
(2024) Yurcik, William; Schick, Andreas; North, Stephen; Gastner, Michael T.; FABIO ROBERTO DE MIRANDA; RODOLFO DA SILVA AVELINO; ANDRE FILIPE DE MORAES BATISTA; Pluta, Gregory; Brooks, Ian
In October 2024, there are two defining characteristics of a healthcare provider: (1) geographic location and services available at their physical structure and (2) Internet connectivity and services available via their virtual presence. For previous centuries we focused on the first defining characteristic and now we need to shift to understand and address issues that may arise from the new second defining characteristic. In this paper we address issues related to Internet connectivity and virtual presence of USA healthcare providers, especially hospitals, when ransomware cyberattacks resulting in service outages occur. We show the cybersecurity posture of a large critical national infrastructure (USA healthcare) can be measured, mapped, and quantitatively baselined. Empirical results reveal systemic issues in USA healthcare presenting "magnified vulnerabilities" in that a single exploit can have an outsized impact on an entire nationwide infrastructure. As the initial step toward addressing this issue, we document for the first time the magnified cybersecurity vulnerability of USA healthcare to shared IT infrastructure, market concentration, and the geographical distribution of hospitals.
Data-driven decision making for the screening of cognitive impairment in primary care: a machine learning approach using data from the ELSA-Brasil study
(2023) Szlejf, C.; ANDRE FILIPE DE MORAES BATISTA; Bertola, L.; Lotufo, P.A.; Benseñor, I.M.; Chiavegatto Filho, A.D.P.; Suemoto, C.K.
The systematic assessment of cognitive performance of older people without cognitive complaints is controversial and unfeasible. Identifying individuals at higher risk of cognitive impairment could optimize resource allocation. We aimed to develop and test machine learning models to predict cognitive impairment using variables obtainable in primary care settings. In this cross-sectional study, we included 8,291 participants of the baseline assessment of the ELSA-Brasil study, who were aged between 50 and 74 years and were free of dementia. Cognitive performance was assessed with a neuropsychological battery and cognitive impairment was defined as global cognitive z-score below 2 standard deviations. Variables used as input to the prediction models included demographics, social determinants, clinical conditions, family history, lifestyle, and laboratory tests. We developed machine learning models using logistic regression, neural networks, and gradient boosted trees. Participants’ mean age was 58.3±6.2 years, 55% were female. Cognitive impairment was present in 328 individuals (4%). Machine learning algorithms presented fair to good discrimination (areas under the ROC curve between 0.801 and 0.873). Extreme Gradient Boosting presented the highest discrimination, high specificity (97%), and negative predictive value (97%). Seventy-six percent of the individuals with cognitive impairment were included among the highest ranked individuals by this algorithm. In conclusion, we developed and tested a machine learning model to predict cognitive impairment based on primary care data that presented good discrimination and high specificity. These characteristics could support the detection of patients who would not benefit from cognitive assessment, facilitating the allocation of human and economic resources.
Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study
(2022) Wichmann, Roberta Moreira; Fagundes, Thales Pardini; Oliveira, Tiago Almeida de; ANDRE FILIPE DE MORAES BATISTA; Chiavegatto Filho, Alexandre Dias Porto
Artificial intelligence (AI) algorithms are transforming several areas of the digital world and are increasingly being applied in healthcare. Mobile apps based on predictive machine learning models have the potential to improve health outcomes, but there is still no consensus on how to inform doctors about their results. The aim of this study was to investigate how healthcare professionals prefer to receive predictions generated by machine learning algorithms. A systematic search in MEDLINE, via PubMed, EMBASE and Web of Science was first performed. We developed a mobile app, RandomIA, to predict the occurrence of clinical outcomes, initially for COVID-19 and later expected to be expanded to other diseases. A questionnaire called System Usability Scale (SUS) was selected to assess the usability of the mobile app. A total of 69 doctors from the five regions of Brazil tested RandomIA and evaluated three different ways to visualize the predictions. For prognostic outcomes (mechanical ventilation, admission to an intensive care unit, and death), most doctors (62.9%) preferred a more complex visualization, represented by a bar graph with three categories (low, medium, and high probability) and a probability density graph for each outcome. For the diagnostic prediction of COVID-19, there was also a majority preference (65.4%) for the same option. Our results indicate that doctors could be more inclined to prefer receiving detailed results from predictive machine learning algorithms.
Predictors of tooth loss: A machine learning approach
(2021) Elani, Hawazin W.; ANDRE FILIPE DE MORAES BATISTA; W. Murray Thomson; Kawachi, Ichiro; Chiavegatto Filho, Alexandre D. P.
