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
Quillen proved that if a Hermitian bihomogeneous polynomial is strictly positive on the unit sphere, then repeated multiplication of the standard sesquilinear form to this polynomial eventually results in a sum of Her...Quillen proved that if a Hermitian bihomogeneous polynomial is strictly positive on the unit sphere, then repeated multiplication of the standard sesquilinear form to this polynomial eventually results in a sum of Hermitian squares. Catlin-D'Angelo and Varolin deduced this positivstellensatz of Quillen from the eventual positive-definiteness of an associated integral operator. Their arguments involve asymptotic expansions of the Bergman kernel. The goal of this article is to give an elementary proof of the positive-definiteness of this integral operator.展开更多
Quillen proved that repeated multiplication of the standard sesquilinear form to a positive Hermitian bihomogeneous polynomial eventually results in a sum of Hermitian squares,which was the first Hermitian analogue of...Quillen proved that repeated multiplication of the standard sesquilinear form to a positive Hermitian bihomogeneous polynomial eventually results in a sum of Hermitian squares,which was the first Hermitian analogue of Hilbert s seventeenth problem in the nondegenerate case.Later Catlin-D’Angelo generalized this positivstellensatz of Quillen to the case of Hermitian algebraic functions on holomorphic line bundles over compact complex manifolds by proving the eventual positivity of an associated integral operator.The arguments of Catlin-D’Angelo involve subtle asymptotic estimates of the Bergman kernel.In this article,the authors give an elementary and geometric proof of the eventual positivity of this integral operator,thereby yielding another proof of the corresponding positivstellensatz.展开更多
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.
文摘Quillen proved that if a Hermitian bihomogeneous polynomial is strictly positive on the unit sphere, then repeated multiplication of the standard sesquilinear form to this polynomial eventually results in a sum of Hermitian squares. Catlin-D'Angelo and Varolin deduced this positivstellensatz of Quillen from the eventual positive-definiteness of an associated integral operator. Their arguments involve asymptotic expansions of the Bergman kernel. The goal of this article is to give an elementary proof of the positive-definiteness of this integral operator.
基金partially supported by the Singapore Ministry of Education Academic Research Fund Tier 1 grant R-146-000-142-112。
文摘Quillen proved that repeated multiplication of the standard sesquilinear form to a positive Hermitian bihomogeneous polynomial eventually results in a sum of Hermitian squares,which was the first Hermitian analogue of Hilbert s seventeenth problem in the nondegenerate case.Later Catlin-D’Angelo generalized this positivstellensatz of Quillen to the case of Hermitian algebraic functions on holomorphic line bundles over compact complex manifolds by proving the eventual positivity of an associated integral operator.The arguments of Catlin-D’Angelo involve subtle asymptotic estimates of the Bergman kernel.In this article,the authors give an elementary and geometric proof of the eventual positivity of this integral operator,thereby yielding another proof of the corresponding positivstellensatz.
