scholarly journals An openEHR Approach to Detailed Clinical Model Development: Tobacco Smoking Summary Archetype as a Case Study

2019 ◽  
Vol 10 (02) ◽  
pp. 219-228
Author(s):  
Ping-Cheng Wei ◽  
Koray Atalag ◽  
Karen Day
Keyword(s):  
Decision Making ◽  
Tacit Knowledge ◽  
Tobacco Smoking ◽  
Model Development ◽  
Data Reuse ◽  
Data Interoperability ◽  
Clinical Model ◽  
Domain Experts ◽  
Model Driven ◽  
Open Standard

Background Data modeling for electronic health records (EHRs) is complex, requiring technological and cognitive sophistication. The openEHR approach leverages the tacit knowledge of domain experts made explicit in a model development process aiming at interoperability and data reuse. Objective The purpose of our research was to explore the process that enabled the aggregation of the tacit knowledge of domain experts in an explicit form using the Clinical Knowledge Manager (CKM) platform and associated assets. The Tobacco Smoking Summary archetype is used to illustrate this. Methods Three methods were used to triangulate findings: (1) observation of CKM discussions by crowdsourced domain experts in two reviews, (2) observation of editor discussions and decision-making, and (3) interviews with eight domain experts. CKM discussions were analyzed for content and editor discussions for decision-making, and interviews were thematically analyzed to explore in depth the explication of tacit knowledge. Results The Detailed Clinical Model (DCM) process consists of a set of reviews by domain experts, with each review followed by editorial discussions and decision-making until an agreement is reached among reviewers and editors that the DCM is publishable. Interviews revealed three themes: (1) data interoperability and reusability, (2) accurate capture of patient data, and (3) challenges of sharing tacit knowledge. Discussion The openEHR approach to developing an open standard revealed a complex set of conditions for a successful interoperable archetype, such as leadership, maximal dataset, crowdsourced domain expertise and tacit knowledge made explicit, editorial vision, and model-driven software. Aggregated tacit knowledge that is explicated into a DCM enables the ability to collect accurate data and plan for the future. Conclusion The process based on the CKM platform enables domain experts and stakeholders to be heard and to contribute to mutually designed standards that align local protocols and agendas to international interoperability requirements.

scholarly journals Model-driven development platform selection: four industry case studies

2021 ◽  
Author(s):  
Siamak Farshidi ◽  
Slinger Jansen ◽  
Sven Fortuin
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Decision Makers ◽  
Development Activity ◽  
Model Driven Development ◽  
Domain Experts ◽  
Theoretical Contribution ◽  
Model Driven ◽  
Development Platform ◽  
Software Production

AbstractModel-driven development platforms shift the focus of software development activity from coding to modeling for enterprises. A significant number of such platforms are available in the market. Selecting the best fitting platform is challenging, as domain experts are not typically model-driven deployment platform experts and have limited time for acquiring the needed knowledge. We model the problem as a multi-criteria decision-making problem and capture knowledge systematically about the features and qualities of 30 alternative platforms. Through four industry case studies, we confirm that the model supports decision-makers with the selection problem by reducing the time and cost of the decision-making process and by providing a richer list of options than the enterprises considered initially. We show that having decision knowledge readily available supports decision-makers in making more rational, efficient, and effective decisions. The study’s theoretical contribution is the observation that the decision framework provides a reliable approach for creating decision models in software production.

Information technology support for integrated supply chain modeling

2008 ◽  
Vol 27 (1) ◽  
pp. 3-13
Author(s):  
Charu Chandra ◽  
Jānis Grabis
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Supply Chain ◽  
Process Modeling ◽  
Service Oriented Architecture ◽  
Model Development ◽  
Integrated Models ◽  
Supply Chain Modeling ◽  
Decision Making Models ◽  
Integrated Supply Chain

Multiple interrelated decision-making models are frequently used in supply chain modeling. Model integration is a precondition for efficient development and utilization of these models. This paper discusses use of modern information technology (IT) techniques and methods for integration of supply chain decision-making models. The overall approach to using IT at various stages of model development is presented. Data and process modeling techniques are used to developed semi-formalized representation of integrated models. These models support integration of decision-making components with other parts of supply chain information system. Process modeling is also used to describe interrelationships among multiple decision-making models. This representation is used as the basis for implementation of integrated models. The service-oriented architecture is proposed as an implementation platform. The presented discussion serves as the basis for further developments in developing integrated supply chain decision-making models.

