Integration of Equity, Diversity, Inclusivity, and Indigeneity Principles into Usability Heuristic Evaluations.

Cognitive walkthrough is a form of usability testing that considers the perspective of the end users to identify issues related to user experience and web design. This project aims to enhance traditional heuristic evaluation methods with consideration of equity, diversity, inclusivity, and indigeneity (EDI-I) principles. The authors provide suggestions that align with modern informatics advancements, aiming for inclusive design systems and the elimination of systemic barriers.

GWO+RuleFit: rule-based explainable machine-learning combined with heuristics to predict mid-treatment FDG PET response to chemoradiation for locally advanced non-small cell lung cancer.

Objective.Vital rules learned from fluorodeoxyglucose positron emission tomography (FDG-PET) radiomics of tumor subregional response can provide clinical decision support for precise treatment adaptation. We combined a rule-based machine learning (ML) model (RuleFit) with a heuristic algorithm (gray wolf optimizer, GWO) for mid-chemoradiation FDG-PET response prediction in patients with locally advanced non-small cell lung cancer.Approach.Tumors subregions were identified using K-means clustering. GWO+RuleFit consists of three main parts: (i) a random forest is constructed based on conventional features or radiomic features extracted from tumor regions or subregions in FDG-PET images, from which the initial rules are generated; (ii) GWO is used for iterative rule selection; (iii) the selected rules are fit to a linear model to make predictions about the target variable. Two target variables were considered: a binary response measure (ΔSUVmean ⩾ 20% decline) for classification and a continuous response measure (ΔSUVmean) for regression. GWO+RuleFit was benchmarked against common ML algorithms and RuleFit, with leave-one-out cross-validated performance evaluated by the area under the receiver operating characteristic curve (AUC) in classification and root-mean-square error (RMSE) in regression.Main results.GWO+RuleFit selected 15 rules from the radiomic feature dataset of 23 patients. For treatment response classification, GWO+RuleFit attained numerically better cross-validated performance than RuleFit across tumor regions and sets of features (AUC: 0.58-0.86 vs. 0.52-0.78,p= 0.170-0.925). GWO+Rulefit also had the best or second-best performance numerically compared to all other algorithms for all conditions. For treatment response regression prediction, GWO+RuleFit (RMSE: 0.162-0.192) performed better numerically for low-dimensional models (p= 0.097-0.614) and significantly better for high-dimensional models across all tumor regions except one (RMSE: 0.189-0.219,p< 0.004).Significance. The GWO+RuleFit selected rules were interpretable, highlighting distinct radiomic phenotypes that modulated treatment response. GWO+Rulefit achieved parsimonious models while maintaining utility for treatment response prediction, which can aid clinical decisions for patient risk stratification, treatment selection, and biologically driven adaptation. Clinical trial: NCT02773238.

Capturing Usability Problems for People Living With Dementia by Applying the DEMIGNED Principles in Usability Evaluation Methods: Mixed Methods Study.

BACKGROUND: Dementia-related impairments can cause complex barriers to access, use, and adopt digital health technologies (DHTs). These barriers can contribute to digital health inequities. Therefore, literature-based design principles called DEMIGNED have been developed to support the design and evaluation of DHTs for this rapidly increasing population. OBJECTIVE: This study aims to apply the DEMIGNED principles in usability evaluation methods to (1) capture usability problems on a mobile website providing information resources for people visiting a memory clinic, including those living with subjective cognitive decline (SCD), mild cognitive impairment (MCI), or dementia, and (2) investigate the realness of usability problems captured by the DEMIGNED principles in expert testing, specifically for mobile websites that act as a means of providing DHTs. METHODS: First, a heuristic evaluation was conducted, with the DEMIGNED principles serving as domain-specific guidelines, with 3 double experts (experienced in both usability and dementia) and 2 usability engineering experts. Second, think-aloud sessions were conducted with patients visiting a memory clinic who were living with SCD, MCI, or dementia. RESULTS: The heuristic evaluation resulted in 36 unique usability problems. A representative sample of 7 people visiting a memory clinic participated in a think-aloud session, including 4 (57%) with SCD, 1 (14%) with MCI, and 2 (29%) with dementia. The analysis of the think-aloud sessions revealed 181 encounters with usability problems. Of these encounters, 144 (79.6%) could be mapped to 18 usability problems identified in the heuristic evaluation. The remaining 37 (20.4%) encounters from the user testing revealed another 10 unique usability problems. Usability problems frequently described in the think-aloud sessions encompassed difficulties with using the search function, discrepancies between the user's expectations and the content organization, the need for scrolling, information overload, and unclear system feedback. CONCLUSIONS: By applying the DEMIGNED principles in expert testing, evaluators were able to capture 79.6% (144/181) of all usability problem encounters in the user testing of a mobile website for people visiting a memory clinic, including people living with dementia. Regarding unique usability problems, 50% (18/36) of the unique usability problems identified during the heuristic evaluation were captured by the user-testing sessions. Future research should look into the applicability of the DEMIGNED principles to other digital health functionalities to increase the accessibility of digital health and decrease digital health inequity for this complex and rapidly increasing population.

