Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation

Kuo, Yao-Yi; Huang, Shong; Chiu, Hung-Wen

doi:10.1186/s12911-021-01653-0

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…Kuo et al developed a method using an artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation [ 19 ]. It is built with a low percentage of missing values and a high rate of missing and erroneous data to enable prediction under missing, noisy, and erroneous inputs, as in the actual clinical situation.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients

Singh

Singh²,

Khan³

et al. 2022

Journal of Healthcare Engineering

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In today’s scenario, sepsis is impacting millions of patients in the intensive care unit due to the fact that the mortality rate is increased exponentially and has become a major challenge in the field of healthcare. Such peoples require determinant care which increases the cost of the treatment by using a large number of resources because of the nonavailability of the resources. The treatment of sepsis is available in the early state, but treatment is not started at the right time, and then it converts to the advanced level of sepsis and increases the fatalities. Thus, an intensive analysis is required to detect and identify sepsis at the early stage. There are some models available that work based on the manual score and based on only the biomark features, but these are not fully automated. Some machine learning-based models are also available, which can reduce the mortality rate, but accuracy is not up to date. This paper proposes a machine learning model for early detecting and predicting sepsis in intensive care unit patients. Various models, random forest (RF), linear regression (LR), support vector machine (SVM), naive Bayes (NB), ensemble (of SVM, RF, NB, and LR), XGBoost, and proposed ensemble (of SVM, RF, NB, LR, and XGBoost), are simulated by using the collected data from intensive care unit patient’s database that is based on the clinical laboratory values and vital signs. The performance of the models is evaluated by considering the same datasets. The balanced accuracy of RF, LR, SVM, NB, ensemble (of SVM, RF, NB, and LR), XGBoost, and proposed ensemble (of SVM, RF, NB, LR, and XGBoost) is 0.90, 0.73, 0.93, 0.74, 0.94, 0.95, and 0.96, respectively. It is also evident from the experimental results that the proposed ensemble model performs well as compared to the other models.

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Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients

Singh

Singh²,

Khan³

et al. 2022

Journal of Healthcare Engineering

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“…diagnosis of infection. AI algorithms were used to interpret PCT levels and diagnose and determine the severity of infections using large datasets [11][12][13]. In many studies, AI algorithms have achieved higher-than-human accuracy in assessing the severity of infections using PCT levels.…”

Section: ≥ 10mentioning

confidence: 99%

PROCAL: A SHap-based approach to explain Random Forest predictions of COVID-19- Procalcitonin levels

Naldan,

Kırboğa,

Işık

et al. 2024

Preprint

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In COVID-19, the inflammatory cytokine storm is a critical factor that increases the severity of the disease. Procalcitonin (PCT) is a costly, time-consuming and important biomarker involved in the cytokine storm that exacerbates the severity of COVID-19. This study aims to develop an algorithm that can predict the PCT value in an explainable and interpretable way using explainable artificial intelligence (XAI) methods. The dataset consists of 1068 COVID-19 patients registered at Erzurum Regional Research Center in Turkey between March 2020 and March 2021 (ethical decision number: 2023/3–17). The Permutation Feature Significance (PFI) method was used to identify essential features and build the model. Among the seven-machine learning (ML) models, RandomForestClassifier performed best. RandomForestClassifier's performance metrics training accuracy: 0.89, test accuracy: 0.88, precision: 0.91, recall: 0.88, F-1 score: 0.88, Brier score: 0.11, AUC (area under the curve): 0.935, confidence intervals: 0.877, 0.883. The importance of the features in the model's predictions was analysed with the Shapley additive annotation (SHap) method integrated into the model. The results showed that LDH U/L, CRP mg/L and lymphocytes are important in predicting PCT. This study showed that PCT plays a vital role in assessing the condition of COVID-19 patients, and XAI methods can be helpful in this assessment.

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Prediction of Sepsis Disease Using Random Search to Optimize Hyperparameter Tuning Based on Lazy Predict Model

Lydia,

Althubiti,

Anupama

et al. 2023

Smart Innovation, Systems and Technologies

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Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation

Cited by 5 publications

References 38 publications

A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients

A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients

PROCAL: A SHap-based approach to explain Random Forest predictions of COVID-19- Procalcitonin levels

Prediction of Sepsis Disease Using Random Search to Optimize Hyperparameter Tuning Based on Lazy Predict Model

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