scholarly journals Evaluation framework to guide implementation of AI systems into healthcare settings

2021 ◽  
Vol 28 (1) ◽  
pp. e100444
Author(s):  
Sandeep Reddy ◽  
Wendy Rogers ◽  
Ville-Petteri Makinen ◽  
Enrico Coiera ◽  
Pieta Brown ◽  
...  
Keyword(s):  
Real World ◽  
Model Development ◽  
Evaluation Framework ◽  
Theoretic Approach ◽  
World Systems ◽  
Translation Research ◽  
Healthcare Settings ◽  
International Panel ◽  
Health Technology Evaluation ◽  
Main Components

ObjectivesTo date, many artificial intelligence (AI) systems have been developed in healthcare, but adoption has been limited. This may be due to inappropriate or incomplete evaluation and a lack of internationally recognised AI standards on evaluation. To have confidence in the generalisability of AI systems in healthcare and to enable their integration into workflows, there is a need for a practical yet comprehensive instrument to assess the translational aspects of the available AI systems. Currently available evaluation frameworks for AI in healthcare focus on the reporting and regulatory aspects but have little guidance regarding assessment of the translational aspects of the AI systems like the functional, utility and ethical components.MethodsTo address this gap and create a framework that assesses real-world systems, an international team has developed a translationally focused evaluation framework termed ‘Translational Evaluation of Healthcare AI (TEHAI)’. A critical review of literature assessed existing evaluation and reporting frameworks and gaps. Next, using health technology evaluation and translational principles, reporting components were identified for consideration. These were independently reviewed for consensus inclusion in a final framework by an international panel of eight expert.ResultsTEHAI includes three main components: capability, utility and adoption. The emphasis on translational and ethical features of the model development and deployment distinguishes TEHAI from other evaluation instruments. In specific, the evaluation components can be applied at any stage of the development and deployment of the AI system.DiscussionOne major limitation of existing reporting or evaluation frameworks is their narrow focus. TEHAI, because of its strong foundation in translation research models and an emphasis on safety, translational value and generalisability, not only has a theoretical basis but also practical application to assessing real-world systems.ConclusionThe translational research theoretic approach used to develop TEHAI should see it having application not just for evaluation of clinical AI in research settings, but more broadly to guide evaluation of working clinical systems.

EXPLORING HEALTHCARE ARCHITECTURE THROUGH THE MEDIUM OF FILM: Motives and Techniques

2014 ◽  
Vol 8 (1) ◽  
pp. 29
Author(s):  
Stephen Verderber
Keyword(s):  
Real World ◽  
The Real ◽  
Healthcare Settings ◽  
Interdisciplinary Field ◽  
Body Of Knowledge ◽  
Behavior Setting ◽  
Physical Environments ◽  
Scant Attention ◽  
History Of

The interdisciplinary field of person-environment relations has, from its origins, addressed the transactional relationship between human behavior and the built environment. This body of knowledge has been based upon qualitative and quantitative assessment of phenomena in the “real world.” This knowledge base has been instrumental in advancing the quality of real, physical environments globally at various scales of inquiry and with myriad user/client constituencies. By contrast, scant attention has been devoted to using simulation as a means to examine and represent person-environment transactions and how what is learned can be applied. The present discussion posits that press-competency theory, with related aspects drawn from functionalist-evolutionary theory, can together function to help us learn of how the medium of film can yield further insights to person-environment (P-E) transactions in the real world. Sampling, combined with extemporary behavior setting analysis, provide the basis for this analysis of healthcare settings as expressed throughout the history of cinema. This method can be of significant aid in examining P-E transactions across diverse historical periods, building types and places, healthcare and otherwise, otherwise logistically, geographically, or temporally unattainable in real time and space.