A heuristic method for solving the Steiner tree problem in graphs using network centralities.

We propose a heuristic method of using network centralities for constructing small-weight Steiner trees in this paper. The Steiner tree problem in graphs is one of the practical NP-hard combinatorial optimization problems. Given a graph and a set of vertices called terminals in the graph, the objective of the Steiner tree problem in graphs is to find a minimum weight Steiner tree that is a tree containing all the terminals. Conventional construction methods make a Steiner tree based on the shortest paths between terminals. If these shortest paths are overlapped as much as possible, we can obtain a small-weight Steiner tree. Therefore, we proposed to use network centralities to distinguish which edges should be included to make a small-weight Steiner tree. Experimental results revealed that using the vertex or the edge betweenness centralities contributes to making small-weight Steiner trees.

Heuristic Research in Nursing Science.

The purpose of this article is to reintroduce and describe the processes and phases of heuristic inquiry and to illustrate how the method can advance nursing science. Heuristic inquiry is a rigorous, systematic, phenomenologically orientated research method developed by Clark Moustakas for investigating, discovering, and understanding the nature and meaning of living experiences. Heuristic inquiry invites the inclusion of the researcher's autobiographical living of experience being investigated honoring the personal experiences of the phenomenon from self and each participant in the research study. The author proposes that heuristic inquiry be used in nursing science by including a theoretical interpretive process connecting the thematic essences of the nursing conceptual framework guiding the study. Nursing theory-guided heuristic research advances the study of caring for persons experiencing human-environmental-health transitions to enhance human betterment and wellbecoming.

Heuristics: The Teaching-Learning Journey.

In this column, the author describes a heuristic framework for teaching-learning nursing made of the humanbecoming paradigm, living the art of humanbecoming, and the humanbecoming teaching-learning model. A story helps to clarify the heuristic framework.

Heuristics, Community Change Concepts, and Health Policy: Coming to Know.

The purpose of this paper is to discuss heuristics, guided by Parse's (2021a) community model, to understand how health policies emerge from the unique values and beliefs of community constituents. Within this paper, there is a discussion about heuristics, health policy, Parse's humanbecoming paradigm, and policy implications reflected upon with the change concepts of the humanbecoming community model.

Moral Distress as Moral Heuristic Missing the Mark.

I propose that moral distress may function as a moral heuristic, and one that misses its mark in signifying a fundamental source for nurses' moral suffering. Epistemic injustice is an insidious workplace wrongdoing that is glossed over or avoided in explicit explanations for nurse moral suffering and is substituted by an emphasis on the nurse's own wrongdoing. I discuss reasons and evidence for considering moral distress as a moral heuristic that obfuscates the role of epistemic injustice as a fundamental constraint on nurses' moral reasoning underlying moral suffering.

Knowledge Graph Convolutional Network with Heuristic Search for Drug Repositioning.

