Explainable Artificial Intelligence Helps in Understanding the Effect of Fibronectin on Survival of Sepsis

Lemańska-Perek, Anna; Krzyżanowska-Gołąb, Dorota; Kobylińska, Katarzyna; Biecek, Przemysław; Skalec, Tomasz; Tyszko, Maciej; Goździk, Waldemar; Adamik, Barbara

doi:10.3390/cells11152433

Cited by 8 publications

(2 citation statements)

References 46 publications

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“…In addition, there has recently been a growing interest in the development of models based on the eXplainable Artificial Intelligence (XAI) paradigm. Such an interpretable and explainable model can provide the necessary means to enable human users to understand the details behind the reasoning capacity of machine learning methods [ 96 ].…”

Section: Discussionmentioning

confidence: 99%

Anomaly Detection in Railway Sensor Data Environments: State-of-the-Art Methods and Empirical Performance Evaluation

Bałdyga,

Barański,

Belter

et al. 2024

Sensors

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To date, significant progress has been made in the field of railway anomaly detection using technologies such as real-time data analytics, the Internet of Things, and machine learning. As technology continues to evolve, the ability to detect and respond to anomalies in railway systems is once again in the spotlight. However, railway anomaly detection faces challenges related to the vast infrastructure, dynamic conditions, aging infrastructure, and adverse environmental conditions on the one hand, and the scale, complexity, and critical safety implications of railway systems on the other. Our study is underpinned by the three objectives. Specifically, we aim to identify time series anomaly detection methods applied to railway sensor device data, recognize the advantages and disadvantages of these methods, and evaluate their effectiveness. To address the research objectives, the first part of the study involved a systematic literature review and a series of controlled experiments. In the case of the former, we adopted well-established guidelines to structure and visualize the review. In the second part, we investigated the effectiveness of selected machine learning methods. To evaluate the predictive performance of each method, a five-fold cross-validation approach was applied to ensure the highest accuracy and generality. Based on the calculated accuracy, the results show that the top three methods are CatBoost (96%), Random Forest (91%), and XGBoost (90%), whereas the lowest accuracy is observed for One-Class Support Vector Machines (48%), Local Outlier Factor (53%), and Isolation Forest (55%). As the industry moves toward a zero-defect paradigm on a global scale, ongoing research efforts are focused on improving existing methods and developing new ones that contribute to the safety and quality of rail transportation. In this sense, there are at least four avenues for future research worth considering: testing richer data sets, hyperparameter optimization, and implementing other methods not included in the current study.

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Section: Discussionmentioning

confidence: 99%

Anomaly Detection in Railway Sensor Data Environments: State-of-the-Art Methods and Empirical Performance Evaluation

Bałdyga,

Barański,

Belter

et al. 2024

Sensors

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“…Recognizing these benefits, the current study focuses on leveraging the capabilities of XAI to develop a more effective and reliable tool for healthcare professionals in the battle against sepsis. In addition, although there are many studies on machine learning-based sepsis classification in the literature, there are very few studies on classification with explainable artificial intelligence and candidate biomarkers [ 12 , 13 , 14 , 15 ]. This study would contribute to the literature in this respect.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model

Aygun,

Yagin,

Yagin

et al. 2024

Diagnostics

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This study aims to develop an interpretable prediction model based on explainable artificial intelligence to predict bacterial sepsis and discover important biomarkers. A total of 1572 adult patients, 560 of whom were sepsis positive and 1012 of whom were negative, who were admitted to the emergency department with suspicion of sepsis, were examined. We investigated the performance characteristics of sepsis biomarkers alone and in combination for confirmed sepsis diagnosis using Sepsis-3 criteria. Three different tree-based algorithms—Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost)—were used for sepsis prediction, and after examining comprehensive performance metrics, descriptions of the optimal model were obtained with the SHAP method. The XGBoost model achieved accuracy of 0.898 (0.868–0.929) and area under the ROC curve (AUC) of 0.940 (0.898–0.980) with a 95% confidence interval. The five biomarkers for predicting sepsis were age, respiratory rate, oxygen saturation, procalcitonin, and positive blood culture. SHAP results revealed that older age, higher respiratory rate, procalcitonin, neutrophil–lymphocyte count ratio, C-reactive protein, plaque, leukocyte particle concentration, as well as lower oxygen saturation, systolic blood pressure, and hemoglobin levels increased the risk of sepsis. As a result, the Explainable Artificial Intelligence (XAI)-based prediction model can guide clinicians in the early diagnosis and treatment of sepsis, providing more effective sepsis management and potentially reducing mortality rates and medical costs.

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AI-Enabled Solutions, Explainability and Ethical Concerns for Predicting Sepsis in ICUs: A Systematic Review

Alexandropoulou,

Panagiotopoulos,

Kleanthous

et al. 2023

2023 IEEE 19th International Conference on E-Science (E-Science)

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Explainable Artificial Intelligence Helps in Understanding the Effect of Fibronectin on Survival of Sepsis

Cited by 8 publications

References 46 publications

Anomaly Detection in Railway Sensor Data Environments: State-of-the-Art Methods and Empirical Performance Evaluation

Anomaly Detection in Railway Sensor Data Environments: State-of-the-Art Methods and Empirical Performance Evaluation

Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model

AI-Enabled Solutions, Explainability and Ethical Concerns for Predicting Sepsis in ICUs: A Systematic Review

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