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

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.

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 Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study

2015 ◽  
Vol 3 (1) ◽  
pp. 42-52 ◽  
Author(s):  
Romain Pirracchio ◽  
Maya L Petersen ◽  
Marco Carone ◽  
Matthieu Resche Rigon ◽  
Sylvie Chevret ◽  
...  
Keyword(s):  
Intensive Care ◽  
Intensive Care Units ◽  
Population Based ◽  
Mortality Prediction ◽  
Population Based Study

scholarly journals Mortality Prediction in Cerebral Hemorrhage Patients Using Machine Learning Algorithms in Intensive Care Units

2021 ◽  
Vol 11 ◽  
Author(s):  
Ximing Nie ◽  
Yuan Cai ◽  
Jingyi Liu ◽  
Xiran Liu ◽  
Jiahui Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Random Forest ◽  
Hospital Mortality ◽  
Intensive Care Units ◽  
Cerebral Hemorrhage ◽  
Characteristic Curve ◽  
Learning Algorithms ◽  
Mortality Prediction ◽  
Machine Learning Algorithms

Objectives: This study aims to investigate whether the machine learning algorithms could provide an optimal early mortality prediction method compared with other scoring systems for patients with cerebral hemorrhage in intensive care units in clinical practice.Methods: Between 2008 and 2012, from Intensive Care III (MIMIC-III) database, all cerebral hemorrhage patients monitored with the MetaVision system and admitted to intensive care units were enrolled in this study. The calibration, discrimination, and risk classification of predicted hospital mortality based on machine learning algorithms were assessed. The primary outcome was hospital mortality. Model performance was assessed with accuracy and receiver operating characteristic curve analysis.Results: Of 760 cerebral hemorrhage patients enrolled from MIMIC database [mean age, 68.2 years (SD, ±15.5)], 383 (50.4%) patients died in hospital, and 377 (49.6%) patients survived. The area under the receiver operating characteristic curve (AUC) of six machine learning algorithms was 0.600 (nearest neighbors), 0.617 (decision tree), 0.655 (neural net), 0.671(AdaBoost), 0.819 (random forest), and 0.725 (gcForest). The AUC was 0.423 for Acute Physiology and Chronic Health Evaluation II score. The random forest had the highest specificity and accuracy, as well as the greatest AUC, showing the best ability to predict in-hospital mortality.Conclusions: Compared with conventional scoring system and the other five machine learning algorithms in this study, random forest algorithm had better performance in predicting in-hospital mortality for cerebral hemorrhage patients in intensive care units, and thus further research should be conducted on random forest algorithm.

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