Drug repositioning is a strategy of repurposing approved drugs for treating new indications, which can accelerate the drug discovery process, reduce development costs, and lower the safety risk. The advancement of biotechnology has significantly accelerated the speed and scale of biological data generation, offering significant potential for drug repositioning through biomedical knowledge graphs that integrate diverse entities and relations from various biomedical sources. To fully learn the semantic information and topological structure information from the biological knowledge graph, we propose a knowledge graph convolutional network with a heuristic search, named KGCNH, which can effectively utilize the diversity of entities and relationships in biological knowledge graphs, as well as topological structure information, to predict the associations between drugs and diseases. Specifically, we design a relation-aware attention mechanism to compute the attention scores for each neighboring entity of a given entity under different relations. To address the challenge of randomness of the initial attention scores potentially impacting model performance and to expand the search scope of the model, we designed a heuristic search module based on Gumbel-Softmax, which uses attention scores as heuristic information and introduces randomness to assist the model in exploring more optimal embeddings of drugs and diseases. Following this module, we derive the relation weights, obtain the embeddings of drugs and diseases through neighborhood aggregation, and then predict drug-disease associations. Additionally, we employ feature-based augmented views to enhance model robustness and mitigate overfitting issues. We have implemented our method and conducted experiments on two data sets. The results demonstrate that KGCNH outperforms competing methods. In particular, case studies on lithium and quetiapine confirm that KGCNH can retrieve more actual drug-disease associations in the top prediction results.

Narratives in exposomics: A reversed heuristic determinism?

Since the completion of the Human Genome Project (HGP), biomedical sciences have moved away from a gene-centred view and towards a multi-factorial one in which environment, broadly speaking, plays a central role in the determination of human health and disease. Environmental exposures have been shown to be highly prevalent in disease causation. They are considered as complementary to genetic factors in the etiology of diseases, hence the introduction of the concept of the "exposome" as encompassing the totality of human environmental exposures, from conception onwards (Wild in Cancer Epidemiol Biomark Prev 14:1847-1850, 2005), and the launch of the Human Exposome Project (HEP) which aims to complement the HGP. At first sight, and seen as complementary to the genome, the exposome could thus appear as contributing to the rise of novel postgenomic deterministic narratives which place the environment at their core. Is this really the case? If so, what sort of determinism is at work in exposomics research? Is it a case of environmental determinism, and if so, in what sense? Or is it a new sort of deterministic view? In this paper, we first show that causal narratives in exposomics are still very similar to gene-centred deterministic narratives. They correspond to a form of Laplacian determinism and, above all, to what Claude Bernard called the "determinism of a phenomenon". Second, we introduce the notion of "reversed heuristic determinism" to characterize the specific deterministic narratives present in exposomics. Indeed, the accepted sorts of external environmental exposures conceived as being at the origins of diseases are determined, methodologically speaking, by their identifiable internal and biological markers. We conclude by highlighting the most relevant implications of the presence of this heuristic determinism in exposomics research.

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration.

Accurately predicting potential evapotranspiration (PET) is crucial in water resource management, agricultural planning, and climate change studies. This research aims to investigate the performance of two machine learning methods, the adaptive network-based fuzzy inference system (ANFIS) and the deep belief network (DBN), in forecasting PET, as well as to explore the potential of hybridizing the ANFIS approach with the Snake Optimizer (ANFIS-SO) algorithm. The study utilized a comprehensive dataset spanning the period from 1983 to 2020. The ANFIS, ANFIS-SO, and DBN models were developed, and their performances were evaluated using statistical metrics, including R2, R adj 2 , NSE, WI, STD, and RMSE. The results showcase the exceptional performance of the DBN model, which achieved R2 and R adj 2 values of 0.99 and NSE and WI scores of 0.99 across the nine stations analyzed. In contrast, the standard ANFIS method exhibited relatively weaker performance, with R2 and R adj 2 values ranging from 0.52 to 0.88. However, the ANFIS-SO approach demonstrated a substantial improvement, with R2 and R adj 2 values ranging from 0.94 to 0.99, suggesting the value of optimization techniques in enhancing the model's capabilities. The Taylor diagram and violin plots with box plots further corroborated the comparative analysis, highlighting the DBN model's superior ability to closely match the observed standard deviation and correlation and its consistent and low-variance predictions. The ANFIS-SO method also exhibited enhanced performance in these visual representations, with an RMSE of 0.86, compared to 0.95 for the standard ANFIS. The insights gained from this study can inform the selection of the most appropriate modeling technique, whether it be the high-precision DBN, the flexible ANFIS, or the optimized ANFIS-SO approach, based on the specific requirements and constraints of the application.