Introduction Little is understood about the socioeconomic predictors of tooth loss, a condition that can negatively impact individual’s quality of life. The goal of this study is to develop a machine-learning algorithm to predict complete and incremental tooth loss among adults and to compare the predictive performance of these models. Methods We used data from the National Health and Nutrition Examination Survey from 2011 to 2014. We developed multiple machine-learning algorithms and assessed their predictive performances by examining the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values. Results The extreme gradient boosting trees presented the highest performance in the prediction of edentulism (AUC = 88.7%; 95%CI: 87.1, 90.2), the absence of a functional dentition (AUC = 88.3% 95%CI: 87.3,89.3) and for predicting missing any tooth (AUC = 83.2%; 95%CI, 82.0, 84.4). Although, as expected, age and routine dental care emerged as strong predictors of tooth loss, the machine learning approach identified additional predictors, including socioeconomic conditions. Indeed, the performance of models incorporating socioeconomic characteristics was better at predicting tooth loss than those relying on clinical dental indicators alone. Conclusions Future application of machine-learning algorithm, with longitudinal cohorts, for identification of individuals at risk for tooth loss could assist clinicians to prioritize interventions directed toward the prevention of tooth loss.
Neonatal mortality prediction with routinely collected data: a machine learning approach
(2021) ANDRE FILIPE DE MORAES BATISTA; Diniz, Carmen S. G.; Bonilha, Eliana A.; Kawachi, Ichiro; Chiavegatto Filho, Alexandre D. P.
Background: Recent decreases in neonatal mortality have been slower than expected for most countries. This study aims to predict the risk of neonatal mortality using only data routinely available from birth records in the largest city of the Americas. Methods: A probabilistic linkage of every birth record occurring in the municipality of São Paulo, Brazil, between 2012 e 2017 was performed with the death records from 2012 to 2018 (1,202,843 births and 447,687 deaths), and a total of 7282 neonatal deaths were identified (a neonatal mortality rate of 6.46 per 1000 live births). Births from 2012 and 2016 (N = 941,308; or 83.44% of the total) were used to train five different machine learning algorithms, while births occurring in 2017 (N = 186,854; or 16.56% of the total) were used to test their predictive performance on new unseen data. Results: The best performance was obtained by the extreme gradient boosting trees (XGBoost) algorithm, with a very high AUC of 0.97 and F1-score of 0.55. The 5% births with the highest predicted risk of neonatal death included more than 90% of the actual neonatal deaths. On the other hand, there were no deaths among the 5% births with the lowest predicted risk. There were no significant differences in predictive performance for vulnerable subgroups. The use of a smaller number of variables (WHO’s five minimum perinatal indicators) decreased overall performance but the results still remained high (AUC of 0.91). With the addition of only three more variables, we achieved the same predictive performance (AUC of 0.97) as using all the 23 variables originally available from the Brazilian birth records. Conclusion: Machine learning algorithms were able to identify with very high predictive performance the neonatal mortality risk of newborns using only routinely collected data.
A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil
(2021) Fernandes, Fernando Timoteo; Oliveira, Tiago Almeida de; Teixeira, Cristiane Esteves; ANDRE FILIPE DE MORAES BATISTA; Costa, Gabriel Dalla; Chiavegatto Filho, Alexandre Dias Porto
The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countries. This study proposes to predict the risk of developing critical conditions in COVID-19 patients by training multipurpose algorithms. We followed a total of 1040 patients with a positive RT-PCR diagnosis for COVID-19 from a large hospital from São Paulo, Brazil, from March to June 2020, of which 288 (28%) presented a severe prognosis, i.e. Intensive Care Unit (ICU) admission, use of mechanical ventilation or death. We used routinely-collected laboratory, clinical and demographic data to train five machine learning algorithms (artificial neural networks, extra trees, random forests, catboost, and extreme gradient boosting). We used a random sample of 70% of patients to train the algorithms and 30% were left for performance assessment, simulating new unseen data. In order to assess if the algorithms could capture general severe prognostic patterns, each model was trained by combining two out of three outcomes to predict the other. All algorithms presented very high predictive performance (average AUROC of 0.92, sensitivity of 0.92, and specificity of 0.82). The three most important variables for the multipurpose algorithms were ratio of lymphocyte per C-reactive protein, C-reactive protein and Braden Scale. The results highlight the possibility that machine learning algorithms are able to predict unspecific negative COVID-19 outcomes from routinely-collected data.
How Reliable Are The Screening Tools As A Triage Element For The Application Of The Global Leadership Initiative On Malnutrition (Glim)? Prospective Multicenter Observational Study
(2023) Lopes, G.G.; Piovacari, S. M. F.; Moraes, J. R.; Santos, H. B. C.; Rakovicius, A. K. Z.; ANDRE FILIPE DE MORAES BATISTA; Pereira, A.J.
