Machine learning models for identifying preterm infants at risk of cerebral hemorrhage

Turova, Varvara; Sidorenko, Irina; Eckardt, Laura; Rieger‐Fackeldey, Esther; Felderhoff-Müser, Ursula; Alves-Pinto, Ana; Lampe, Renée

doi:10.1371/journal.pone.0227419

Cited by 18 publications

(17 citation statements)

References 31 publications

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“…The age of onset is usually around 60 years, and the male patients aged 61∼70 are more than the female patients of the same age. The age group of the patients in this age group accounted for 31% of the total population [ 16 – 18 ].…”

Section: Discussionmentioning

confidence: 99%

Effect of Minimally Invasive Puncture Drainage and Conservative Treatment on Prognosis of Patients with Cerebral Hemorrhage

Jia

Wang

Yang

et al. 2021

Journal of Healthcare Engineering

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The objective of this study was to explore the effect of minimally invasive puncture drainage under unsupervised learning algorithm and conservative treatment on the prognosis of patients with cerebral hemorrhage. Fifty patients with cerebral hemorrhage were selected as the research objects. The CT images of patients were segmented by unsupervised learning algorithm, and the application value of unsupervised learning algorithm on CT images of patients with cerebral hemorrhage was evaluated. According to the treatment wishes of the patients themselves and their authorizers, they were divided into 30 patients with cerebral hemorrhage in the minimally invasive group and 20 patients with cerebral hemorrhage in the conservative group. The incidence rate of complications of cerebral hemorrhage, the length of hospitalization of the two groups, hematoma volume at admission, 3 days and 7 days after operation, and the hematoma dissipation rate on the 3rd and 7th day after operation were used as the evaluation index of therapeutic effect. MRS and ADL scores were used as prognostic indicators. The results show that K-means clustering algorithm has high quality and short time for CT image segmentation. The overall incidence rate of complications in minimally invasive group was 10%, lower than that in conservative group (25%) ( P < 0.05 ), and the length of hospitalization in minimally invasive group was longer than that in conservative group ( P < 0.05 ). The hematoma volume of minimally invasive group was 16.5 ± 2.4 mL on the 3rd day after operation, and that of conservative group was 27.4 ± 1.8 mL. There was significant difference between the two groups ( P < 0.05 ). In addition, CT showed that the hematoma reduction degree of minimally invasive group was higher than that of conservative group, and the hematoma dissipation rate was higher than that of conservative group on the 3rd and 7th day ( P < 0.05 ). The good MRS score in minimally invasive group was 3.15 times that in conservative group, and the good ADL score was 1.6 times that in conservative group, and there was significant difference in the total score between the two groups ( P < 0.05 ). Minimally invasive puncture drainage is better than conservative treatment in the clearance of hematoma, which is conducive to the recovery of neurological function and daily life of patients with cerebral hemorrhage and is of great help to the prognosis of patients.

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Section: Discussionmentioning

confidence: 99%

Effect of Minimally Invasive Puncture Drainage and Conservative Treatment on Prognosis of Patients with Cerebral Hemorrhage

Jia

Wang

Yang

et al. 2021

Journal of Healthcare Engineering

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“…Using ML approaches to predict NICU outcomes has emerged as a major research trend in the past decade [ 16 , 27 , 28 , 29 , 30 ], although no study has focused on neonates with clinically suspected sepsis. In the NICU, unproven culture-negative sepsis and clinical sepsis are much more common than clinicians have previously thought, but they are scarcely investigated [ 31 , 32 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Approaches to Predict In-Hospital Mortality among Neonates with Clinically Suspected Sepsis in the Neonatal Intensive Care Unit

Hsu

Chang

Cheng

et al. 2021

JPM

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Background: preterm and critically ill neonates often experience clinically suspected sepsis during their prolonged hospitalization in the neonatal intensive care unit (NICU), which can be the initial sign of final adverse outcomes. Therefore, we aimed to utilize machine learning approaches to predict neonatal in-hospital mortality through data-driven learning. Methods: a total of 1095 neonates who experienced clinically suspected sepsis in a tertiary-level NICU in Taiwan between August 2017 and July 2020 were enrolled. Clinically suspected sepsis was defined based on clinical features and laboratory criteria and the administration of empiric antibiotics by clinicians. The variables used for analysis included patient demographics, clinical features, laboratory data, and medications. The machine learning methods used included deep neural network (DNN), k-nearest neighbors, support vector machine, random forest, and extreme gradient boost. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). Results: the final in-hospital mortality of this cohort was 8.2% (90 neonates died). A total of 765 (69.8%) and 330 (30.2%) patients were randomly assigned to the training and test sets, respectively. Regarding the efficacy of the single model that most accurately predicted the outcome, DNN exhibited the greatest AUC (0.923, 95% confidence interval [CI] 0.953–0.893) and the best accuracy (95.64%, 95% CI 96.76–94.52%), Cohen’s kappa coefficient value (0.74, 95% CI 0.79–0.69) and Matthews correlation coefficient value (0.75, 95% CI 0.80–0.70). The top three most influential variables in the DNN importance matrix plot were the requirement of ventilator support at the onset of suspected sepsis, the feeding conditions, and intravascular volume expansion. The model performance was indistinguishable between the training and test sets. Conclusions: the DNN model was successfully established to predict in-hospital mortality in neonates with clinically suspected sepsis, and the machine learning algorithm is applicable for clinicians to gain insights and have better communication with families in advance.

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“…Therefore, cerebral functional monitoring (CFM) using aEEG/EEG is critical for diagnosis, prognosis, and treatment in the newborn period ( 120 ). ML approaches have been used to analyze several clinical factors in 230 very preterm infants to predict the risk of intracerebral hemorrhage with good predictive ability achieved with different combinations of clinical and laboratory parameters ( 121 ). However, the developed models need to be tested further in new larger datasets before being used in the clinics.…”

Section: The Use Of Eeg/aeeg For Precision Medicinementioning

confidence: 99%

Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury

et al. 2021

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Despite advances in neonatal care to prevent neonatal brain injury and neurodevelopmental impairment, predicting long-term outcome in neonates at risk for brain injury remains difficult. Early prognosis is currently based on cranial ultrasound (CUS), MRI, EEG, NIRS, and/or general movements assessed at specific ages, and predicting outcome in an individual (precision medicine) is not yet possible. New algorithms based on large databases and machine learning applied to clinical, neuromonitoring, and neuroimaging data and genetic analysis and assays measuring multiple biomarkers (omics) can fulfill the needs of modern neonatology. A synergy of all these techniques and the use of automatic quantitative analysis might give clinicians the possibility to provide patient-targeted decision-making for individualized diagnosis, therapy, and outcome prediction. This review will first focus on common neonatal neurological diseases, associated risk factors, and most common treatments. After that, we will discuss how precision medicine and machine learning (ML) approaches could change the future of prediction and prognosis in this field.

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Machine learning models for identifying preterm infants at risk of cerebral hemorrhage

Cited by 18 publications

References 31 publications

Effect of Minimally Invasive Puncture Drainage and Conservative Treatment on Prognosis of Patients with Cerebral Hemorrhage

Effect of Minimally Invasive Puncture Drainage and Conservative Treatment on Prognosis of Patients with Cerebral Hemorrhage

Machine Learning Approaches to Predict In-Hospital Mortality among Neonates with Clinically Suspected Sepsis in the Neonatal Intensive Care Unit

Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury

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