A research infrastructure for real-time evaluation of predictive algorithms for intensive care units

2013 ◽  
Author(s):  
Zhengbo Zhang ◽  
Joan Lee ◽  
D. J. Scott ◽  
L. Lehman ◽  
R. G. Mark
Keyword(s):  
Intensive Care ◽  
Real Time ◽  
Intensive Care Units ◽  
Research Infrastructure ◽  
Predictive Algorithms ◽  
Time Evaluation

scholarly journals Real-Time Data Mining Models for Predicting Length of Stay in Intensive Care Units

2014 ◽  
Author(s):  
Rui Veloso ◽  
Filipe Portela ◽  
Manuel Filipe Santos ◽  
Álvaro Silva ◽  
Fernando Rua ◽  
...  
Keyword(s):  
Data Mining ◽  
Intensive Care ◽  
Length Of Stay ◽  
Real Time ◽  
Intensive Care Units ◽  
Time Data ◽  
Real Time Data

scholarly journals PMC12 ASSESSMENT OF THE WORKLOAD REAL TIME DEDICATED TO EACH PATIENT IN INTENSIVE CARE UNITS (ICU): PRELIMINARY RESULTS OF THE CRRÉA STUDY

2010 ◽  
Vol 13 (7) ◽  
pp. A330-A331
Author(s):  
B Garrigues ◽  
JY Lefrant ◽  
C Pribil ◽  
J Bazin ◽  
F Maurel
Keyword(s):  
Intensive Care ◽  
Real Time ◽  
Intensive Care Units ◽  
Preliminary Results

Regional trends in multidrug-resistant infections in German intensive care units: a real-time model for epidemiological monitoring and analysis

2009 ◽  
Vol 73 (3) ◽  
pp. 239-245 ◽  
Author(s):  
A. Kohlenberg ◽  
F. Schwab ◽  
E. Meyer ◽  
M. Behnke ◽  
C. Geffers ◽  
...  
Keyword(s):  
Intensive Care ◽  
Real Time ◽  
Intensive Care Units ◽  
Multidrug Resistant ◽  
Time Model ◽  
Epidemiological Monitoring ◽  
Regional Trends

scholarly journals Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units

2021 ◽  
Author(s):  
Stef Baas ◽  
Sander Dijkstra ◽  
Aleida Braaksma ◽  
Plom van Rooij ◽  
Fieke J. Snijders ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Length Of Stay ◽  
The Netherlands ◽  
Real Time ◽  
Data Warehouse ◽  
Intensive Care Units ◽  
Bed Occupancy ◽  
Control Centre

AbstractThis paper presents a mathematical model that provides a real-time forecast of the number of COVID-19 patients admitted to the ward and the Intensive Care Unit (ICU) of a hospital based on the predicted inflow of patients, their Length of Stay (LoS) in both the ward and the ICU as well as transfer of patients between the ward and the ICU. The data required for this forecast is obtained directly from the hospital’s data warehouse. The resulting algorithm is tested on data from the first COVID-19 peak in the Netherlands, showing that the forecast is very accurate. The forecast may be visualised in real-time in the hospital’s control centre and is used in several Dutch hospitals during the second COVID-19 peak.

scholarly journals Noninvasive Real-Time Mortality Prediction in Intensive Care Units Based on Gradient-Boosting Method (Preprint)

10.2196/23888 ◽  
2020 ◽  
Author(s):  
Huizhen Jiang ◽  
Longxiang Su ◽  
Hao Wang ◽  
Dongkai Li ◽  
Congpu Zhao ◽  
...  
Keyword(s):  
Intensive Care ◽  
Real Time ◽  
Intensive Care Units ◽  
Mortality Prediction ◽  
Gradient Boosting ◽  
Boosting Method

Feature engineering combined with 1-D convolutional neural network for improved mortality prediction

2020 ◽  
Vol 16 (4) ◽  
Author(s):  
Rohit Verma ◽  
Saumil Maheshwari ◽  
Anupam Shukla
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intensive Care ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Intensive Care Units ◽  
Mortality Prediction ◽  
Support Vector ◽  
Feature Engineering ◽  
Light Gradient

