Design Of Decision Support System Research Articles

The anatomy of clinical decision-making: aligning AI design with ICU routines

PurposeIn the face of numerous explanations for why AI-driven decision support systems (DSS) have failed to deliver on their promise of improving organizational decision-making, this paper problematizes the under-theorized mismatch between the design of DSS and the actual decision-making processes that the technology is supposed to support. We examine this mismatch by studying the implementation of a DSS in the intensive care unit (ICU) of a large academic hospital.Design/methodology/approachBased on 27 months of ethnographic fieldwork, we contend that the studied DSS was designed on the assumption that individual intensivists are responsible for making life-critical discharge decisions at one particular moment in time.FindingsHowever, our study of actual decision-making practices reveals that discharge decision-making is instead a protracted process, involving multiple actors fragmented across time and space. To account for these complexities, we advocate for a “dynamic routines” perspective, which highlights the actual patterns of action pursued throughout a clinical decision-making process.Originality/valueOur application of this perspective contributes to a more granular understanding of discharge decision-making, which can help future DSS designers better grasp the peculiarities and complexities—or “anatomy”—of the decision-making process. We also suggest integrating an “anticipatory ethnographic approach” into the design and pre-implementation phases of future DSS to help bridge the current gap between design assumptions and actual decision-making practices.

Design of Digital Economy Industry Investment Decision Support System Based on Fuzzy Comprehensive Evaluation Method

Design of Digital Economy Industry Investment Decision Support System Based on Fuzzy Comprehensive Evaluation Method

Decision Support System Design To Determine The Feasibility of Students Staying In The Dormitory Using The Simple Additive Weighting Method

The research aims to analyze the feasibility of students staying in the dormitory using the Simple Additive Weighting (SAW) method. The process of admitting residents is currently done manually; therefore, a system is needed to facilitate dormitory management, supported by the SAW method as a systematic approach to identify and evaluate factors influencing the feasibility of staying in the dormitory. In the SAW method, criteria weights are assigned to each factor based on their importance according to the feasibility level determined by the dormitory management. Subsequently, calculations are performed using the SAW formula to obtain the final feasibility score for each student. Based on the test results, the developed system can facilitate and expedite the dormitory management in determining the selection process for students who wish to stay in the dormitory, as well as improve decision-making accuracy.

Design of a Machine Learning-based Decision Support System for Product Scheduling on Non Identical Parallel Machines

Design of a Machine Learning-based Decision Support System for Product Scheduling on Non Identical Parallel Machines

Design and Implementation of the Employment Management Decision Support System based on Machine Learning

To address the challenges of current college student employment management, this study designed and implemented a machine learning-based decision support system for college student employment management. The system collects and analyzes multidimensional data, uses machine learning algorithms for prediction and matching, provides personalized employment guidance for students, and provides decision support for universities and enterprises. The research results indicate that the system can effectively improve the efficiency and accuracy of employment guidance, promote school-enterprise cooperation, and achieve a win-win situation for all parties.

Human factors methods in the design of digital decision support systems for population health: a scoping review

BackgroundWhile Human Factors (HF) methods have been applied to the design of decision support systems (DSS) to aid clinical decision-making, the role of HF to improve decision-support for population health outcomes is less understood. We sought to comprehensively understand how HF methods have been used in designing digital population health DSS.Materials and methodsWe searched English documents published in health sciences and engineering databases (Medline, Embase, PsychINFO, Scopus, Comendex, Inspec, IEEE Xplore) between January 1990 and September 2023 describing the development, validation or application of HF principles to decision support tools in population health.ResultsWe identified 21,581 unique records and included 153 studies for data extraction and synthesis. We included research articles that had a target end-user in population health and that used HF. HF methods were applied throughout the design lifecycle. Users were engaged early in the design lifecycle in the needs assessment and requirements gathering phase and design and prototyping phase with qualitative methods such as interviews. In later stages in the lifecycle, during user testing and evaluation, and post deployment evaluation, quantitative methods were more frequently used. However, only three studies used an experimental framework or conducted A/B testing.ConclusionsWhile HF have been applied in a variety of contexts in the design of data-driven DSSs for population health, few have used Human Factors to its full potential. We offer recommendations for how HF can be leveraged throughout the design lifecycle. Most crucially, system designers should engage with users early on and throughout the design process. Our findings can support stakeholders to further empower public health systems.

Supporting the Episodic Application of Clinical Guidelines over Significant Time Periods.

