Integration of outcome estimations with a clinical decision support system: Application in the neonatal intensive care unit (NICU)

Frize, Monique; Gilchrist, Jeff; Martirosyan, Hasmik; Bariciak, Erika

doi:10.1109/memea.2015.7145194

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…Two articles presented the data with sensitivity, specificity, positive predictive value, and negative predictive value. One article presented the data with only sensitivity and specificity [20]. Most of the prediction models were calculated in the first 48 h after the neonate was born.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review

Mangold¹,

Zoretic²,

Thallapureddy³

et al. 2021

Neonatology

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Introduction: Approximately 7,000 newborns die every day, accounting for almost half of child deaths under 5 years of age. Deciphering which neonates are at increased risk for mortality can have an important global impact. As such, integrating high computational technology (e.g., artificial intelligence [AI]) may help identify the early and potentially modifiable predictors of neonatal mortality. Therefore, the objective of this study was to collate, critically appraise, and analyze neonatal prediction studies that included AI. Methods: A literature search was performed in PubMed, Cochrane, OVID, and Google Scholar. We included studies that used AI (e.g., machine learning (ML) and deep learning) to formulate prediction models for neonatal death. We excluded small studies (n < 500 individuals) and studies using only antenatal factors to predict mortality. Two independent investigators screened all articles for inclusion. The data collection consisted of study design, number of models, features used per model, feature importance, internal and/or external validation, and calibration analysis. Our primary outcome was the average area under the receiving characteristic curve (AUC) or sensitivity and specificity for all models included in each study. Results: Of 434 articles, 11 studies were included. The total number of participants was 1.26 M with gestational ages ranging from 22 weeks to term. Number of features ranged from 3 to 66 with timing of prediction as early as 5 min of life to a maximum of 7 days of age. The average number of models per study was 4, with neural network, random forest, and logistic regression comprising the most used models (58.3%). Five studies (45.5%) reported calibration plots and 2 (18.2%) conducted external validation. Eight studies reported results by AUC and 5 studies reported the sensitivity and specificity. The AUC varied from 58.3% to 97.0%. The mean sensitivities ranged from 63% to 80% and specificities from 78% to 99%. The best overall model was linear discriminant analysis, but it also had a high number of features (n = 17). Discussion/Conclusion: ML models can accurately predict death in neonates. This analysis demonstrates the most commonly used predictors and metrics for AI prediction models for neonatal mortality. Future studies should focus on external validation, calibration, as well as deployment of applications that can be readily accessible to health-care providers.

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Section: Resultsmentioning

confidence: 99%

“…The sensitivity was lower in 9 of the 11 models that included sensitivity and specificity. The model with highest sensitivity and specificity was by Frize et al [20] which was an artificial neural network with 18 features and a sensitivity and specificity of 81 and 98%, respectively.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review

Mangold¹,

Zoretic²,

Thallapureddy³

et al. 2021

Neonatology

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“…Artificial Neural Networks (ANNs) were used by Frize et al [19] to build an automated tool predicting neonate's mortality 48 hours after admission (Sensitivity = 81%, Specificity = 98%). And by Saadah et al [20] to predict mortality risk in case of nosocomial outbreaks of RSV(Sensitivity = 82%, Specificity = 100%).…”

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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“…Viewing treatments made by the physicians, and their next plans by parents will help them make critical decisions for their neonate ( 2 ). The work which was done in ( 2 ) was extended in ( 20 ) by using new methodologies and adding new artificial intelligence algorithms for neonate outcome prediction in the NICU. With multi agent systems, we can transform the passive behavior of system components into an autonomous and proactive environment, which will help us to create a high quality fast response system ( 6 ).…”

Section: Introductionmentioning

confidence: 99%

An agent based architecture for high-risk neonate management at neonatal intensive care unit

et al. 2018

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BackgroundIn recent years, the use of new tools and technologies has decreased the neonatal mortality rate. Despite the positive effect of using these technologies, the decisions are complex and uncertain in critical conditions when the neonate is preterm or has a low birth weight or malformations. There is a need to automate the high-risk neonate management process by creating real-time and more precise decision support tools.ObjectiveTo create a collaborative and real-time environment to manage neonates with critical conditions at the NICU (Neonatal Intensive Care Unit) and to overcome high-risk neonate management weaknesses by applying a multi agent based analysis and design methodology as a new solution for NICU management.MethodsThis study was a basic research for medical informatics method development that was carried out in 2017. The requirement analysis was done by reviewing articles on NICU Decision Support Systems. PubMed, Science Direct, and IEEE databases were searched. Only English articles published after 1990 were included; also, a needs assessment was done by reviewing the extracted features and current processes at the NICU environment where the research was conducted. We analyzed the requirements and identified the main system roles (agents) and interactions by a comparative study of existing NICU decision support systems. The Universal Multi Agent Platform (UMAP) was applied to implement a prototype of our multi agent based high-risk neonate management architecture.ResultsLocal environment agents interacted inside a container and each container interacted with external resources, including other NICU systems and consultation centers. In the NICU container, the main identified agents were reception, monitoring, NICU registry, and outcome prediction, which interacted with human agents including nurses and physicians.ConclusionManaging patients at the NICU units requires online data collection, real-time collaboration, and management of many components. Multi agent systems are applied as a well-known solution for management, coordination, modeling, and control of NICU processes. We are currently working on an outcome prediction module using artificial intelligence techniques for neonatal mortality risk prediction. The full implementation of the proposed architecture and evaluation is considered the future work.

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Integration of outcome estimations with a clinical decision support system: Application in the neonatal intensive care unit (NICU)

Cited by 6 publications

References 7 publications

Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review

Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review

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

An agent based architecture for high-risk neonate management at neonatal intensive care unit

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