ANDRE FILIPE DE MORAES BATISTA
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Artigo Científico 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 BATISTAIntrodução: 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.Artigo Científico 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 dosArtigo Científico 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.Artigo Científico 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 OliveiraBackground: 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.Artigo Científico 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.Artigo Científico Early identification of older individuals at risk of mobility decline with machine learning(2022) Nascimento, Carla Ferreira do; ANDRE FILIPE DE MORAES BATISTA; Duarte, Yeda Aparecida Oliveira; Chiavegatto Filho, Alexandre Dias PortoBackground: : The early identification of individuals at risk of mobility decline can improve targeted strategies of prevention. Aims: : To evaluate the predictive performance of machine learning (ML) algorithms in identifying older in dividuals at risk of future mobility decline. Methods: : We used data from the SABE Study (Health, Well-being and Aging Study), a representative sample of people aged 60 years and more, living in the Municipality of São Paulo, Brazil. Mobility decline was assessed 5 years after admission in the study by self-reported difficulty to walk a block, climb steps, being able to stoop, crouch and kneel, or lifting or carrying weights greater than 5 kg. Popular machine learning algorithms were trained in 70% of the sample with 10-fold cross-validation, and predictive performance metrics were obtained from applying the trained algorithms to the other 30% (test set). Results: : Of the 1,615 individuals, 48% developed difficulty in at least one of the four tasks, 32% in stooping, crouching and kneeling, and 30% in carrying weights. The random forest algorithm had the best predictive performance for most outcomes. The tasks that the algorithm was able to predict with better performance were crouching and kneeling (AUC-ROC: 0.81[0.76–0.85]), and lifting or carrying weights (AUC-ROC: 0.80 [0.75–0.84]). Age was the most important variable for the algorithms, followed by education and back pain, according to the SHAP (SHapley Additive exPlanations) values. Conclusion: : Applications of ML algorithms are a promising tool to identify older patients at risk of mobility decline, with the potential of improving targeted preventive programs.Artigo Científico 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 PortoThe 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.Artigo Científico 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.Artigo Científico SNA-Based Reasoning for Multi-Agent Team Composition(2015) ANDRE FILIPE DE MORAES BATISTA; Marietto, Maria das Graças BrunoThe social network analysis (SNA), branch of complex systems can be used in the construction of multi agent systems. This paper proposes a study of how social network analysis can assist in modeling multi agent systems, while addressing similarities and differences between the two theories. We built a prototype of multi-agent systems for resolution of tasks through the formation of teams of agents that are formed on the basis of the social network established between agents. Agents make use of performance indicators to assess when should change their social network to maximize the participation in teams.Trabalho de Evento 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, IanIn 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.