Medical errors contribute significantly to morbidity and mortality, emphasizing the critical role of Clinical Guidelines (GLs) in patient care. Automating GL application can enhance GL adherence, improve patient outcomes, and reduce costs. However, several barriers exist to GL implementation and real-time automated support. Challenges include creating a formalized, machine-comprehensible GL representation, and an episodic decision-support system for sporadic treatment advice. This system must accommodate the non-continuous nature of care delivery, including partial actions or partially met treatment goals. We describe the design and implementation of an episodic GL-based clinical decision support system and its retrospective technical evaluation using patient records from a geriatric center. Initial evaluation scores of the e-Picard system were promising, with a mean 94% correctness and 90% completeness based on 50 random pressure ulcer patients. Errors were mainly due to knowledge specification, algorithmic issues, and missing data. Post-corrections, scores improved to 100% correctness and a mean 97% completeness, with missing data still affecting completeness. The results validate the system's capability to assess guideline adherence and provide quality recommendations. Despite initial limitations, we have demonstrated the feasibility of providing, through the e-Picard episodic algorithm, realistic medical decision-making support for noncontinuous, intermittent consultations.

Cognitive Systems Engineering Issues in the Design of Machine Learning Systems

The objective of this panel is to better understand how machine learning (ML) systems influence users and to identify strategies to make such interactions more effective when designing decision support systems and joint cognitive systems for real-time control. The panelists will draw upon their insights from the fields of aviation, defense, ground transportation and medicine, as well as the literature on human-automation and human-AI interaction. The panelists will discuss findings regarding both the positive and negative influences that the design of such systems can have on perceptual, cognitive and decision making processes. They will further provide concrete examples of system designs. Topics for discussion will include: (a) The Ironies of AI Based on Machine Learning: Challenges and New Directions, (b) Understanding the Influences of ML on Joint Cognitive Performance, (c) Mixed Initiative Access to Information, (d) Focusing and Accelerating Decision Making, and (e) Designing the Operational Domain.

Design of Financial Data Analysis and Decision Support System based on Big Data

Design of Financial Data Analysis and Decision Support System based on Big Data

The Use of fMRI Regional Analysis to Automatically Detect ADHD Through a 3D CNN-Based Approach.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by a reduced attention span, hyperactivity, and impulsive behaviors, which typically manifest during childhood. This study employs functional magnetic resonance imaging (fMRI) to use spontaneous brain activity for classifying individuals with ADHD, focusing on a 3D convolutional neural network (CNN) architecture to facilitate the design of decision support systems. We developed a novel deep learning model based on 3D CNNs using the ADHD-200 database, which comprises datasets from NeuroImage (NI), New York University (NYU), and Peking University (PU). We used fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) data in three dimensions and performed a fivefold cross-validation to address the dataset imbalance. We aimed to verify the efficacy of our proposed 3D CNN by contrasting it with a fully connected neural network (FCNN) architecture. The 3D CNN achieved accuracy rates of 76.19% (NI), 69.92% (NYU), and 70.77% (PU) for fALFF data. The FCNN model yielded lower accuracy rates across all datasets. For generalizability, we trained on NI and NYU datasets and tested on PU. The 3D CNN achieved 69.48% accuracy on fALFF outperforming the FCNN. Our results demonstrate that using 3D CNNs for classifying fALFF data is an effective approach for diagnosing ADHD. Also, FCNN confirmed the efficiency of the designed model.

Interviews with farmers from the US corn belt highlight opportunity for improved decision support systems and continued structural barriers to farmland diversification

Diversifying high-input, monocropped landscapes like the US Corn Belt would provide both economic and ecosystem service benefits to the agricultural landscape. Decision support systems (DSS) and digital agriculture could help farmers decide if diversification is suitable for their operation. However, adoption of DSS by farmers remains low, likely due to lack of farmer engagement before and during the DSS development process. This study aimed to better understand the tasks, tools, and people involved in implementing farmland diversification with the goal to inform design of agricultural DSS. Semi-structured interviews were conducted with 11 farmers who had diversified their corn/soybean cropland with government-supported conservation programs (e.g., CRP, wetlands) and alternative crops (e.g., small grains, pasture) in the past four years. Interview data was transcribed and then analyzed using affinity diagramming. Results show farmers needed DSS to layer multiple sources of data and observations over several years to identify field productivity trends and drivers; spatial orientation of practices to fit management and field constraints; matching operation goals to alternative practices; financial planning and market exploration; and information on promising emerging practices like subsidized pollinator habitat. However, the interviews also highlighted structural barriers to diversification that DSS cannot or can only partially address. These included social pressures; market access; crop insurance policy; and quality of relationships with governmental agencies. Results indicate better DSS design can empower individual farmers to diversify cropland, but structural interventions will be needed to successfully diversify the agricultural landscape and support economic and ecosystem health.

