An assessment of visualization tools for patient monitoring and medical decision making

Craft, Max; Dobrenz, Bev; Dornbush, Erik; Hunter, Molly E.; Morris, Jordan; Stone, Michelle T.; Barnes, Laura E.

doi:10.1109/sieds.2015.7116976

Cited by 10 publications

(4 citation statements)

References 26 publications

(30 reference statements)

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“…With the fuzzy AHP-TOPSIS method and a hybrid approach, the article will also give a scientific perspective on these roles and responsibilities [22][23][24][25][26][27]. The fuzzy AHP-TOPSIS method is a scientific MCDM method that has been used for a long time and has been proven to be accurate and useful [28][29][30][31][32][33][34][35].…”

Section: Contribution and Scopementioning

confidence: 99%

Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System

Albarrak

2023

Diagnostics

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Medical data, such as electronic health records, are a repository for a patient’s medical records for use in the diagnosis of different diseases. Using medical data for individual patient care raises a number of concerns, including trustworthiness in data management, privacy, and patient data security. The introduction of visual analytics, a computing system that integrates analytics approaches with interactive visualizations, can potentially deal with information overload concerns in medical data. The practice of assessing the trustworthiness of visual analytics tools or applications using factors that affect medical data analysis is known as trustworthiness evaluation for medical data. It has a variety of major issues, such as a lack of important evaluation of medical data, the need to process much of medical data for diagnosis, the need to make trustworthy relationships clear, and the expectation that it will be automated. Decision-making strategies have been utilized in this evaluation process to avoid these concerns and intelligently and automatically analyze the trustworthiness of the visual analytics tool. The literature study found no hybrid decision support system for visual analytics tool trustworthiness in medical data diagnosis. Thus, this research develops a hybrid decision support system to assess and improve the trustworthiness of medical data for visual analytics tools using fuzzy decision systems. This study examined the trustworthiness of decision systems using visual analytics tools for medical data for the diagnosis of diseases. The hybrid multi-criteria decision-making-based decision support model, based on the analytic hierarchy process and sorting preferences by similarity to ideal solutions in a fuzzy environment, was employed in this study. The results were compared to highly correlated accuracy tests. In conclusion, we highlight the benefits of our proposed study, which includes performing a comparison analysis on the recommended models and some existing models in order to demonstrate the applicability of an optimal decision in real-world environments. In addition, we present a graphical interpretation of the proposed endeavor in order to demonstrate the coherence and effectiveness of our methodology. This research will also help medical experts select, evaluate, and rank the best visual analytics tools for medical data.

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Section: Contribution and Scopementioning

confidence: 99%

Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System

Albarrak

2023

Diagnostics

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“…It typically is emphasized, or assumed prima facie, that having "some" form of data visualization is superior to the absence of such rendering when making complex decisions. In the biomedical field, for example, O' Donoghue et al (2018) provides commentary on the paucity of visual tools used by clinicians, drawing a connection to physicians' cognitive processing errors and misdiagnoses (Hegarty 2011, Craft et al 2015. Indeed, the view that data visualization is a means to augment cognitive processing that is not easily replicated by text and numbers alone is shared broadly by cognitive psychology researchers (cf.…”

Section: Perspectives On Visualizationmentioning

confidence: 99%

Visualization in Operations Management Research

Basole

Bendoly

Chandrasekaran

et al. 2022

INFORMS Journal on Data Science

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The unprecedented availability of data, along with the growing variety of software packages to visualize it, presents both opportunities and challenges for operations management (OM) research. OM researchers typically use data to describe conditions, predict phenomena, or make prescriptions depending on whether they are building, testing, or translating theories to practice. Visualization, when used appropriately, can complement, aid, and augment the researcher’s understanding in the different stages of research (theory building, testing, or translating and conveying results). On the other hand, if used incorrectly or without sufficient consideration, visualization can yield misleading and erroneous claims. This article formally examines the benefits of visualization as a complementary method enhancing each stage of a broader OM research strategy by examining frameworks and cases from extant research in different OM contexts. Our discussion offers guidance with regard to researchers’ use of visual data renderings, particularly toward avoiding misrepresentation, which can arise with the incorrect use of visualization. We close with a consideration of emerging trends and their implications for researchers and practitioners as well as recommendations for both authors and reviewers, regardless of domain, in evaluating the effectiveness of visuals at each stage of research.

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“…Unplanned ICU admission can be effectively averted by using a system that has a good predictive model and stimulates situational awareness of the relevant clinicians by visualisation. Visualisation can facilitate good clinical decision-making processes [3].…”

Section: Introductionmentioning

confidence: 99%

Implementing a system for the real-time risk assessment of patients considered for intensive care

Dahella

Briggs

Coombes

et al. 2020

BMC Med Inform Decis Mak

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Background: Delay in identifying deterioration in hospitalised patients is associated with delayed admission to an intensive care unit (ICU) and poor outcomes. For the HAVEN project (HICF ref.: HICF-R9-524), we have developed a mathematical model that identifies deterioration in hospitalised patients in real time and facilitates the intervention of an ICU outreach team. This paper describes the system that has been designed to implement the model. We have used innovative technologies such as Portable Format for Analytics (PFA) and Open Services Gateway initiative (OSGi) to define the predictive statistical model and implement the system respectively for greater configurability, reliability, and availability. Results: The HAVEN system has been deployed as part of a research project in the Oxford University Hospitals NHS Foundation Trust. The system has so far processed > 164,000 vital signs observations and > 68,000 laboratory results for > 12,500 patients and the algorithm generated score is being evaluated to review patients who are under consideration for transfer to ICU. No clinical decisions are being made based on output from the system. The HAVEN score has been computed using a PFA model for all these patients. The intent is that this score will be displayed on a graphical user interface for clinician review and response. Conclusions: The system uses a configurable PFA model to compute the HAVEN score which makes the system easily upgradable in terms of enhancing systems' predictive capability. Further system enhancements are planned to handle new data sources and additional management screens.

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An assessment of visualization tools for patient monitoring and medical decision making

Cited by 10 publications

References 26 publications

Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System

Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System

Visualization in Operations Management Research

Implementing a system for the real-time risk assessment of patients considered for intensive care

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