Coleção de Artigos Acadêmicos
URI permanente para esta coleçãohttps://repositorio.insper.edu.br/handle/11224/3227
Navegar
17 resultados
Resultados da Pesquisa
Artigo Científico Resultados por Segmentos: Um estudo aplicado em uma Startup de Tecnologia(2021) Nisiyama, Edelcio Koitiro; Coelho, Fernanda Guimarães; Oyadomari, José Carlos TiomatsuEste relato é baseado nos conceitos de intervenção na realidade de uma empresa com o objetivo de melhorar seu desempenho organizacional. Evidencia-se um contexto comum em empresas PME, ou seja, a necessidade de implementação de relatórios gerenciais de lucratividade por segmentos mostrando soluções para algumas das dificuldades normalmente enfrentadas em sua definição e operacionalização. As empresas analisadas são do setor de tecnologia e o modelo de negócio combina o SAAS (Software as a Service) com a assunção da obrigação de prêmios em pontos. Os clientes pagam pela implantação do software que permite o acesso e a adesão do programa de incentivo/fidelidade. As empresas PME representam a grande maioria das empresas existentes no Brasil cujo nível de mortalidade após 5 anos de existência está próximo dos 60% das empresas e a falta de controle gerencial é um dos principais fatores dessa mortalidade. Nesse contexto de empresas PME, esse relato evidencia a utilização do custeio ABC (Activity-Based Costing) para implementação da análise de lucratividade por segmentos como instrumento de controle gerencial. Em particular, o “Time-Driven ABC”, ou seja, o ABC com base no tempo foi utilizado para reduzir as distorções provocadas por alocações arbitrárias de custos indiretos. Ressalte-se que esse relato apresenta um problema real que foi sanado por meio da participação da Coordenadora Financeira do grupo de empresas em um curso de educação executiva, com posterior intervenção colaborativa com os professores, coautores desse relato.Artigo Científico Custeio Baseado em Atividades em uma Pequena Academia de Ginastica: Estudo Intervencionista(2021) Araujo, Ana Cristina Mendez de; Oyadomari, José Carlos Tiomatsu; Slavov, Tiago Nascimento Borges; Dultra-de-Lima, Ronaldo GomesA gestão de custos dentro de pequenas empresas torna-se uma peça fundamental, pois traz aos gestores informações valiosas e geração de conhecimento em relação ao seu negócio. Este trabalho tem por objetivo apresentar evidências no uso do custeio baseado em atividades (activity-based costing [ABC]) em uma microempresa que atua no segmento de academia de ginástica. Em termos metodológicos, optou-se por por pesquisa intervencionista, sendo os dados coletados por entrevistas, observação in loco e mapeamento de processos para mensuração do objeto de custeio e indicações de um modelo adequado para gestão de custos e preços. O processo teve três etapas: a primeira com a compilação dos dados do fluxo de caixa, com a finalidade de apurar e classificar os gastos da academia; a segunda, para custear as atividades, utilizando como método o de custo por atividade (ABC); e a terceira, a partir de uma survey, identificar o grau de importância e de satisfação de clientes, bem como a percepção de valor por parte deles. Os resultados apontam que, dentre os atributos que tiveram maior grau de satisfação, a qualificação do profissional é o que se destaca; na visão dos clientes, eles estão dispostos a pagar por isso e é esse o elemento que tem maior peso nos custos da academia. O trabalho traz como contribuição a análise de modelo de custeio para academias, tendo como limitação que esse precisa ser testado em outras situações além do caso estudado.Artigo Científico Cultura Organizacional, Nível de Parceria da Controladoria e Sistemas de Avaliação de Desempenho(2021) Bassi, Marília; Russo, Paschoal Tadeu; Oyadomari, José Carlos Tiomatsu; Antunes, Maria TherezaObjetivo: Compreender as associações existentes entre as diferentes intensidades de tipificações de cultura organizacional (CO), níveis de parceria exercidos pelo setor de controladoria (NPC) e a amplitude dos sistemas de avaliação de desempenho organizacional (ASADO). Método: Estudo descritivo, com abordagem quantitativa, por meio de uma survey com 89 respondentes, especialmente controllers, cujas percepções foram avaliadas de acordo com as três variáveis mencionadas, mediante análise multivariada (ANACOR e HOMALS). Resultados: Os resultados revelam que organizações com culturas organizacionais mais fortes estão diretamente associadas a setores de controladoria mais participativos (níveis mais elevados de parceria da controladoria), os quais utilizam sistemas de avaliação de desempenho mais amplos. Mostram, ainda, ausência de associação direta entre cultura organizacional e amplitude de sistemas de avaliação de desempenho. Contribuições: Para a academia, evidenciar a compreensão da associação direta entre NPC e ASADO e a impossibilidade de mostrar associação direta entre CO e ASADO. Com isso, o estudo transcende as usuais abordagens explicativas da Teoria da Contingência. Para a prática profissional, sobretudo para os responsáveis pela área de controladoria, oferece uma visão clara da associação de culturas organizacionais fortes com níveis superiores de parceria da área, e desta com a maior amplitude dos sistemas de avaliação desenvolvidos pela área.Artigo Científico 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.Artigo 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 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 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 A Software to Compare Clusters between Groups and Its Application to the Study of Autism Spectrum Disorder(2017) MACIEL CALEBE VIDAL; Sato, João R.; Balardin, Joana B.; Takahashi, Daniel Y.; Fujita, AndréUnderstanding how brain activities cluster can help in the diagnosis of neuropsychological disorders. Thus, it is important to be able to identify alterations in the clustering structure of functional brain networks. Here, we provide an R implementation of Analysis of Cluster Variability (ANOCVA), which statistically tests (1) whether a set of brain regions of interest (ROI) are equally clustered between two or more populations and (2) whether the contribution of each ROI to the differences in clustering is significant. To illustrate the usefulness of our method and software, we apply the R package in a large functional magnetic resonance imaging (fMRI) dataset composed of 896 individuals (529 controls and 285 diagnosed with ASD—autism spectrum disorder) collected by the ABIDE (The Autism Brain Imaging Data Exchange) Consortium. Our analysis show that the clustering structure of controls and ASD subjects are different (p < 0.001) and that specific brain regions distributed in the frontotemporal, sensorimotor, visual, cerebellar, and brainstem systems significantly contributed (p < 0.05) to this differential clustering. These findings suggest an atypical organization of domain-specific functionbrain modules in ASD.Artigo Científico Identification of alterations associated with age in the clustering structure of functional brain networks(2018) Guzman, Grover E. C.; Sato, Joao R.; MACIEL CALEBE VIDAL; Fujita, AndreInitial studies using resting-state functional magnetic resonance imaging on the trajectories of the brain network from childhood to adulthood found evidence of functional integration and segregation over time. The comprehension of how healthy individuals’ functional integration and segregation occur is crucial to enhance our understanding of possible deviations that may lead to brain disorders. Recent approaches have focused on the framework wherein the functional brain network is organized into spatially distributed modules that have been associated with specific cognitive functions. Here, we tested the hypothesis that the clustering structure of brain networks evolves during development. To address this hypothesis, we defined a measure of how well a brain region is clustered (network fitness index), and developed a method to evaluate its association with age. Then, we applied this method to a functional magnetic resonance imaging data set composed of 397 males under 31 years of age collected as part of the Autism Brain Imaging Data Exchange Consortium. As results, we identified two brain regions for which the clustering change over time, namely, the left middle temporal gyrus and the left putamen. Since the network fitness index is associated with both integration and segregation, our finding suggests that the identified brain region plays a role in the development of brain systems.