scholarly journals STANDARDISING MAINTENANCE JOBS TO IMPROVE GROUPING DECISION MAKING

2021 ◽  
Vol 1 ◽  
pp. 2701-2710
Author(s):  
Julie Krogh Agergaard ◽  
Kristoffer Vandrup Sigsgaard ◽  
Niels Henrik Mortensen ◽  
Jingrui Ge ◽  
Kasper Barslund Hansen ◽  
...  
Keyword(s):  
Decision Making ◽  
Tacit Knowledge ◽  
True Variance ◽  
Complex Decision ◽  
Maintenance Decision ◽  
Maintenance Activities

AbstractMaintenance decision making is an important part of managing the costs, effectiveness and risk of maintenance. One way to improve maintenance efficiency without affecting the risk picture is to group maintenance jobs. Literature includes many examples of algorithms for the grouping of maintenance activities. However, the data is not always available, and with increasing plant complexity comes increasingly complex decision requirements, making it difficult to leave the decision making up to algorithms.This paper suggests a framework for the standardisation of maintenance data as an aid for maintenance experts to make decisions on maintenance grouping. The standardisation improves the basis for decisions, giving an overview of true variance within the available data. The goal of the framework is to make it simpler to apply tacit knowledge and make right decisions.Applying the framework in a case study showed that groups can be identified and reconfigured and potential savings easily estimated when maintenance jobs are standardised. The case study enabled an estimated 7%-9% saved on the number of hours spent on the investigated jobs.

Democracy's Wisdom: An Aristotelian Middle Way for Collective Judgment

2013 ◽  
Vol 107 (1) ◽  
pp. 104-122 ◽  
Author(s):  
JOSIAH OBER
Keyword(s):  
Decision Making ◽  
Common Interest ◽  
Legislative Process ◽  
Domain Experts ◽  
Middle Way ◽  
Satisfactory Model ◽  
Collective Judgment ◽  
The World ◽  
Epistemic Approach ◽  
Multiple Domains

A satisfactory model of decision-making in an epistemic democracy must respect democratic values, while advancing citizens’ interests, by taking account of relevant knowledge about the world. Analysis of passages in Aristotle and legislative process in classical Athens points to a “middle way” between independent-guess aggregation and deliberation: an epistemic approach to decision-making that offers a satisfactory model of collective judgment that is both time-sensitive and capable of setting agendas endogenously. By aggregating expertise across multiple domains, Relevant Expertise Aggregation (REA) enables a body of minimally competent voters to make superior choices among multiple options, on matters of common interest. REA differs from a standard Condorcet jury in combining deliberation with voting based on judgments about the reputations and arguments of domain-experts.

scholarly journals Generating Collaborative Systems for Digital Libraries: a Model-Driven Approach

2010 ◽  
Vol 29 (4) ◽  
pp. 171 ◽  
Author(s):  
Alessio Malizia ◽  
Paolo Bottoni ◽  
S. Levialdi
Keyword(s):  
Digital Library ◽  
User Interfaces ◽  
Digital Libraries ◽  
Automatic Generation ◽  
Visual Language ◽  
Domain Experts ◽  
Model Driven ◽  
Neutral Models ◽  
Definition Of ◽  
High Level

The design and development of a digital library involves different stakeholders, such as: information architects, librarians, and domain experts, who need to agree on a common language to describe, discuss, and negotiate the services the library has to offer. To this end, high-level, language-neutral models have to be devised. Metamodeling techniques favor the definition of domainspecific visual languages through which stakeholders can share their views and directly manipulate representations of the domain entities. This paper describes CRADLE (Cooperative-Relational Approach to Digital Library Environments), a metamodel-based framework and visual language for the definition of notions and services related to the development of digital libraries. A collection of tools allows the automatic generation of several services, defined with the CRADLE visual language, and of the graphical user interfaces providing access to them for the final user. The effectiveness of the approach is illustrated by presenting digital libraries generated with CRADLE, while the CRADLE environment has been evaluated by using the cognitive dimensions framework.

scholarly journals Process and Content in Decisions from Memory

2019 ◽  
Author(s):  
Wenjia Joyce Zhao ◽  
Russell Richie ◽  
Sudeep Bhatia
Keyword(s):  
Decision Making ◽  
Ethical Decision Making ◽  
Memory Search ◽  
Well Being ◽  
Quantitative Model ◽  
Decision Processes ◽  
Language Models ◽  
Choice Data ◽  
Model Driven ◽  
Decision Context