A heuristic-based approach for assessing usability in electric powered wheelchairs: A preliminary investigation

Objective: An important issue related to electric powered wheelchair (EPW) is usability. Recent studies did not use heuristic evaluation and did not consider users’ and developers’ participation in the usability evaluation process of the EPW, especially when it has to be driven using alternative commands. Thus, this study investigates the use of heuristics to evaluate the usability of EPW driven by alternative commands, considering the opinion of users and assistive technology (AT) development professionals.MethodsThe study was carried out with 54 participants: 28 EPW users (Group I) and 26 AT developers (Group II). We built a set of 25 heuristics that affects EPW usability. Participants rated each of the 25 heuristics according to their importance for the usability of EPW using the five-point Likert scale. We used the R Software to perform the Wilcoxon Mann–Whitney test as a statistical comparisons test between Group I and II.ResultsThe results showed a statistically significant difference (p<0.05) between Group I and II in the evaluation of 16 heuristics. We identified an important set of heuristics that could help evaluate and improve the usability of EPW.ConclusionThe findings highlighted the importance of considering users’ and developers’ points of view in developing an EPW and its evaluation criteria. It could help the design of the device match the user's needs and expectations. The set of heuristics in this study can be adapted to evaluate other devices’ usability using the heuristic evaluation approach. (AU)

Objetivo: La usabilidad es una cuestión importante relacionada con las sillas de ruedas eléctricas (SRE). Los estudios recientes no han utilizado evaluaciones heurísticas ni han contemplado la participación de los usuarios y desarrolladores en el proceso de evaluación de dichas sillas, especialmente cuando deben conducirse utilizando comandos alternativos. Por ello, este estudio investiga el uso de la heurística para evaluar la usabilidad de las SRE impulsadas por comandos alternativos, considerando la opinión de los usuarios y los profesionales del desarrollo de la tecnología asistencial (TA).MétodosEl estudio se realizó con 54 participantes: 28 usuarios de SRE (Grupo I) y 26 desarrolladores de TA (Grupo II). Construimos un conjunto de 25 heurísticas que afectan a la usabilidad de las SRE. Los sujetos calificaron cada una de las 25 heurísticas de acuerdo con su importancia para la usabilidad de las SRE utilizando la escala de Likert de cinco puntos. Utilizamos el software R para realizar la prueba de Wilcoxon-Mann-Whitney como prueba de comparación estadística entre los Grupos I y II.ResultadosLos resultados reflejaron una diferencia estadística significativa (p < 0,05) entre los Grupos I y II en la evaluación de las 16 heurísticas. Identificamos un conjunto importante de heurísticas que podrían ayudar a evaluar y mejorar la usabilidad de las SRE.ConclusiónLos hallazgos subrayaron la importancia de estimar los puntos de vista de los usuarios y desarrolladores a la hora de desarrollar una SRE, así como sus criterios de evaluación. Estos podrían contribuir a que el diseño del dispositivo coincidiera con las necesidades y expectativas de los usuarios. El conjunto de heurísticas de este estudio puede adaptarse, para evaluar la usabilidad de otros dispositivos, utilizando el enfoque de evaluación heurística. (AU)

The role of information systems in emergency department decision-making-a literature review.

OBJECTIVES: Healthcare providers employ heuristic and analytical decision-making to navigate the high-stakes environment of the emergency department (ED). Despite the increasing integration of information systems (ISs), research on their efficacy is conflicting. Drawing on related fields, we investigate how timing and mode of delivery influence IS effectiveness. Our objective is to reconcile previous contradictory findings, shedding light on optimal IS design in the ED. MATERIALS AND METHODS: We conducted a systematic review following PRISMA across PubMed, Scopus, and Web of Science. We coded the ISs' timing as heuristic or analytical, their mode of delivery as active for automatic alerts and passive when requiring user-initiated information retrieval, and their effect on process, economic, and clinical outcomes. RESULTS: Our analysis included 83 studies. During early heuristic decision-making, most active interventions were ineffective, while passive interventions generally improved outcomes. In the analytical phase, the effects were reversed. Passive interventions that facilitate information extraction consistently improved outcomes. DISCUSSION: Our findings suggest that the effectiveness of active interventions negatively correlates with the amount of information received during delivery. During early heuristic decision-making, when information overload is high, physicians are unresponsive to alerts and proactively consult passive resources. In the later analytical phases, physicians show increased receptivity to alerts due to decreased diagnostic uncertainty and information quantity. Interventions that limit information lead to positive outcomes, supporting our interpretation. CONCLUSION: We synthesize our findings into an integrated model that reveals the underlying reasons for conflicting findings from previous reviews and can guide practitioners in designing ISs in the ED.

