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Artif Intell Med. 2014 Nov;62(3):193-201. doi: 10.1016/j.artmed.2014.10.001. Epub 2014 Oct 22.

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

Author information

1
Department of Informatics, Universidade Federal de Viçosa, Av. PH Rolfs - s/n, CEP 36570-900 Minas Gerais, Brazil. Electronic address: fabio.cerqueira@ufv.br.
2
Department of Informatics, Universidade Federal de Viçosa, Av. PH Rolfs - s/n, CEP 36570-900 Minas Gerais, Brazil.
3
National Laboratory for Scientific Computing (LNCC), Av. Getúlio Vargas, 333, Bairro Quitandinha, CEP 25651-075 Petrópolis, RJ, Brazil.
4
National Laboratory for Scientific Computing (LNCC), Av. Getúlio Vargas, 333, Bairro Quitandinha, CEP 25651-075 Petrópolis, RJ, Brazil; Faculdade de Educação Tecnológica do Estado do Rio de Janeiro, Av. Getúlio Vargas, 335, Bairro Quitandinha, CEP 25651-075 Petrópolis, RJ, Brazil.
5
Department of Nutrition and Health, Universidade Federal de Viçosa, Av. PH Rolfs - s/n, CEP 36570-900 Minas Gerais, Brazil.
6
Department of Medicine and Nursing, Universidade Federal de Viçosa, Av. PH Rolfs - s/n, CEP 36570-900 Minas Gerais, Brazil.

Abstract

OBJECTIVE:

This paper describes NICeSim, an open-source simulator that uses machine learning (ML) techniques to aid health professionals to better understand the treatment and prognosis of premature newborns.

METHODS:

The application was developed and tested using data collected in a Brazilian hospital. The available data were used to feed an ML pipeline that was designed to create a simulator capable of predicting the outcome (death probability) for newborns admitted to neonatal intensive care units. However, unlike previous scoring systems, our computational tool is not intended to be used at the patients bedside, although it is possible. Our primary goal is to deliver a computational system to aid medical research in understanding the correlation of key variables with the studied outcome so that new standards can be established for future clinical decisions. In the implemented simulation environment, the values of key attributes can be changed using a user-friendly interface, where the impact of each change on the outcome is immediately reported, allowing a quantitative analysis, in addition to a qualitative investigation, and delivering a totally interactive computational tool that facilitates hypothesis construction and testing.

RESULTS:

Our statistical experiments showed that the resulting model for death prediction could achieve an accuracy of 86.7% and an area under the receiver operating characteristic curve of 0.84 for the positive class. Using this model, three physicians and a neonatal nutritionist performed simulations with key variables correlated with chance of death. The results indicated important tendencies for the effect of each variable and the combination of variables on prognosis. We could also observe values of gestational age and birth weight for which a low Apgar score and the occurrence of respiratory distress syndrome (RDS) could be less or more severe. For instance, we have noticed that for a newborn with 2000 g or more the occurrence of RDS is far less problematic than for neonates weighing less.

CONCLUSIONS:

The significant accuracy demonstrated by our predictive model shows that NICeSim might be used for hypothesis testing to minimize in vivo experiments. We observed that the model delivers predictions that are in very good agreement with the literature, demonstrating that NICeSim might be an important tool for supporting decision making in medical practice. Other very important characteristics of NICeSim are its flexibility and dynamism. NICeSim is flexible because it allows the inclusion and deletion of variables according to the requirements of a particular study. It is also dynamic because it trains a just-in-time model. Therefore, the system is improved as data from new patients become available. Finally, NICeSim can be extended in a cooperative manner because it is an open-source system.

KEYWORDS:

Artificial neural networks; Clinical decision making; Machine learning in medicine; Perinatal care; Prenatal care; Support vector machine

PMID:
25457563
DOI:
10.1016/j.artmed.2014.10.001
[Indexed for MEDLINE]

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