NICeSim: An open-source simulator based on machine learning techniques to support medical research on prenatal and perinatal care decision making

Cerqueira, Fábio Ribeiro; Ferreira, Tiago Geraldo; Oliveira, Alcione de Paiva; Augusto, Douglas Adriano; Krempser, Eduardo; Barbosa, Hélio J. C.; Franceschini, Sylvia do Carmo Castro; Freitas, Brunnella Alcântara Chagas de; Gomes, Andréia Patrícia; Siqueira-Batista, Rodrigo

doi:10.1016/j.artmed.2014.10.001

Cited by 21 publications

(27 citation statements)

References 17 publications

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“…A somehow different approach can be found in the use of ML methods for the development of MDSS for neonatal critical care assistance by Cerqueira et al [ 45 ], where the explicit target is the prediction of the RoD for newborns admitted to NICUs, but where the emphasis is placed on the ML pipeline of the MDSS (including data preprocessing) as a simulation tool to investigate the problem beyond actual prediction. The proposed MDSS uses ANN (standard MLP) and SVM classifiers.…”

Section: Methodsmentioning

confidence: 99%

Machine learning in critical care: state-of-the-art and a sepsis case study

Vellido

Ribas²,

Carles

et al. 2018

BioMed Eng OnLine

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BackgroundLike other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the daunting task of extracting usable knowledge from these data using algorithmic methods. In the medical context this may for instance realized through the design of medical decision support systems for diagnosis, prognosis and patient management. The intensive care unit (ICU), and by extension the whole area of critical care, is becoming one of the most data-driven clinical environments.ResultsThe increasing availability of complex and heterogeneous data at the point of patient attention in critical care environments makes the development of fresh approaches to data analysis almost compulsory. Computational Intelligence (CI) and Machine Learning (ML) methods can provide such approaches and have already shown their usefulness in addressing problems in this context. The current study has a dual goal: it is first a review of the state-of-the-art on the use and application of such methods in the field of critical care. Such review is presented from the viewpoint of the different subfields of critical care, but also from the viewpoint of the different available ML and CI techniques. The second goal is presenting a collection of results that illustrate the breath of possibilities opened by ML and CI methods using a single problem, the investigation of septic shock at the ICU.ConclusionWe have presented a structured state-of-the-art that illustrates the broad-ranging ways in which ML and CI methods can make a difference in problems affecting the manifold areas of critical care. The potential of ML and CI has been illustrated in detail through an example concerning the sepsis pathology. The new definitions of sepsis and the relevance of using the systemic inflammatory response syndrome (SIRS) in its diagnosis have been considered. Conditional independence models have been used to address this problem, showing that SIRS depends on both organ dysfunction measured through the Sequential Organ Failure (SOFA) score and the ICU outcome, thus concluding that SIRS should still be considered in the study of the pathophysiology of Sepsis. Current assessment of the risk of dead at the ICU lacks specificity. ML and CI techniques are shown to improve the assessment using both indicators already in place and other clinical variables that are routinely measured. Kernel methods in particular are shown to provide the best performance balance while being amenable to representation through graphical models, which increases their interpretability and, with it, their likelihood to be accepted in medical practice.

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Section: Methodsmentioning

confidence: 99%

Machine learning in critical care: state-of-the-art and a sepsis case study

Vellido

Ribas²,

Carles

et al. 2018

BioMed Eng OnLine

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“…According to Cerqueira et al (2014) , the main characteristics of NICeSim are flexibility, since it permits the inclusion and exclusion of variables according to the requirements of a particular study, and dynamism, as it allows the training of a just in time model. Therefore, the system can be improved with new data when they become available.…”

Section: Processing and Analysis Of Resultsmentioning

confidence: 99%

The use of Artificial Neural Networks in the diagnosis of manageable factors on Brazilian Primary Health Care

Cabral

Cerqueira

Siqueira-Batista

et al. 2021

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The social determinants of health are relevant in the main strategies of Primary Health Care. However, it is known the difficulties of the health sector to overcome the factors that negatively interfere with the health of the population. Thus, it was aimed to create a computer model to present in detail the factors that somehow are related to the Primary Health Care, enabling public health managers to make decisions efficiently. Using artificial neural networks, it was possible to create a classifier model that could show which variables are related to the efficiency in Primary Care and which lead to inefficiency. Moreover, it was used the NICeSim simulator as a tool to evaluate the behavior of each variable identified as relevant to the efficiency in Primary Care of cities. The results demonstrate that the created model was superior to previously proposed models. Furthermore, our model has been demonstrated to be very effective in identifying variables that affect Primary Health. The created model shows that factors, such as illiteracy and welfare programs, considerably affect the efficiency of health care, reinforcing the argument that the focus of the public policies should be dealt in an intersectoral way, improving the factors that positively influence the population health.

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“…And by Saadah et al [20] to predict mortality risk in case of nosocomial outbreaks of RSV(Sensitivity = 82%, Specificity = 100%). Cerqueira et al www.ijacsa.thesai.org [21] also used ANN and Support Vector Machine (SVM) to design NICeSim which is an open-source simulator based on machine learning techniques to predict death probability of newborns(accuracy = 86.7%, AUC = 0.84).…”

Section: B Machine Learning Techniquesmentioning

confidence: 99%

New Approach based on Machine Learning for Short-Term Mortality Prediction in Neonatal Intensive Care Unit

Kefi¹,

Aloui²,

Naceur³

2019

IJACSA

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Mortality remains one of the most important outcomes to predict in Intensive Care Units (ICUs). In fact, the sooner mortality is predicted, the better critical decisions are made by doctors based on patient's illness severity. In this paper, a new approach based on Machine Learning (ML) techniques for short-term mortality prediction in Neonatal Intensive Care Unit (NICU) is proposed. This approach relies on many steps. At first, relevant features are selected from available data upon neonates' admission and from the time-series variables collected within the two first hours of stay in the NICU from the Medical Information Mart for Intensive Care III (MIMIC-III). After that, to predict mortality, many classifiers were tested which are Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF). The experimental results showed that LDA was the best performing classifier with an accuracy equal to 0.947 and AUROC equal to 0.97 with 31 features. The third step of this approach is mortality time prediction using the Galaxy-Random Forest method achieving an f-score equal to 0.871. The proposed approach compared favorably in terms of time, accuracy and AUROC with existing scoring systems and ML techniques. It is the first work predicting neonates mortality based on ML techniques and time-series data after only two hours of admission to the NICU.

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NICeSim: An open-source simulator based on machine learning techniques to support medical research on prenatal and perinatal care decision making

Cited by 21 publications

References 17 publications

Machine learning in critical care: state-of-the-art and a sepsis case study

Machine learning in critical care: state-of-the-art and a sepsis case study

The use of Artificial Neural Networks in the diagnosis of manageable factors on Brazilian Primary Health Care

New Approach based on Machine Learning for Short-Term Mortality Prediction in Neonatal Intensive Care Unit

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