Empathetic and Emotive Design: A Scoping Review.

With the advent of the digital health era, there has emerged a new emphasis on collecting health information from patients and their families using technology platforms that are both empathetic and emotive in their design to meet the needs and situations of individuals, who are experiencing a health event or crisis. Digital empathy has emerged as an aspect of interactions between individuals and healthcare organizations especially in times of crises as more empathetic and emotive digital health platforms hold greater capacity to engage the user while collecting valuable health information that could be used to respond to the individuals' needs. In this paper we report on the results of a scoping review used to derive an initial set of evidence-based empathetic or emotive design heuristics.

When concern is not enough: Overcoming the climate awareness-action gap.

Climate concern is on the rise in many countries and recent research finds that lifestyle- and behaviour-change could advance climate action; yet, individuals struggle to move their climate concern into action. This is known as the 'awareness-action inconsistency,' 'psychological climate paradox,' or 'values-action gap.' While this gap has been extensively studied, climate action implementation and policy-design seldom sufficiently apply that body of knowledge in practice. This Perspective presents a comprehensive heuristic to account for how individuals bring climate change into their awareness (climate action-logics), how they keep climate change out of their awareness (climate shadow), how social narratives contribute to shaping choices (climate discourses), and how systems and structures influence and constrain agency (climate-action systems). The heuristic is illustrated with an example of 15-Minute Cities in Canada. Understanding the multifaceted dilemma that weighs on people's sense-making and behaviours may help policy-makers and practitioners to ameliorate the climate awareness-action gap.

Proxies, heuristics, and goal alignment.

Decision-making heuristics rely on proxies so the elements of heuristics appear to map well to the elements of proxies identified by John et al. However, unlike proxy failure, heuristics do not fail because of feedback. This may be because for successful heuristics the goals of regulators and agents are aligned, but this is not the case for proxy failure.

Heuristics in risky decision-making relate to preferential representation of information.

When making choices, individuals differ from one another, as well as from normativity, in how they weigh different types of information. One explanation for this relates to idiosyncratic preferences in what information individuals represent when evaluating choice options. Here, we test this explanation with a simple risky-decision making task, combined with magnetoencephalography (MEG). We examine the relationship between individual differences in behavioral markers of information weighting and neural representation of stimuli pertinent to incorporating that information. We find that the extent to which individuals (N = 19) behaviorally weight probability versus reward information is related to how preferentially they neurally represent stimuli most informative for making probability and reward comparisons. These results are further validated in an additional behavioral experiment (N = 88) that measures stimulus representation as the latency of perceptual detection following priming. Overall, the results suggest that differences in the information individuals consider during choice relate to their risk-taking tendencies.

Heuristic test reveals little effect of learning and maturation on early prey capture experiences in a web-building spider.

Behaviors can vary throughout an animal's life and this variation can often be explained by changes associated with learning and/or maturing. Currently, there is little consensus regarding how these processes interact to affect behaviors. Here we proposed a heuristic approach to disentangle the effects of learning and maturation on behavior and applied it to the predatory behaviors of Physocyclus globosus spiderlings. We varied the degree of prey difficulty and familiarity spiderlings received along the first instar and across the molt to the second instar and quantified the time spiderlings spent wrapping prey, as a proxy for prey capture efficiency. We found no overall evidence for learning or maturation. Changes in efficiency were mainly due to the switch from difficult to easy prey, or vice versa. However, there was one treatment where spiderlings improved in efficiency before and after the molt, without a switch in prey type. This provides some indication that difficult prey may offer more opportunity for learning or maturation to impact behavior. Although we found little effect of learning or maturation on prey capture efficiency, we suggest that our heuristic approach is effective and could be useful in investigating these processes in other behaviors and other animals.

Empathetic and Emotive Design Heuristics: Preliminary Results of Their Application to Evaluating Survey User Interfaces.

Empathetic and emotive design is becoming increasingly important in the digital age. In this research we describe the results of a combined cognitive walkthrough and heuristic evaluation using newly developed, empirically derived empathy or emotive design heuristics. We applied the heuristics to the evaluation of four commonly used survey platforms. Our preliminary findings revealed that the heuristics performed effectively in scoring survey platforms on their level of empathy. Survey platforms that are highly empathetic were scored highest.