Comparison and Evaluation of Learning Capabilities of Deep Learning Methods for Predicting Ship Motions

The development of intelligent ship control systems in real-world conditions relies heavily on the accurate identification and prediction of ship seakeeping and maneuvering trajectories. In this study, we comprehensively evaluate a selection of deep learning methods to assess their learning capabilities in terms of idealizing ship motion behavior in realistic operational environments. To recover real conditions, we utilize historical Automatic Identification System (AIS) data and a time domain 6 Degree of Freedom (6- DoF) grounding dynamics model to generate ship motion sequences for a Ro-Ro passenger ship operating in the Gulf of Finland. Via a rigorous evaluation process, we validate the performance of these methods using extensive data streams. The analysis includes the identification and estimation of uncertainties between two ports. The paper demonstrates the proficiency of the selected deep learning methods in capturing ship maneuvering features, their potential use in the design of ship control and intelligent decision support systems.

Design and Implementation of an Intelligent Metro Project Investment Decision Support System

The construction of subway projects involves tight engineering cycles, multiple technical challenges, and complex coordination among various stakeholders. Due to the influence of uncertain factors during the construction process, the investment in subway project construction exhibits non-linear changes over time. Investment decision-making is the process through which the investment entity determines its investment activities. For typical investment entities, project investment decision-making primarily entails analyzing and evaluating proposed engineering projects based on investigation, analysis, and argumentation, ultimately deciding whether to invest. With the widespread application of information technology (IT) across various fields, decision support systems (DSS) have emerged to enhance the decision-making capabilities of enterprise management. This article designs an intelligent subway project investment DSS, leveraging data mining (DM) technology to integrate DSS with a data warehouse (DW).

Design and Application of Decision Support System for Educational Management Based on Big Data

Decision support system (DSS) for educational management based on big data leverage advanced analytics and machine learning techniques to enhance decision-making processes within educational institutions. By collecting and analyzing large volumes of data from various sources such as student performance, administrative records, and institutional resources, the DSS provides valuable insights and predictive models to administrators and educators. These insights can inform strategic planning, resource allocation, student interventions, and curriculum development, ultimately improving student outcomes and organizational efficiency. With the ability to process vast amounts of data in real-time, the DSS enables timely and data-driven decision-making, empowering educational leaders to address challenges and capitalize on opportunities in today's complex educational landscape.The integration of big data analytics with the Recommender Ranking Decision Support System (RRDS) presents a transformative approach to enhancing educational management. This paper explores the application of big data analytics within the educational setting, focusing on the development and implementation of the RRDS. By leveraging large and diverse datasets, the RRDS enables personalized learning experiences, optimizes resource allocation, and enhances student outcomes. Through predictive modeling, the RRDS predicts student performance and identifies at-risk students, facilitating targeted interventions to support student success and retention. Additionally, the system offers personalized learning recommendations tailored to individual student needs, fostering engagement and motivation. The findings underscore the significance of data-driven decision-making in improving educational practices and institutional performance. As educational institutions increasingly embrace technology-driven solutions, the integration of big data analytics and RRDS stands poised to revolutionize educational management, ultimately leading to more effective teaching and learning methodologies and better outcomes for students.

To NIRS or not: understanding clinical decision-making of respiratory support management related to acute respiratory failure using Critical Decision Method

Clinical decisions related to noninvasive respiratory support (NIRS) for acute respiratory failure patients are complex. NIRS strategies have favorable benefits for patients when successful but NIRS failure comes with longer ICU stays, increased mortality, and more intubation-related and mechanical ventilator-associated complications. Our goal is to understand how physicians make decisions regarding NIRS and explore the differences between emergency physicians and critical care physicians—who commonly make these decisions. We used the critical decision method to conduct ten semi-structured interviews with five emergency physicians and five critical care physicians from ten different healthcare systems across the United States. We found that physicians similarly use objective and subjective clinical cues but differentially use temporal and contextual cues in their decision-making. Several insights identified in our study can be helpful in the design and development of clinical decision support systems for respiratory support decision-making. Specifically, we recommend two customized distinct clinical decision support systems for respiratory support decision-making, one for each designated clinical setting (i.e., ED and ICU) with tailored designs that support the different decision-making processes.

Challenges and Facilitation Approaches for the Participatory Design of AI-Based Clinical Decision Support Systems: Protocol for a Scoping Review.

