Are Artificial Neural Networks “Ready to Use” for Decision Making in the Neonatal Intensive Care Unit?: Commentary on the article by Mueller et al. and page 11

Cr, Walker; Frize, Monique

doi:10.1203/01.pdr.0000129654.02381.b9

Cited by 9 publications

(4 citation statements)

References 14 publications

(9 reference statements)

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“…There are multiple forms of boosting: stochastic, adaptive, and gradient Ref. 41 Bagged (Bootstrap Aggregating) Complex Trees…”

Section: Mechanical Ventilationmentioning

confidence: 99%

“…36 AI and natural language processing of physician documentation have been utilized to predict mortality rates in the SICU. 41 AI was trained on physician notes and/or the Oxford Acute Severity of Illness Score, retrieved from the MIMIC III database. This study assessed 3,838 SICU stays and found AI, that was trained on physician notes with severity scores, had an AUC of 0.88 and accuracy of 94.6%.…”

Section: Nicu and Sicumentioning

confidence: 99%

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AI in the Intensive Care Unit: Up-to-Date Review

Nguyen

Ngo

vanSonnenberg

2020

J Intensive Care Med

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AI is the latest technologic trend that likely will have a huge impact in medicine. AI’s potential lies in its ability to process large volumes of data and perform complex pattern analyses. The ICU is an area of medicine that is particularly conducive to AI applications. Much AI ICU research currently is focused on improving high volumes of data on high-risk patients and making clinical workflow more efficient. Emerging topics of AI medicine in the ICU include AI sensors, sepsis prediction, AI in the NICU or SICU, and the legal role of AI in medicine. This review will cover the current applications of AI medicine in the ICU, potential pitfalls, and other AI medicine-related topics relevant for the ICU.

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“…There are multiple forms of boosting: stochastic, adaptive, and gradient Ref. 41 Bagged (Bootstrap Aggregating) Complex Trees…”

Section: Mechanical Ventilationmentioning

confidence: 99%

Section: Nicu and Sicumentioning

confidence: 99%

AI in the Intensive Care Unit: Up-to-Date Review

Nguyen

Ngo

vanSonnenberg

2020

J Intensive Care Med

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“…In the biological domain, ANN application to samples' characterization, identification and interaction include: interpreting pyrolysis mass spectrometery, GC and HPLC data; pattern recognition of DNA, RNA, protein structure and microscopic images; prediction of microbial growth, biomass and shelf-life of food products; and identification of microorganisms and molecules [2]. In the medical and behavioral sciences, image analysis has resulted in systems capable of diagnosis and prognosis of various diseases, (including cancer), classification of cancer subtypes, predicting tumor sensitivity to drugs, identification of potential biomarkers, analysis of gene expression data, medical imaging and radiological diagnosis, analysis of wave forms, outcome prediction, identification of pathological specimens, interpretation of laboratory data, evaluation of epidemiologic data, waveform analysis (including electroencephalography, electromyogram, electrocardiogram and Doppler ultrasound), length of stay in intensive care units following various diseases/surgery, and predicting admission decisions in psychiatric wards [25-30]. …”

Section: General Application and Improving Performance Of Annsmentioning

confidence: 99%

Artificial neural networks for diagnosis and survival prediction in colon cancer

Ahmed

2005

Mol Cancer

165

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ANNs are nonlinear regression computational devices that have been used for over 45 years in classification and survival prediction in several biomedical systems, including colon cancer. Described in this article is the theory behind the three-layer free forward artificial neural networks with backpropagation error, which is widely used in biomedical fields, and a methodological approach to its application for cancer research, as exemplified by colon cancer. Review of the literature shows that applications of these networks have improved the accuracy of colon cancer classification and survival prediction when compared to other statistical or clinicopathological methods. Accuracy, however, must be exercised when designing, using and publishing biomedical results employing machine-learning devices such as ANNs in worldwide literature in order to enhance confidence in the quality and reliability of reported data.

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“…Artificial Intelligence, particularly artificial neural network (ANN), is a powerful and rigorous tool for the analysis of non-linear and highly complex relationships that learns from all data including experimental and clinical variables, by allowing a very accurate estimation of parameters [ 26 , 27 ]. ANN has been applied in the perinatal field for diagnosis, data mining and clinical decision [ 28 , 29 , 30 , 31 , 32 ]. However, currently no mathematical models have been reported that predict maternal blood biomarkers from clinical and anthropometric data.…”

Section: Introductionmentioning

confidence: 99%

Key Clinical Factors Predicting Adipokine and Oxidative Stress Marker Concentrations among Normal, Overweight and Obese Pregnant Women Using Artificial Neural Networks

Solis-Paredes

Estrada-Gutiérrez

Perichart-Perera

et al. 2017

IJMS

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Maternal obesity has been related to adverse neonatal outcomes and fetal programming. Oxidative stress and adipokines are potential biomarkers in such pregnancies; thus, the measurement of these molecules has been considered critical. Therefore, we developed artificial neural network (ANN) models based on maternal weight status and clinical data to predict reliable maternal blood concentrations of these biomarkers at the end of pregnancy. Adipokines (adiponectin, leptin, and resistin), and DNA, lipid and protein oxidative markers (8-oxo-2′-deoxyguanosine, malondialdehyde and carbonylated proteins, respectively) were assessed in blood of normal weight, overweight and obese women in the third trimester of pregnancy. A Back-propagation algorithm was used to train ANN models with four input variables (age, pre-gestational body mass index (p-BMI), weight status and gestational age). ANN models were able to accurately predict all biomarkers with regression coefficients greater than R2 = 0.945. P-BMI was the most significant variable for estimating adiponectin and carbonylated proteins concentrations (37%), while gestational age was the most relevant variable to predict resistin and malondialdehyde (34%). Age, gestational age and p-BMI had the same significance for leptin values. Finally, for 8-oxo-2′-deoxyguanosine prediction, the most significant variable was age (37%). These models become relevant to improve clinical and nutrition interventions in prenatal care.

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Are Artificial Neural Networks “Ready to Use” for Decision Making in the Neonatal Intensive Care Unit?: Commentary on the article by Mueller et al. and page 11

Cited by 9 publications

References 14 publications

AI in the Intensive Care Unit: Up-to-Date Review

AI in the Intensive Care Unit: Up-to-Date Review

Artificial neural networks for diagnosis and survival prediction in colon cancer

Key Clinical Factors Predicting Adipokine and Oxidative Stress Marker Concentrations among Normal, Overweight and Obese Pregnant Women Using Artificial Neural Networks

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