In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community ca...In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home(H2H)program that has been operating since 2017.In this follow on practice and policy article,we further elaborate on Singapore's H2H program and care model,and its supporting AI model for multiple readmission prediction,in the following ways:(1)by providing updates on the AI and supporting information systems,(2)by reporting on customer engagement and related service delivery outcomes including staff‐related time savings and patient benefits in terms of bed days saved,(3)by sharing lessons learned with respect to(i)analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants,(ii)balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables,and(iii)the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems,(4)by highlighting how this H2H effort supported broader Covid‐19 response efforts across Singapore's public healthcare system,and finally(5)by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards.For the convenience of the reader,some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.展开更多
A recent systematic experimental characterisation of technological thin films,based on elaborated design of experiments as well as probe calibration and correction procedures,allowed for the first time the determinati...A recent systematic experimental characterisation of technological thin films,based on elaborated design of experiments as well as probe calibration and correction procedures,allowed for the first time the determination of nanoscale friction under the concurrent influence of several process parameters,comprising normal forces,sliding velocities,and temperature,thus providing an indication of the intricate correlations induced by their interactions and mutual effects.This created the preconditions to undertake in this work an effort to model friction in the nanometric domain with the goal of overcoming the limitations of currently available models in ascertaining the effects of the physicochemical processes and phenomena involved in nanoscale contacts.Due to the stochastic nature of nanoscale friction and the relatively sparse available experimental data,meta-modelling tools fail,however,at predicting the factual behaviour.Based on the acquired experimental data,data mining,incorporating various state-of-the-art machine learning(ML)numerical regression algorithms,is therefore used.The results of the numerical analyses are assessed on an unseen test dataset via a comparative statistical validation.It is therefore shown that the black box ML methods provide effective predictions of the studied correlations with rather good accuracy levels,but the intrinsic nature of such algorithms prevents their usage in most practical applications.Genetic programming-based artificial intelligence(AI)methods are consequently finally used.Despite the marked complexity of the analysed phenomena and the inherent dispersion of the measurements,the developed AI-based symbolic regression models allow attaining an excellent predictive performance with the respective prediction accuracy,depending on the sample type,between 72%and 91%,allowing also to attain an extremely simple functional description of the multidimensional dependence of nanoscale friction on the studied variable process parameters.An effective tool for nanoscale friction prediction,adaptive control purposes,and further scientific and technological nanotribological analyses is thus obtained.展开更多
文摘In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home(H2H)program that has been operating since 2017.In this follow on practice and policy article,we further elaborate on Singapore's H2H program and care model,and its supporting AI model for multiple readmission prediction,in the following ways:(1)by providing updates on the AI and supporting information systems,(2)by reporting on customer engagement and related service delivery outcomes including staff‐related time savings and patient benefits in terms of bed days saved,(3)by sharing lessons learned with respect to(i)analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants,(ii)balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables,and(iii)the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems,(4)by highlighting how this H2H effort supported broader Covid‐19 response efforts across Singapore's public healthcare system,and finally(5)by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards.For the convenience of the reader,some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.
基金The work described in this paper is enabled by using the equipment funded via the EU European Regional Development Fund project entitled“Research Infrastructure for Campus-based Laboratories at the University of Rijeka–RISK”(Project RC.2.2.06-0001)the support of the University of Rijeka,Croatia,grant entitled“Advanced mechatronics devices for smart technological solutions”(Grant uniri-tehnic-18-32).
文摘A recent systematic experimental characterisation of technological thin films,based on elaborated design of experiments as well as probe calibration and correction procedures,allowed for the first time the determination of nanoscale friction under the concurrent influence of several process parameters,comprising normal forces,sliding velocities,and temperature,thus providing an indication of the intricate correlations induced by their interactions and mutual effects.This created the preconditions to undertake in this work an effort to model friction in the nanometric domain with the goal of overcoming the limitations of currently available models in ascertaining the effects of the physicochemical processes and phenomena involved in nanoscale contacts.Due to the stochastic nature of nanoscale friction and the relatively sparse available experimental data,meta-modelling tools fail,however,at predicting the factual behaviour.Based on the acquired experimental data,data mining,incorporating various state-of-the-art machine learning(ML)numerical regression algorithms,is therefore used.The results of the numerical analyses are assessed on an unseen test dataset via a comparative statistical validation.It is therefore shown that the black box ML methods provide effective predictions of the studied correlations with rather good accuracy levels,but the intrinsic nature of such algorithms prevents their usage in most practical applications.Genetic programming-based artificial intelligence(AI)methods are consequently finally used.Despite the marked complexity of the analysed phenomena and the inherent dispersion of the measurements,the developed AI-based symbolic regression models allow attaining an excellent predictive performance with the respective prediction accuracy,depending on the sample type,between 72%and 91%,allowing also to attain an extremely simple functional description of the multidimensional dependence of nanoscale friction on the studied variable process parameters.An effective tool for nanoscale friction prediction,adaptive control purposes,and further scientific and technological nanotribological analyses is thus obtained.