In the last few years, there has been an increasing interest in the development of artificial intelligence (AI)-based clinical decision support systems (CDSS). However, there are barriers to the successful implementation of such systems in practice, including the lack of acceptance of these systems. Participatory approaches aim to involve future users in designing applications such as CDSS to be more acceptable, feasible, and fundamentally more relevant for practice. The development of technologies based on AI, however, challenges the process of user involvement and related methods. The aim of this review is to summarize and present the main approaches, methods, practices, and specific challenges for participatory research and development of AI-based decision support systems involving clinicians. This scoping review will follow the Joanna Briggs Institute approach to scoping reviews. The search for eligible studies was conducted in the databases MEDLINE via PubMed; ACM Digital Library; Cumulative Index to Nursing and Allied Health; and PsycInfo. The following search filters, adapted to each database, were used: Period January 01, 2012, to October 31, 2023, English and German studies only, abstract available. The scoping review will include studies that involve the development, piloting, implementation, and evaluation of AI-based CDSS (hybrid and data-driven AI approaches). Clinical staff must be involved in a participatory manner. Data retrieval will be accompanied by a manual gray literature search. Potential publications will then be exported into reference management software, and duplicates will be removed. Afterward, the obtained set of papers will be transferred into a systematic review management tool. All publications will be screened, extracted, and analyzed: title and abstract screening will be carried out by 2 independent reviewers. Disagreements will be resolved by involving a third reviewer. Data will be extracted using a data extraction tool prepared for the study. This scoping review protocol was registered on March 11, 2023, at the Open Science Framework. The full-text screening had already started at that time. Of the 3,118 studies screened by title and abstract, 31 were included in the full-text screening. Data collection and analysis as well as manuscript preparation are planned for the second and third quarter of 2024. The manuscript should be submitted towards the end of 2024. This review will describe the current state of knowledge on participatory development of AI-based decision support systems. The aim is to identify knowledge gaps and provide research impetus. It also aims to provide relevant information for policy makers and practitioners. DERR1-10.2196/58185.

The Minimally-Invasive Oral Glucose Minimal Model: Estimation of Gastric Retention, Glucose Rate of Appearance, and Insulin Sensitivity From Type 1 Diabetes Data Collected in Real-Life Conditions.

Modeling the effect of meal composition on glucose excursion would help in designing decision support systems (DSS) for type 1 diabetes (T1D) management. In fact, macronutrients differently affect post-prandial gastric retention (GR), rate of appearance (R[Formula: see text]), and insulin sensitivity (S[Formula: see text]). Such variables can be estimated, in inpatient settings, from plasma glucose (G) and insulin (I) data using the Oral glucose Minimal Model (OMM) coupled with a physiological model of glucose transit through the gastrointestinal tract (reference OMM, R-OMM). Here, we present a model able to estimate those quantities in daily-life conditions, using minimally-invasive (MI) technologies, and validate it against the R-OMM. Forty-seven individuals with T1D (weight =78±13 kg, age =42±10 yr) underwent three 23-hour visits, during which G and I were frequently sampled while wearing continuous glucose monitoring (CGM) and insulin pump (IP). Using a Bayesian Maximum A Posteriori estimator, R-OMM was identified from plasma G and I measurements, and MI-OMM was identified from CGM and IP data. The MI-OMM fitted the CGM data well and provided precise parameter estimates. GR and R[Formula: see text] model parameters were not significantly different using the MI-OMM and R-OMM (p 0.05) and the correlation between the two S[Formula: see text] was satisfactory ( ρ =0.77). The MI-OMM is usable to estimate GR, R[Formula: see text], and S[Formula: see text] from data collected in real-life conditions with minimally-invasive technologies. Applying MI-OMM to datasets where meal compositions are available will allow modeling the effect of each macronutrient on GR, R[Formula: see text], and S[Formula: see text]. DSS could finally exploit this information to improve diabetes management.

CNN-driven Art Design Decision Support System Based on Big Data

CNN-driven Art Design Decision Support System Based on Big Data

Conceptual design of a nationwide spatial decision support system for forest fire prevention and fighting

Conceptual design of a nationwide spatial decision support system for forest fire prevention and fighting

Initial User-Centred Design of an AI-Based Clinical Decision Support System for Primary Care.

A clinical decision support system based on different methods of artificial intelligence (AI) can support the diagnosis of patients with unclear diseases by providing tentative diagnoses as well as proposals for further steps. In a user-centred-design process, we aim to find out how general practitioners envision the user interface of an AI-based clinical decision support system for primary care. A first user-interface prototype was developed using the task model based on user requirements from preliminary work. Five general practitioners evaluated the prototype in two workshops. The discussion of the prototype resulted in categorized suggestions with key messages for further development of the AI-based clinical decision support system, such as the integration of intelligent parameter requests. The early inclusion of different user feedback facilitated the implementation of a user interface for a user-friendly decision support system.