Rationale: The international GLIM guideline recommends on the use of nutritional screening for the diagnosis of hospital malnutrition. However, as clinical outcomes were not included in the original validation of these instruments and their sensitivity (true positive rate) is unknown, it is not possible to report a chance of a truly at nutritional risk patient not being identified by screening and consequently not being evaluated by the GLIM. Methods: Multicenter, prospective observational trial in 3 Brazilian tertiary hospitals, carried out between December/21 to February/22. A convenience sample was used based on patients with expected length of stay at hospital longer than 48 hours. Pregnant women, under 18 years old, palliative care, lymphedema and muscle atrophy of neurological causes patients were excluded. NRS 2002, MNA-SF and ESPEN 2019 tools were applied to the specific populations (following international guidelines) by trained and validated Dietitians. Hospital mortality data were extracted from the local electronic medical records. Results: 676 patients were included, 54% male, 90% white with a mean age of 63 years (SD: ±21) and BMI of 27.50 kg/m2 (SD: ±4.72), hospitalized in the wards (68%). The most used nutritional screening was NRS 2002 (52%). In the sample, 39% were at nutritional risk, of these 5.6% died. An overview of nutritional screening tools’ performance are shown in the Table. In addition, accuracy found was 0.59 and area under the curve was 0,69 for predicting in-hospital deaths.
Proposição e validação de ferramenta de avaliação da desnutrição hospitalar, com base no Global Leadership Initiative on Malnutrition: protocolo do Estudo GLIM-BR
(2022) Lopes, Giovanna Guimarães; Piovacari, Silvia Maria Fraga; Pereira, Adriano José; ANDRE FILIPE DE MORAES BATISTA
Introdução: O consenso do Global Leadership Initiative on Malnutrition (GLIM) publicou uma proposta de diretrizes para estruturação de uma ferramenta, composta por critérios diagnósticos fenotípicos e etiológicos. Posteriormente, foi publicado o Guia de Validação do GLIM, que visa estimular e direcionar iniciativas de validação desta nova ferramenta. O objetivo deste artigo é compartilhar o protocolo do Estudo GLIM-BR, em andamento, o qual irá propor e validar um novo instrumento nacional de classificação diagnóstica de desnutrição hospitalar baseado no GLIM, utilizando desfechos clínicos. Método: Estudo de validação prospectivo observacional multicêntrico, dividido em 3 fases, realizado em 4 hospitais brasileiros terciários, com pacientes internados e com expectativa de permanência maior que 48h. Modelos preditivos baseados em aprendizado de máquina/inteligência artificial serão utilizados na definição do conjunto ótimo de variáveis e cutoffs capazes de predizer desfechos clínicos, como óbito e tempo de permanência. Resultados: Na fase 1, das 12 variáveis apresentadas e discutidas no painel de opinião de especialistas, 8 tiveram aprovação sem alteração. As demais variáveis foram ajustadas por meio do consenso Grupo GLIM-BR. Todas as variáveis foram escolhidas com base na literatura atual (racional teórico) e utilizando, sempre que possível, outras ferramentas já validadas. A Fase 2 já possui resultados preliminares (subestudo) apresentados em Congresso internacional (ESPEN) e que serão submetidos para publicação em periódico científico internacional nos próximos meses. A Fase 3 está em curso e as variáveis de interesse selecionadas para serem avaliadas pelo modelo preditivo do estudo GLIM-BR, em cada uma das categorias propostas pelo GLIM, estão divulgadas neste artigo, juntamente com detalhes do protocolo de pesquisa em curso. Conclusão: Almeja-se desenvolver uma ferramenta validada para diagnóstico da desnutrição hospitalar, que contorne limitações identificadas em ferramentas de avaliação nutricional vigentes, prática e pronta para uso pela comunidade de nutricionistas nos serviços hospitalares.
Data Leakage in Health Outcomes Prediction With Machine Learning. Comment on “Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning"
(2021) Chiavegatto Filho, Alexandre; ANDRE FILIPE DE MORAES BATISTA; Santos, Hellen Geremias dos
Cause-specific mortality prediction in older residents of São Paulo, Brazil: a machine learning approach
(2021) Nascimento, Carla Ferreira do; Hellen Geremias dos Santos; ANDRE FILIPE DE MORAES BATISTA; Lay, Alejandra Andrea Roman; Duarte, Yeda Aparecida Oliveira
Background: Populational ageing has been increasing in a remarkable rate in developing countries. In this scenario, preventive strategies could help to decrease the burden of higher demands for healthcare services. Machine learning algorithms have been increasingly applied for identifying priority candidates for preventive actions, presenting a better predictive performance than traditional parsimonious models. Methods: Data were collected from the Health, Well Being and Aging (SABE) Study, a representative sample of older residents of São Paulo, Brazil. Machine learning algorithms were applied to predict death by diseases of respiratory system (DRS), diseases of circulatory system (DCS), neoplasms and other specific causes within 5 years, using socioeconomic, demographic and health features. The algorithms were trained in a random sample of 70% of subjects, and then tested in the other 30% unseen data. Results: The outcome with highest predictive performance was death by DRS (AUC−ROC = 0.89), followed by the other specific causes (AUC−ROC = 0.87), DCS (AUC−ROC = 0.67) and neoplasms (AUC−ROC = 0.52). Among only the 25% of individuals with the highest predicted risk of mortality from DRS were included 100% of the actual cases. The machine learning algorithms with the highest predictive performance were light gradient boosted machine and extreme gradient boosting. Conclusion: The algorithms had a high predictive performance for DRS, but lower for DCS and neoplasms. Mortality prediction with machine learning can improve clinical decisions especially regarding targeted preventive measures for older individuals.