Neonatal intensive care decision support systems using artificial intelligence techniques: a systematic review

Malak, Jaleh Shoshtarian; Zeraati, Hojjat; Nayeri, Fatemeh; Safdari, Reza; Shahraki, Azimeh Danesh

doi:10.1007/s10462-018-9635-1

Cited by 33 publications

(18 citation statements)

References 63 publications

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“…Since several studies have analyzed medical imaging data such as computed tomography and magnetic resonance imaging scans and radiographs by using deep learning and machine learning, recent studies have developed prediction models for the early diagnosis of bloodstream infections and symptomatic systemic inflammatory response syndrome in newborns [ 7 - 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study

et al. 2020

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Background Neonatal sepsis is associated with most cases of mortalities and morbidities in the neonatal intensive care unit (NICU). Many studies have developed prediction models for the early diagnosis of bloodstream infections in newborns, but there are limitations to data collection and management because these models are based on high-resolution waveform data. Objective The aim of this study was to examine the feasibility of a prediction model by using noninvasive vital sign data and machine learning technology. Methods We used electronic medical record data in intensive care units published in the Medical Information Mart for Intensive Care III clinical database. The late-onset neonatal sepsis (LONS) prediction algorithm using our proposed forward feature selection technique was based on NICU inpatient data and was designed to detect clinical sepsis 48 hours before occurrence. The performance of this prediction model was evaluated using various feature selection algorithms and machine learning models. Results The performance of the LONS prediction model was found to be comparable to that of the prediction models that use invasive data such as high-resolution vital sign data, blood gas estimations, blood cell counts, and pH levels. The area under the receiver operating characteristic curve of the 48-hour prediction model was 0.861 and that of the onset detection model was 0.868. The main features that could be vital candidate markers for clinical neonatal sepsis were blood pressure, oxygen saturation, and body temperature. Feature generation using kurtosis and skewness of the features showed the highest performance. Conclusions The findings of our study confirmed that the LONS prediction model based on machine learning can be developed using vital sign data that are regularly measured in clinical settings. Future studies should conduct external validation by using different types of data sets and actual clinical verification of the developed model.

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Section: Introductionmentioning

confidence: 99%

A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study

et al. 2020

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“…Specifically, artificial neural networks (ANN) “learn” from training data and allow a robust and more accurate estimation of complex patterns. Such tools are currently being used in medical decision support systems [ 23 , 24 ]. Therefore, the aim of this study was to develop models to estimate intestinal perforation in premature neonates with NEC, based on ANN, evaluating maternal and patients’ multifaceted variables, at birth and during hospitalization.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Neonatal Intestinal Perforation Associated with Necrotizing Enterocolitis by Machine Learning Reveals New Key Factors

Irles

González‐Pérez

Muiños

et al. 2018

IJERPH

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Intestinal perforation (IP) associated with necrotizing enterocolitis (NEC) is one of the leading causes of mortality in premature neonates; with major nutritional and neurodevelopmental sequelae. Since predicting which neonates will develop perforation is still challenging; clinicians might benefit considerably with an early diagnosis tool and the identification of critical factors. The aim of this study was to forecast IP related to NEC and to investigate the predictive quality of variables; based on a machine learning-based technique. The Back-propagation neural network was used to train and test the models with a dataset constructed from medical records of the NICU; with birth and hospitalization maternal and neonatal clinical; feeding and laboratory parameters; as input variables. The outcome of the models was diagnosis: (1) IP associated with NEC; (2) NEC or (3) control (neither IP nor NEC). Models accurately estimated IP with good performances; the regression coefficients between the experimental and predicted data were R2 > 0.97. Critical variables for IP prediction were identified: neonatal platelets and neutrophils; orotracheal intubation; birth weight; sex; arterial blood gas parameters (pCO2 and HCO3); gestational age; use of fortifier; patent ductus arteriosus; maternal age and maternal morbidity. These models may allow quality improvement in medical practice.

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“…According to the matching requirements, select a certain number of characteristic nodes in each block area to organize, build the set of characteristic points, and then use multi threads to build the set of characteristic vectors. Then, the feature vector set is processed by two-way matching and eliminating mismatches [ 7 , 8 ]. Finally, according to whether the number of matching points is greater than the set detection value, if it is greater than, the matching is successful.…”

Section: The Automatic Detection Methods Of Shooting Action Teaching Imentioning

confidence: 99%

Automatic Detection of Image Features in Basketball Shooting Teaching Based on Artificial Intelligence

Yu¹,

Liu

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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In order to weaken the limitation between the adjacent nodes of shooting action teaching image and fully expose the necessary image features, an automatic detection method of shooting action teaching image features based on artificial intelligence is proposed. Starting from the decomposition of shooting teaching action, the practical attribute of shooting teaching image is defined, and then the standardized image detection environment based on artificial intelligence is built according to the action essentials of consistent shooting. On this basis, through the way of extracting the characteristics of teaching image, calculating the characteristic scale value of shooting action, combining with the feature description principle of automatic detection, the design of automatic detection method of shooting action teaching image characteristics is completed. The experimental results show that, compared with the traditional feature method, the UTI traction coefficient between the adjacent nodes of the image increases to 0.33, while the matching time required for detection decreases to 17.5 mm, so as to fully expose the necessary features of the shooting action teaching image.

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Neonatal intensive care decision support systems using artificial intelligence techniques: a systematic review

Cited by 33 publications

References 63 publications

A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study

A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study

Estimation of Neonatal Intestinal Perforation Associated with Necrotizing Enterocolitis by Machine Learning Reveals New Key Factors

Automatic Detection of Image Features in Basketball Shooting Teaching Based on Artificial Intelligence

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