scholarly journals Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Antonin Dauvin ◽  
Carolina Donado ◽  
Patrik Bachtiger ◽  
Ke-Chun Huang ◽  
Christopher Martin Sauer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Impaired Renal Function ◽  
Kidney Injury ◽  
Learning Models ◽  
Intensive Care Patients ◽  
Using Data ◽  
Machine Learning Models ◽  
Baseline Hemoglobin

AbstractPatients admitted to the intensive care unit frequently have anemia and impaired renal function, but often lack historical blood results to contextualize the acuteness of these findings. Using data available within two hours of ICU admission, we developed machine learning models that accurately (AUC 0.86–0.89) classify an individual patient’s baseline hemoglobin and creatinine levels. Compared to assuming the baseline to be the same as the admission lab value, machine learning performed significantly better at classifying acute kidney injury regardless of initial creatinine value, and significantly better at predicting baseline hemoglobin value in patients with admission hemoglobin of <10 g/dl.

scholarly journals Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury

2021 ◽  
Vol 8 ◽  
Author(s):  
Jiawei He ◽  
Jin Lin ◽  
Meili Duan
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Intensive Care ◽  
Kidney Injury ◽  
Learning Models ◽  
Short Term ◽  
Acute Kidney Disease ◽  
Mimic Iii ◽  
Machine Learning Models

Background: Sepsis-associated acute kidney injury (AKI) is frequent in patients admitted to intensive care units (ICU) and may contribute to adverse short-term and long-term outcomes. Acute kidney disease (AKD) reflects the adverse events developing after AKI. We aimed to develop and validate machine learning models to predict the occurrence of AKD in patients with sepsis-associated AKI.Methods: Using clinical data from patients with sepsis in the ICU at Beijing Friendship Hospital (BFH), we studied whether the following three machine learning models could predict the occurrence of AKD using demographic, laboratory, and other related variables: Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), decision trees, and logistic regression. In addition, we externally validated the results in the Medical Information Mart for Intensive Care III (MIMIC III) database. The outcome was the diagnosis of AKD when defined as AKI prolonged for 7–90 days according to Acute Disease Quality Initiative-16.Results: In this study, 209 patients from BFH were included, with 55.5% of them diagnosed as having AKD. Furthermore, 509 patients were included from the MIMIC III database, of which 46.4% were diagnosed as having AKD. Applying machine learning could successfully achieve very high accuracy (RNN-LSTM AUROC = 1; decision trees AUROC = 0.954; logistic regression AUROC = 0.728), with RNN-LSTM showing the best results. Further analyses revealed that the change of non-renal Sequential Organ Failure Assessment (SOFA) score between the 1st day and 3rd day (Δnon-renal SOFA) is instrumental in predicting the occurrence of AKD.Conclusion: Our results showed that machine learning, particularly RNN-LSTM, can accurately predict AKD occurrence. In addition, Δ SOFAnon−renal plays an important role in predicting the occurrence of AKD.

scholarly journals Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models

2021 ◽  
Vol 8 ◽  
Author(s):  
Longxiang Su ◽  
Zheng Xu ◽  
Fengxiang Chang ◽  
Yingying Ma ◽  
Shengjun Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Random Forest ◽  
Random Forest Classifier ◽  
Early Prediction ◽  
Learning Models ◽  
Prediction Of Mortality ◽  
Severity Prediction ◽  
Machine Learning Models

Background: Early prediction of the clinical outcome of patients with sepsis is of great significance and can guide treatment and reduce the mortality of patients. However, it is clinically difficult for clinicians.Methods: A total of 2,224 patients with sepsis were involved over a 3-year period (2016–2018) in the intensive care unit (ICU) of Peking Union Medical College Hospital. With all the key medical data from the first 6 h in the ICU, three machine learning models, logistic regression, random forest, and XGBoost, were used to predict mortality, severity (sepsis/septic shock), and length of ICU stay (LOS) (&gt;6 days, ≤ 6 days). Missing data imputation and oversampling were completed on the dataset before introduction into the models.Results: Compared to the mortality and LOS predictions, the severity prediction achieved the best classification results, based on the area under the operating receiver characteristics (AUC), with the random forest classifier (sensitivity = 0.65, specificity = 0.73, F1 score = 0.72, AUC = 0.79). The random forest model also showed the best overall performance (mortality prediction: sensitivity = 0.50, specificity = 0.84, F1 score = 0.66, AUC = 0.74; LOS prediction: sensitivity = 0.79, specificity = 0.66, F1 score = 0.69, AUC = 0.76) among the three models. The predictive ability of the SOFA score itself was inferior to that of the above three models.Conclusions: Using the random forest classifier in the first 6 h of ICU admission can provide a comprehensive early warning of sepsis, which will contribute to the formulation and management of clinical decisions and the allocation and management of resources.

scholarly journals Data-Driven Approach for Predicting and Explaining the Risk of Long-Term Unemployment

2020 ◽  
Vol 214 ◽  
pp. 01023
Author(s):  
Linan (Frank) Zhao
Keyword(s):  
Machine Learning ◽  
Age Groups ◽  
Learning Models ◽  
Public Authorities ◽  
Ensemble Machine Learning ◽  
European Public ◽  
Data Driven Approach ◽  
Using Data ◽  
Machine Learning Models

