Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the IoT Medical Environment

Background:Sepsis,a potentially fatal inflammatory disease triggered by infection,carries significant healthimplications worldwide.Timely detection is crucial as sepsis can rapidly escalate if left undetected.Recentadvancements in deep learning(DL)offer powerful tools to address this challenge.Aim:Thus,this study proposeda hybrid CNNBDLSTM,a combination of a convolutional neural network(CNN)with a bi-directional long shorttermmemory(BDLSTM)model to predict sepsis onset.Implementing the proposed model provides a robustframework that capitalizes on the complementary strengths of both architectures,resulting in more accurate andtimelier predictions.Method:The sepsis prediction method proposed here utilizes temporal feature extraction todelineate six distinct time frames before the onset of sepsis.These time frames adhere to the sepsis-3 standardrequirement,which incorporates 12-h observation windows preceding sepsis onset.All models were trained usingthe Medical Information Mart for Intensive Care III(MIMIC-III)dataset,which sourced 61,522 patients with 40clinical variables obtained from the IoT medical environment.The confusion matrix,the area under the receiveroperating characteristic curve(AUCROC)curve,the accuracy,the precision,the F1-score,and the recall weredeployed to evaluate themodels.Result:The CNNBDLSTMmodel demonstrated superior performance comparedto the benchmark and other models,achieving an AUCROC of 99.74%and an accuracy of 99.15%one hour beforesepsis onset.These results indicate that the CNNBDLSTM model is highly effective in predicting sepsis onset,particularly within a close proximity of one hour.Implication:The results could assist practitioners in increasingthe potential survival of the patient one hour before sepsis onset.

Investigating computational models for diagnosis and prognosis of sepsis based on clinical parameters: Opportunities, challenges, and future research directions

This study investigates the use of computational frameworks for sepsis.We consider two dimensions for investi-gation-early diagnosis of sepsis(EDS)and mortality prediction rate for sepsis patients(MPS).We concentrate on the clinical parameters on which sepsis diagnosis and prognosis are currently done,including customized treatment plans based on historical data of the patient.We identify the most notable literature that uses com-putational models to address EDS and MPS based on those clinical parameters.In addition to the review of the computational models built upon the clinical parameters,we also provide details regarding the popular publicly available data sources.We provide brief reviews for each model in terms of prior art and present an analysis of their results,as claimed by the respective authors.With respect to the use of machine learning models,we have provided avenues for model analysis in terms of model selection,model validation,model interpretation,and model comparison.We further present the challenges and limitations of the use of computational models,providing future research directions.This study intends to serve as a benchmark for first-hand impressions on the use of computational models for EDS and MPS of sepsis,along with the details regarding which model has been the most promising to date.We have provided details regarding all the ML models that have been used to date for EDS and MPS of sepsis.

Enhancing Septic Shock Detection through Interpretable Machine Learning

This article presents an innovative approach that leverages interpretable machine learning models and cloud computing to accelerate the detection of septic shock by analyzing electronic health data.Unlike traditional methods,which often lack transparency in decision-making,our approach focuses on early detection,offering a proactive strategy to mitigate the risks of sepsis.By integrating advanced machine learning algorithms with interpretability techniques,our method not only provides accurate predictions but also offers clear insights into the factors influencing the model’s decisions.Moreover,we introduce a preference-based matching algorithm to evaluate disease severity,enabling timely interventions guided by the analysis outcomes.This innovative integration significantly enhances the effectiveness of our approach.We leverage a clinical health dataset comprising 1,552,210 Electronic Health Records(EHR)to train our interpretable machine learning models within a cloud computing framework.Through techniques like feature importance analysis and model-agnostic interpretability tools,we aim to clarify the crucial indicators contributing to septic shock prediction.This transparency not only assists healthcare professionals in comprehending the model’s predictions but also facilitates the integration of our system into existing clinical workflows.We validate the effectiveness of our interpretable models using the same dataset,achieving an impressive accuracy rate exceeding 98%through the application of oversampling techniques.The findings of this study hold significant implications for the advancement of more effective and transparent diagnostic tools in the critical domain of sepsis management.