AbstractObjectivesThe appropriate care for patients admitted in Intensive care units (ICUs) is becoming increasingly prominent, thus recognizing the use of machine learning models. The real-time prediction of mortality of patients admitted in ICU has the potential for providing the physician with the interpretable results. With the growing crisis including soaring cost, unsafe care, misdirected care, fragmented care, chronic diseases and evolution of epidemic diseases in the domain of healthcare demands the application of automated and real-time data processing for assuring the improved quality of life. The intensive care units (ICUs) are responsible for generating a wealth of useful data in the form of Electronic Health Record (EHR). This data allows for the development of a prediction tool with perfect knowledge backing.MethodWe aimed to build the mortality prediction model on 2012 Physionet Challenge mortality prediction database of 4,000 patients admitted in ICU. The challenges in the dataset, such as high dimensionality, imbalanced distribution and missing values, were tackled with analytical methods and tools via feature engineering and new variable construction. The objective of the research is to utilize the relations among the clinical variables and construct new variables which would establish the effectiveness of 1-Dimensional Convolutional Neural Network (1-D CNN) with constructed features.ResultsIts performance with the traditional machine learning algorithms like XGBoost classifier, Light Gradient Boosting Machine (LGBM) classifier, Support Vector Machine (SVM), Decision Tree (DT), K-Neighbours Classifier (K-NN), and Random Forest Classifier (RF) and recurrent models like Long Short-Term Memory (LSTM) and LSTM-attention is compared for Area Under Curve (AUC). The investigation reveals the best AUC of 0.848 using 1-D CNN model.ConclusionThe relationship between the various features were recognized. Also, constructed new features using existing ones. Multiple models were tested and compared on different metrics.

scholarly journals Noninvasive Real-time Mortality Prediction in Intensive Care Units Based on Gradient Boosting Method (Preprint)

2020 ◽  
Author(s):  
Huizhen Jiang ◽  
Longxiang Su ◽  
Hao Wang ◽  
Dongkai Li ◽  
Congpu Zhao ◽  
...  
Keyword(s):  
Intensive Care ◽  
Real Time ◽  
Intensive Care Units ◽  
Prediction Models ◽  
Mortality Prediction ◽  
Gradient Boosting ◽  
Noninvasive Method ◽  
Icu Patients ◽  
The Real ◽  
Boosting Method

BACKGROUND It is especially necessary to pay attention to the critically ill patients in ICU(Intensive Care Units) real time. Scoring systems are mostly used in the risk prediction of mortality, while usually they are not so precise and real-time with the clinical data simply weighted, and it is also time-consuming for clinical staff. OBJECTIVE We would like to fuse all the medical data together and predict the real-time mortality of ICU patients by machine learning method, which would be valuable and significant. Besides, we want to explore predicting the mortality by noninvasive data to lessen the pain of patients. METHODS In this paper, we established 5 models to predict mortality real-time based on different features. Based on monitoring data, examination data and scoring data, we structured the feature engineering. 5 Real-time Mortality prediction models were RMM(Monitoring features), RMA(APACHE and monitoring features), RMS(SOFA and monitoring features), RMME(Monitoring and Examination features) and RM(all features from monitoring, examination data and scoring data). Then, we compared the performance of all models and put more focus on the noninvasive method RMM. RESULTS After extensive experiments, the performance of RMME was superior to that of other 4 models. With the scoring features included, the model showed worse performance. And, RMM only based on monitoring features performed better than that of RMA and RMS. Therefore, it is meaningful and practicable to predict mortality by the noninvasive way, which could reduce the extra physical damage to patients like drawing blood. Moreover, we explored the top 9 features relevant with the real-time mortality prediction. Top 9 features were "ABP (mmHg) invasive mean pressure", "Heart rate", "ABP (mmHg) invasive systolic pressure", "Oxygen concentration", "SPO2", "Balance of inflow and outflow", "Total input", "ABP (mmHg) invasive diastolic pressure" and "NBP-average pressure", which could be paid more focus on during the general clinical work. CONCLUSIONS This research could be helpful in real-time mortality prediction of ICU patients, especially by the noninvasive method. It is meaningful and friendly to patients, which is of strong practical significance.

Planning for a Filmless Hospital

2010 ◽  
pp. 72-91
Author(s):  
Carrison K.S. Tong ◽  
Eric T.T. Wong
Keyword(s):  
Intensive Care ◽  
Real Time ◽  
Intensive Care Units ◽  
Emergency Departments ◽  
Cost Effective ◽  
Operating Rooms ◽  
Access To Information ◽  
X Ray ◽  
Effective Manner

Filmless hospital is transforming at an unprecedented rate. Physicians, nurses, clinicians, pharmacists, radiologists, emergency departments, local doctor’s offices, operating rooms, intensive care units, and insurance offices all must have instantaneous access to information from CT, MR, and X-ray images to treat their patients. Considering that these individuals could be on different floors of a hospital, across a campus, or scattered over several states, connecting them in real-time and in a cost-effective manner to the information they need is a monumental IT challenge. Detail planning is important in a filmless hospital project.

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