Long-term unemployment has significant societal impact and is of particular concerns for policymakers with regard to economic growth and public finances. This paper constructs advanced ensemble machine learning models to predict citizens’ risks of becoming long-term unemployed using data collected from European public authorities for employment service. The proposed model achieves 81.2% accuracy on identifying citizens with high risks of long-term unemployment. This paper also examines how to dissect black-box machine learning models by offering explanations at both a local and global level using SHAP, a state-of-the-art model-agnostic approach to explain factors that contribute to long-term unemployment. Lastly, this paper addresses an under-explored question when applying machine learning in the public domain, that is, the inherent bias in model predictions. The results show that popular models such as gradient boosted trees may produce unfair predictions against senior age groups and immigrants. Overall, this paper sheds light on the recent increasing shift for governments to adopt machine learning models to profile and prioritize employment resources to reduce the detrimental effects of long-term unemployment and improve public welfare.

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.

scholarly journals Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning

2020 ◽  
Vol 7 (2) ◽  
pp. 55
Author(s):  
Yasir Suhail ◽  
Madhur Upadhyay ◽  
Aditya Chhibber ◽  
Kshitiz
Keyword(s):  
Machine Learning ◽  
Human Error ◽  
Ensemble Methods ◽  
Treatment Decision ◽  
Learning Models ◽  
Error Training ◽  
Treatment Plans ◽  
Suitable Treatment ◽  
Using Data ◽  
Machine Learning Models

Extraction of teeth is an important treatment decision in orthodontic practice. An expert system that is able to arrive at suitable treatment decisions can be valuable to clinicians for verifying treatment plans, minimizing human error, training orthodontists, and improving reliability. In this work, we train a number of machine learning models for this prediction task using data for 287 patients, evaluated independently by five different orthodontists. We demonstrate why ensemble methods are particularly suited for this task. We evaluate the performance of the machine learning models and interpret the training behavior. We show that the results for our model are close to the level of agreement between different orthodontists.

scholarly journals Artificial Intelligence in the Intensive Care Unit

2020 ◽  
Author(s):  
Massimiliano Greco ◽  
Pier F. Caruso ◽  
Maurizio Cecconi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intensive Care ◽  
Language Processing ◽  
Laboratory Data ◽  
Kidney Injury ◽  
Support Vector ◽  
Learning Models ◽  
Electronic Health ◽  
Natural Terrain

AbstractThe diffusion of electronic health records collecting large amount of clinical, monitoring, and laboratory data produced by intensive care units (ICUs) is the natural terrain for the application of artificial intelligence (AI). AI has a broad definition, encompassing computer vision, natural language processing, and machine learning, with the latter being more commonly employed in the ICUs. Machine learning may be divided in supervised learning models (i.e., support vector machine [SVM] and random forest), unsupervised models (i.e., neural networks [NN]), and reinforcement learning. Supervised models require labeled data that is data mapped by human judgment against predefined categories. Unsupervised models, on the contrary, can be used to obtain reliable predictions even without labeled data. Machine learning models have been used in ICU to predict pathologies such as acute kidney injury, detect symptoms, including delirium, and propose therapeutic actions (vasopressors and fluids in sepsis). In the future, AI will be increasingly used in ICU, due to the increasing quality and quantity of available data. Accordingly, the ICU team will benefit from models with high accuracy that will be used for both research purposes and clinical practice. These models will be also the foundation of future decision support system (DSS), which will help the ICU team to visualize and analyze huge amounts of information. We plea for the creation of a standardization of a core group of data between different electronic health record systems, using a common dictionary for data labeling, which could greatly simplify sharing and merging of data from different centers.

scholarly journals Nursing Activities Score and Acute Kidney Injury

2017 ◽  
Vol 70 (3) ◽  
pp. 475-480 ◽  
Author(s):  
Filipe Utuari de Andrade Coelho ◽  
Mirian Watanabe ◽  
Cassiane Dezoti da Fonseca ◽  
Katia Grillo Padilha ◽  
Maria de Fátima Fernandes Vattimo
Keyword(s):  
Intensive Care Unit ◽  
Acute Kidney Injury ◽  
Intensive Care ◽  
Kidney Disease ◽  
Quantitative Study ◽  
Kidney Injury ◽  
Nursing Workload ◽  
Intensive Care Patients ◽  
Stage 1

ABSTRACT Objective: to evaluate the nursing workload in intensive care patients with acute kidney injury (AKI). Method: A quantitative study, conducted in an intensive care unit, from April to August of 2015. The Nursing Activities Score (NAS) and Kidney Disease Improving Global Outcomes (KDIGO) were used to measure nursing workload and to classify the stage of AKI, respectively. Results: A total of 190 patients were included. Patients who developed AKI (44.2%) had higher NAS when compared to those without AKI (43.7% vs 40.7%), p <0.001. Patients with stage 1, 2 and 3 AKI showed higher NAS than those without AKI. A relationship was identified between stage 2 and 3 with those without AKI (p = 0.002 and p <0.001). Conclusion: The NAS was associated with the presence of AKI, the score increased with the progression of the stages, and it was associated with AKI, stage 2 and 3.

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