Information stored in memory influences the formation of preferences and beliefs in most everyday decision tasks. The richness of this information, and the complexity inherent in interacting memory and decision processes, makes the quantitative model-driven analysis of such decisions very difficult. In this paper we present a general framework that is capable of addressing the theoretical and methodological barriers to building formal models of naturalistic memory-based decision making. Our framework implements established theories of memory search and decision making within a single integrated cognitive system, and uses computational language models to quantify the thoughts over which memory and decision processes operate. It can thus describe both the content of the information that is sampled from memory, as well as the processes involved in retrieving and evaluating this information in order to make a decision. Furthermore, our framework is tractable, and the parameters that characterize memory-based decisions can be recovered using thought-listing and choice data from existing experimental tasks, and in turn be used to make quantitative predictions regarding choice probability, length of deliberation, retrieved thoughts, and the effects of decision context. We showcase the power and generality of our framework by applying it to study risk perception, consumer behavior, financial decision making, ethical decision making, legal decision making, food choice, and judgments about well-being, society and culture.

scholarly journals Development of Fuzzy Based Autoregressive Moving Average Exogenous Input Model for Water Flow in Nigerian Kanji Hydro-Power Dam

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
D. O Araromi
Keyword(s):  
Controller Design ◽  
Moving Average ◽  
Model Development ◽  
Autoregressive Moving Average ◽  
Hydro Power ◽  
Model Driven ◽  
Robust Control System ◽  
Input Variables ◽  
Water Outflow ◽  
Safety Operation

Design of robust control system for any system requires model-driven approach. Therefore, it becomes imperative to develop a dynamic model suitable for controller design on safety operation of hydropower dam for power production in Kanji dam in Nigeria. Model for reservoir flow was developed in MATLAB environment using Fuzzy Based Autoregressive Moving Average Exogenous Input (FARMAX) model structure in this study. The data used for model development covered a period of ten years (2003-2013). It consists of water inflow (WI), water outflow (WO) and spillage (S). WI and S are input variables while WO was the output variable. The model obtained using the unsmoothed data with an outlier gave -14.115%, -0.302 and 610.317 for fit, R2 and RMSE, respectively. Unsmoothed data with no outlier gave -13.802%, -0.295 and 608.643 corresponding to fit, R2 and RMSE, respectively. The model obtained using the smoothed data in the presence of an outlier gave 80.533%, 0.962 and 104.113 for fit, R2 and RMSE, respectively. Smoothed data in the absence of outlier gave 81.533%, 0.962 and 99.637 for to fit, R2 and RMSE, respectively. FARMAX has the best fit value of 87.8774% when number of rules was equal to 3 with optima model order of 3 1 4 3. The model can serve as a decision support system in evaluating the optimal reservoir operation policies in real time.

scholarly journals STRUCTURAL MODEL DEVELOPMENT BASED ON EXPERT-PSYCHOLOGICAL APPROACH IN MANAGEMENT DECISION-MAKING PROCESSES

2017 ◽  
Vol 2017 (2) ◽  
pp. 191-195
Author(s):  
Татьяна Карлова ◽  
Tatyana Karlova ◽  
Александр Бекмешов ◽  
Aleksandr Bekmeshov ◽  
Марианна Михайлова ◽  
...  
Keyword(s):  
Decision Making ◽  
Structural Model ◽  
Model Development ◽  
Management Decision ◽  
Psychological Approach ◽  
Management Decision Making ◽  
Decision Making Processes

scholarly journals A Survey of Domain Knowledge Elicitation in Applied Machine Learning

2021 ◽  
Vol 5 (12) ◽  
pp. 73
Author(s):  
Daniel Kerrigan ◽  
Jessica Hullman ◽  
Enrico Bertini
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Development Process ◽  
Model Development ◽  
Knowledge Elicitation ◽  
Future Directions ◽  
Domain Experts ◽  
Applied Machine Learning ◽  
Elicitation Process ◽  
Model Development Process

Eliciting knowledge from domain experts can play an important role throughout the machine learning process, from correctly specifying the task to evaluating model results. However, knowledge elicitation is also fraught with challenges. In this work, we consider why and how machine learning researchers elicit knowledge from experts in the model development process. We develop a taxonomy to characterize elicitation approaches according to the elicitation goal, elicitation target, elicitation process, and use of elicited knowledge. We analyze the elicitation trends observed in 28 papers with this taxonomy and identify opportunities for adding rigor to these elicitation approaches. We suggest future directions for research in elicitation for machine learning by highlighting avenues for further exploration and drawing on what we can learn from elicitation research in other fields.

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