Phenomenological Modelling

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Real World ◽  
Biological Diversity ◽  
False Positives ◽  
World Systems ◽  
Economic Activities ◽  
Essential Services ◽  
Low Dimension ◽  
Convergent Cross Mapping

Detecting causal interactions among climatic, environmental, and human forces in complex biophysical systems is essential for understanding how these systems function and how public policies can be devised that protect the flow of essential services to biological diversity, agriculture, and other core economic activities. Convergent Cross Mapping (CCM) detects causal networks in real-world systems diagnosed with deterministic, low-dimension, and nonlinear dynamics. If CCM detects correspondence between phase spaces reconstructed from observed time series variables, then the variables are determined to causally interact in the same dynamic system. CCM can give false positives by misconstruing synchronized variables as causally interactive. Extended (delayed) CCM screens for false positives among synchronized variables.

scholarly journals Higher-order temporal network effects through triplet evolution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Higher Order ◽  
Prediction Algorithm ◽  
Interaction Patterns ◽  
Temporal Networks ◽  
World Systems ◽  
Order Interaction ◽  
Space And Time ◽  
Pairwise Interactions

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.

scholarly journals Conceptualising changes to tobacco and alcohol policy as affecting a single interlinked system

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Duncan Gillespie ◽  
Jenny Hatchard ◽  
Hazel Squires ◽  
Anna Gilmore ◽  
Alan Brennan
Keyword(s):  
Health Economic ◽  
Policy Change ◽  
Conceptual Understanding ◽  
Communicable Disease ◽  
Model Development ◽  
Economic Models ◽  
Alcohol Policy ◽  
Policy System ◽  
The People ◽  
Main Components

Abstract Background To support a move towards a coordinated non-communicable disease approach in public health policy, it is important to conceptualise changes to policy on tobacco and alcohol as affecting a single interlinked system. For health economic models to effectively inform policy, the first step in their development should be to develop a conceptual understanding of the system complexity that is likely to affect the outcomes of policy change. Our aim in this study was to support the development and interpretation of health economic models of the effects of changes to tobacco and alcohol policies by developing a conceptual understanding of the main components and mechanisms in the system that links policy change to outcomes. Methods Our study was based on a workshop from which we captured data on participant discussions on the joint tobacco–alcohol policy system. To inform these discussions, we prepared with a literature review and a survey of participants. Participants were academics and policy professionals who work in the United Kingdom. Data were analysed thematically to produce a description of the main components and mechanisms within the system. Results Of the people invited, 24 completed the survey (18 academic, 6 policy); 21 attended the workshop (16 academic, 5 policy). Our analysis identified eleven mechanisms through which individuals might modify the effects of a policy change, which include mechanisms that might lead to linked effects of policy change on tobacco and alcohol consumption. We identified ten mechanisms by which the tobacco and alcohol industries might modify the effects of policy changes, grouped into two categories: Reducing policy effectiveness; Enacting counter-measures. Finally, we identified eighteen research questions that indicate potential avenues for further work to understand the potential outcomes of policy change. Conclusions Model development should carefully consider the ways in which individuals and the tobacco and alcohol industries might modify the effects of policy change, and the extent to which this results in an unequal societal distribution of outcomes. Modelled evidence should then be interpreted in the light of the conceptual understanding of the system that the modelling necessarily simplifies in order to predict the outcomes of policy change.

scholarly journals Asymmetry underlies stability in power grids

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ferenc Molnar ◽  
Takashi Nishikawa ◽  
Adilson E. Motter
Keyword(s):  
Symmetry Breaking ◽  
Real World ◽  
Power Grids ◽  
Stable Operation ◽  
World Systems ◽  
Symmetric State ◽  
Network Systems ◽  
Additional Improvement ◽  
The Stability ◽  
General Method

AbstractBehavioral homogeneity is often critical for the functioning of network systems of interacting entities. In power grids, whose stable operation requires generator frequencies to be synchronized—and thus homogeneous—across the network, previous work suggests that the stability of synchronous states can be improved by making the generators homogeneous. Here, we show that a substantial additional improvement is possible by instead making the generators suitably heterogeneous. We develop a general method for attributing this counterintuitive effect to converse symmetry breaking, a recently established phenomenon in which the system must be asymmetric to maintain a stable symmetric state. These findings constitute the first demonstration of converse symmetry breaking in real-world systems, and our method promises to enable identification of this phenomenon in other networks whose functions rely on behavioral homogeneity.

scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

scholarly journals Modeling and simulation of economic processes

2010 ◽  
Author(s):  
Bogdan Brumar
Keyword(s):  
Decision Analysis ◽  
Modeling And Simulation ◽  
Real World ◽  
Analytical Methods ◽  
Simulation Experiment ◽  
Management Decision ◽  
Economic Systems ◽  
World Systems ◽  
Simulation Techniques ◽  
Laborious Work

In general, any activity requires a longer action often characterized by a degree of uncertainty, insecurity, in terms of size of the objective pursued. Because of the complexity of real economic systems, the stochastic dependencies between different variables and parameters considered, not all systems can be adequately represented by a model that can be solved by analytical methods and covering all issues for management decision analysis-economic horizon real. Often in such cases, it is considered that the simulation technique is the only alternative available. Using simulation techniques to study real-world systems often requires a laborious work. Making a simulation experiment is a process that takes place in several stages.

scholarly journals Improving speed of models for improved real-world decision-making

2021 ◽  
Author(s):  
Jason Thompson ◽  
Haifeng Zhao ◽  
Sachith Seneviratne ◽  
Rohan Byrne ◽  
Rajith Vidanaarachichi ◽  
...  
Keyword(s):  
High Performance Computing ◽  
Real World ◽  
High Performance ◽  
Time Pressure ◽  
Practical Importance ◽  
Model Development ◽  
Sudden Onset ◽  
Health Crisis ◽  
Performance Improvements ◽  
Performance Computing

The sudden onset of the COVID-19 global health crisis and as-sociated economic and social fall-out has highlighted the im-portance of speed in modeling emergency scenarios so that ro-bust, reliable evidence can be placed in policy and decision-makers’ hands as swiftly as possible. For computational social scientists who are building complex policy models but who lack ready access to high-performance computing facilities, such time-pressure can hinder effective engagement. Popular and ac-cessible agent-based modeling platforms such as NetLogo can be fast to develop, but slow to run when exploring broad param-eter spaces on individual workstations. However, while deploy-ment on high-performance computing (HPC) clusters can achieve marked performance improvements, transferring models from workstations to HPC clusters can also be a technically challenging and time-consuming task. In this paper we present a set of generic templates that can be used and adapted by NetLogo users who have access to HPC clusters but require ad-ditional support for deploying their models on such infrastruc-ture. We show that model run-time speed improvements of be-tween 200x and 400x over desktop machines are possible using 1) a benchmark ‘wolf-sheep predation’ model in addition to 2) an example drawn from our own work modeling the spread of COVID-19 in Victoria, Australia. We describe how a focus on improving model speed is non-trivial for model development and discuss its practical importance for improved policy and de-cision-making in the real world. We provide all associated doc-umentation in a linked git repository.

scholarly journals Identifying important nodes from content-associated heterogeneous graph by LeaderRank

2021 ◽  
Vol 2113 (1) ◽  
pp. 012082
Author(s):  
Yulong Dai ◽  
Qiyou Shen ◽  
Xiangqian Xu ◽  
Jun Yang
Keyword(s):  
Real World ◽  
Experimental Analysis ◽  
Differential Information ◽  
World Systems ◽  
Ranking Problem ◽  
Data Set ◽  
Importance Ranking ◽  
Basic Concepts ◽  
Important Nodes ◽  
Graph Nodes

Abstract Most real-world systems consist of a large number of interacting entities of many types. However, most of the current researches on systems are based on the assumption that the type of node or link in the network is unique. In other words, the network is homogeneous, containing the same type of nodes and links. Based on this assumption, differential information between nodes and edges is ignored. This paper firstly introduces the research background, challenges and significance of this research. Secondly, the basic concepts of the model are introduced. Thirdly, a novel type-sensitive LeaderRank algorithm is proposed and combined with distance rule to solve the importance ranking problem of content-associated heterogeneous graph nodes. Finally, the writer influence data set is used for experimental analysis to further prove the validity of the model.

Sign in / Sign up

Export Citation Format

Share Document