Sepsis is a complex and heterogeneous syndrome that remains a serious challenge to healthcare worldwide.Patients afflicted by severe sepsis or septic shock are customarily placed under intensive care unit(ICU)supervis...Sepsis is a complex and heterogeneous syndrome that remains a serious challenge to healthcare worldwide.Patients afflicted by severe sepsis or septic shock are customarily placed under intensive care unit(ICU)supervision,where a multitude of apparatus is poised to produce high-granularity data.This reservoir of high-quality data forms the cornerstone for the integration of AI into clinical practice.However,existing reviews currently lack the inclusion of the latest advancements.This review examines the evolving integration of artificial intelligence(AI)in sepsis management.Applications of artificial intelligence include early detection,subtyping analysis,precise treatment and prognosis assessment.AI-driven early warning systems provide enhanced recognition and intervention capabilities,while profiling analyzes elucidate distinct sepsis manifestations for targeted therapy.Precision medicine harnesses the potential of artificial intelligence for pathogen identification,antibiotic selection,and fluid optimization.In conclusion,the seamless amalgamation of artificial intelligence into the domain of sepsis management heralds a transformative shift,ushering in novel prospects to elevate diagnostic precision,therapeutic efficacy,and prognostic acumen.As AI technologies develop,their impact on shaping the future of sepsis care warrants ongoing research and thoughtful implementation.展开更多
Background.In critical care,intensivists are required to continuously monitor high-dimensional vital signs and lab measurements to detect and diagnose acute patient conditions,which has always been a challenging task....Background.In critical care,intensivists are required to continuously monitor high-dimensional vital signs and lab measurements to detect and diagnose acute patient conditions,which has always been a challenging task.Recently,deep learning models such as recurrent neural networks(RNNs)have demonstrated their strong potential on predicting such events.However,in real deployment,the patient data are continuously coming and there is no effective adaptation mechanism for RNN to incorporate those new data and become more accurate.Methods.In this study,we propose a novel self-correcting mechanism for RNN to fill in this gap.Our mechanism feeds prediction errors from the predictions of previous timestamps into the prediction of the current timestamp,so that the model can“learn”from previous predictions.We also proposed a regularization method that takes into account not only the model’s prediction errors on the labels but also its estimation errors on the input data.Results.We compared the performance of our proposed method with the conventional deep learning models on two real-world clinical datasets for the task of acute kidney injury(AKI)prediction and demonstrated that the proposed model achieved an area under ROC curve at 0.893 on the MIMIC-III dataset and 0.871 on the Philips eICU dataset.Conclusions.The proposed self-correcting RNNs demonstrated effectiveness in AKI prediction and have the potential to be applied to clinical applications.展开更多
Background:In charge of dispatching the ambulances,Emergency Medical Services(EMS)call center specialists often have difficulty deciding the acuity of a case given the information they can gather within a limited time...Background:In charge of dispatching the ambulances,Emergency Medical Services(EMS)call center specialists often have difficulty deciding the acuity of a case given the information they can gather within a limited time.Although there are protocols to guide their decision-making,observed performance can still lack sensitivity and specificity.Machine learning models have been known to capture complex relationships that are subtle,and well-trained data models can yield accurate predictions in a split of a second.Methods:In this study,we proposed a proof-of-concept approach to construct a machine learning model to better predict the acuity of emergency cases.We used more than 360,000 structured emergency call center records of cases received by the national emergency call center in Singapore from 2018 to 2020.Features were created using call records,and multiple machine learning models were trained.Results:A Random Forest model achieved the best performance,reducing the over-triage rate by an absolute margin of 15%compared to the call center specialists while maintaining a similar level of under-triage rate.Conclusions:The model has the potential to be deployed as a decision support tool for dispatchers alongside current protocols to optimize ambulance dispatch triage and the utilization of emergency ambulance resources.展开更多
文摘Sepsis is a complex and heterogeneous syndrome that remains a serious challenge to healthcare worldwide.Patients afflicted by severe sepsis or septic shock are customarily placed under intensive care unit(ICU)supervision,where a multitude of apparatus is poised to produce high-granularity data.This reservoir of high-quality data forms the cornerstone for the integration of AI into clinical practice.However,existing reviews currently lack the inclusion of the latest advancements.This review examines the evolving integration of artificial intelligence(AI)in sepsis management.Applications of artificial intelligence include early detection,subtyping analysis,precise treatment and prognosis assessment.AI-driven early warning systems provide enhanced recognition and intervention capabilities,while profiling analyzes elucidate distinct sepsis manifestations for targeted therapy.Precision medicine harnesses the potential of artificial intelligence for pathogen identification,antibiotic selection,and fluid optimization.In conclusion,the seamless amalgamation of artificial intelligence into the domain of sepsis management heralds a transformative shift,ushering in novel prospects to elevate diagnostic precision,therapeutic efficacy,and prognostic acumen.As AI technologies develop,their impact on shaping the future of sepsis care warrants ongoing research and thoughtful implementation.
基金This work is supported by the National University of Singapore start-up grant with award number R-608-000-172-133.
文摘Background.In critical care,intensivists are required to continuously monitor high-dimensional vital signs and lab measurements to detect and diagnose acute patient conditions,which has always been a challenging task.Recently,deep learning models such as recurrent neural networks(RNNs)have demonstrated their strong potential on predicting such events.However,in real deployment,the patient data are continuously coming and there is no effective adaptation mechanism for RNN to incorporate those new data and become more accurate.Methods.In this study,we propose a novel self-correcting mechanism for RNN to fill in this gap.Our mechanism feeds prediction errors from the predictions of previous timestamps into the prediction of the current timestamp,so that the model can“learn”from previous predictions.We also proposed a regularization method that takes into account not only the model’s prediction errors on the labels but also its estimation errors on the input data.Results.We compared the performance of our proposed method with the conventional deep learning models on two real-world clinical datasets for the task of acute kidney injury(AKI)prediction and demonstrated that the proposed model achieved an area under ROC curve at 0.893 on the MIMIC-III dataset and 0.871 on the Philips eICU dataset.Conclusions.The proposed self-correcting RNNs demonstrated effectiveness in AKI prediction and have the potential to be applied to clinical applications.
基金MOE Academic Research Fund(AcRF)Tier 1 FRC WBS R-608-000-301-114the National Research Foundation Singapore under its AI Singapore Pro-gramme award number AISG-100E-2020-055.
文摘Background:In charge of dispatching the ambulances,Emergency Medical Services(EMS)call center specialists often have difficulty deciding the acuity of a case given the information they can gather within a limited time.Although there are protocols to guide their decision-making,observed performance can still lack sensitivity and specificity.Machine learning models have been known to capture complex relationships that are subtle,and well-trained data models can yield accurate predictions in a split of a second.Methods:In this study,we proposed a proof-of-concept approach to construct a machine learning model to better predict the acuity of emergency cases.We used more than 360,000 structured emergency call center records of cases received by the national emergency call center in Singapore from 2018 to 2020.Features were created using call records,and multiple machine learning models were trained.Results:A Random Forest model achieved the best performance,reducing the over-triage rate by an absolute margin of 15%compared to the call center specialists while maintaining a similar level of under-triage rate.Conclusions:The model has the potential to be deployed as a decision support tool for dispatchers alongside current protocols to optimize ambulance dispatch triage and the utilization of emergency ambulance resources.
