Intracranial pressure model in intensive care unit using a simple recurrent neural network through time

2004 ◽  
Vol 57 ◽  
pp. 239-256 ◽  
Author(s):  
Jiann-Shing Shieh ◽  
Chi-Fong Chou ◽  
Sheng-Jean Huang ◽  
Ming-Chien Kao
Keyword(s):  
Neural Network ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Intracranial Pressure ◽  
Recurrent Neural Network

A recurrent neural network-based predictive model for pressure ulcers based on an intensive care unit dataset (MIMIC-III) (Preprint)

2021 ◽  
Author(s):  
Minseop Park ◽  
Hyeok Choi ◽  
Hee-Sung Ahn ◽  
Hee-Ju Kang ◽  
Saehoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Dynamic Features ◽  
Braden Scale ◽  
Mlp Model ◽  
Mimic Iii

BACKGROUND A pressure ulcer (PU) is a localized cutaneous injury caused by pressure or shear, which usually occurs in the region of a bony prominence. PUs are common in hospitalized patients and cause complications including infection. OBJECTIVE This study aimed to build a recurrent neural network-based algorithm to predict PUs 24 hours before their occurrence. METHODS This study analyzed a freely accessible intensive care unit (ICU) dataset, MIMIC- III. Deep learning and machine learning algorithms including long short-term memory (LSTM), multilayer perceptron (MLP), and XGBoost were applied to 37 dynamic features (including the Braden scale, vital signs and laboratory results, and interventions to reduce the risk of PUs) and 35 static features (including the length of time spent in the ICU, demographics, and comorbidities). Their outcomes were compared in terms of the area under the receiver operating characteristic (AUROC) and the area under the precision-recall curve (AUPRC). RESULTS A total of 1,048 cases of PUs (10.0%) and 9,402 controls (90.0%) without PUs satisfied the inclusion criteria for analysis. The LSTM + MLP model (AUROC: 0.7929 ± 0.0095, AUPRC: 0.4819 ± 0.0109) outperformed the other models, namely: MLP model (AUROC: 0.7777 ± 0.0083, AUPRC: 0.4527 ± 0.0195) and XGBoost (AUROC: 0.7465 ± 0.0087, AUPRC: 0.4052 ± 0.0087). Various features, including the length of time spent in the ICU, Glasgow coma scale, and the Braden scale, contributed to the prediction model. CONCLUSIONS This study suggests that recurrent neural network-based algorithms such as LSTM can be applied to evaluate the risk of PUs in ICU patients.

scholarly journals Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study (Preprint)

2019 ◽  
Author(s):  
Longxiang Su ◽  
Chun Liu ◽  
Dongkai Li ◽  
Jie He ◽  
Fanglan Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Data Set ◽  
Machine Learning Methods ◽  
Heparin Treatment ◽  
Neural Network Algorithm ◽  
Mimic Iii ◽  
Machine Learning Models

BACKGROUND Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. OBJECTIVE The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. METHODS Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e–Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. RESULTS Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. CONCLUSIONS The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning–based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

Managing Edema and Intracranial Pressure in the Intensive Care Unit

2013 ◽  
pp. 363-378
Author(s):  
Brian M. Cummings ◽  
Phoebe H. Yager ◽  
Sarah A. Murphy ◽  
Brian Kalish ◽  
Chetan Bhupali ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Intracranial Pressure

Stridor: Intracranial Pathology Causing Postextubation Vocal Cord Paralysis

PEDIATRICS ◽  
1991 ◽  
Vol 87 (1) ◽  
pp. 39-43
Author(s):  
Frank C. Chaten ◽  
Steven E. Lucking ◽  
Edwin S. Young ◽  
John J. Mickell
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Intracranial Pressure ◽  
Intracranial Hypertension ◽  
Vocal Cord ◽  
Vocal Cord Paralysis ◽  
Pediatric Intensive Care ◽  
Laryngeal Nerve ◽  
The Other ◽  
Neurologic Disorders

During an 18-month period in a pediatric intensive care unit, nine patients with vocal cord paralysis were identified using flexible bronchoscopy. When tracheally extubated, each child was found to have stridor. The children ranged in age from 17 days to 5½ years. Two patients had unilateral paralysis, but neither required tracheostomy. Seven patients displayed bilateral abductor vocal cord paralysis. Of these, six patients required tracheostomy. Surgical injury to the recurrent laryngeal nerve was the probable cause in two patients. The other seven patients had neurologic disorders with documented or suspected increases of intracranial pressure. Four of the seven patients with bilateral abductor vocal cord paralysis regained cord mobility within 4 months. Both children with unilateral cord paralysis have no stridor and vocalize well 1 year later. Cord paralysis in the setting of intracranial hypertension probably results from compression or ischemia of the vagus nerve before it exits the skull. Early visualization of the larynx should be done in patients who become stridulous when extubated, especially those with prior thoracic procedures or with neurologic disorders associated with intracranial hypertension.

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