Estimating the risk and benefit of radiation therapy in (y)pN1 stage breast cancer patients: A Bayesian network model incorporating expert knowledge (KROG 22-13)

BACKGROUND

We aimed to evaluate the risk and benefit of (y)pN1 breast cancer patients in a Bayesian network model.

METHOD

We developed a Bayesian network (BN) model comprising three parts: pretreatment, intervention, and risk/benefit. The pretreatment part consisted of clinical information from a tertiary medical center. The intervention part regarded the field of radiotherapy. The risk/benefit component encompasses radiotherapy (RT)-related side effects and effectiveness, including factors such as recurrence, cardiac toxicity, lymphedema, and radiation pneumonitis. These factors were evaluated in terms of disability weights and probabilities from a nationwide expert survey. The overall disease burden (ODB) was calculated as the sum of the probability multiplied by the disability weight. A higher value of ODB indicates a greater disease burden for the patient.

RESULTS

Among the 58 participants, a BN model utilizing discretization and clustering techniques revealed five distinct clusters. Overall, factors associated with breast reconstruction and RT exhibited high discrepancies (24-34 %), while RT-related side effects demonstrated low discrepancies (3-11 %) among the experts. When incorporating recurrence and RT-related side effects, the mean ODB of (y)pN1 patients was 0.258 (range, 0.244-0.337), with a higher tendency observed in triple-negative breast cancer (TNBC) or mastectomy cases. The ODB for TNBC patients undergoing mastectomy without postmastectomy radiotherapy was 0.327, whereas for non-TNBC patients undergoing breast conserving surgery with RT, the disease burden was 0.251. There was an increasing trend in ODB as the field of RT increased.

CONCLUSION

We developed a Bayesian network model based on an expert survey, which helps to understand treatment patterns and enables precise estimations of RT-related risk and benefit in (y)pN1 patients.

Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning

Background

With the development of traditional Chinese medicine (TCM) syndrome knowledge accumulation and artificial intelligence (AI), this study proposes a holistic TCM syndrome differentiation model for the classification prediction of multiple TCM syndromes based on deep learning and accelerates the construction of modern foundational TCM equipment.

Methods

We searched publicly available TCM guidelines and textbooks for expert knowledge and validated these sources using ten-fold cross-validation. Based on the BERT and CNN models, with the classification constraints from TCM holistic syndrome differentiation, the TCM-BERT-CNN model was constructed, which completes the end-to-end TCM holistic syndrome text classification task through symptom input and syndrome output. We assessed the performance of the model using precision, recall, and F1 scores as evaluation metrics.

Results

The TCM-BERT-CNN model had a higher precision (0.926), recall (0.9238), and F1 score (0.9247) than the BERT, TextCNN, LSTM RNN, and LSTM ATTENTION models and achieved superior results in model performance and predictive classification of most TCM syndromes. Symptom feature visualization demonstrated that the TCM-BERT-CNN model can effectively identify the correlation and characteristics of symptoms in different syndromes with a strong correlation, which conforms to the diagnostic characteristics of TCM syndromes.

Conclusions

The TCM-BERT-CNN model proposed in this study is in accordance with the TCM diagnostic characteristics of holistic syndrome differentiation and can effectively complete diagnostic prediction tasks for various TCM syndromes. The results of this study provide new insights into the development of deep learning models for holistic syndrome differentiation in TCM.

A homologous and heterogeneous multi-view inter-patient adaptive network for arrhythmia detection

BACKGROUND AND OBJECTIVE

Electrocardiogram (ECG) is a widely used diagnostic tool for arrhythmia assessment in clinical practice. However, current arrhythmia detection algorithms rely heavily on signal-based data, while cardiologists often use image-based data. This discrepancy, combined with individual differences in physiological signals, poses challenges for accurate arrhythmia detection. To address these challenges and improve arrhythmia detection performance, we propose a homologous and heterogeneous multi-view inter-patient adaptive network.

METHODS

We designed a multi-view representation learning module to capture dynamic and morphological characteristics from ECG signals and electrocardiographic images. Expert knowledge was also elicited to gain internally-invariant characteristics of each category. Finally, we designed a new loss function that aligns the embedding of the source and target domains in the feature space to minimize the negative effects of individual differences.

RESULTS

Experiments on the MIT-BIH arrhythmia database demonstrate that our proposed method outperforms state-of-the-art baselines in terms of accuracy, positive predictive value, sensitivity and F1-score. These results indicate the effectiveness of our method in accurately detecting arrhythmias.

CONCLUSIONS

Our homologous and heterogeneous multi-view inter-patient adaptive network successfully addresses the challenges of utilizing both ECG signal and electrocardiographic image data for arrhythmia detection and overcoming individual differences in physiological signals. Our proposed method has the potential to improve early diagnosis and treatment outcomes of arrhythmias in clinical practice.

Ranking ecosystem services delivered by trees in urban and rural areas

Policies and strategies for tree management and protection on a national, regional, and local level have not sufficiently considered differences between rural and urban areas. We used expert knowledge to compare rural and urban areas in a case study evaluating the relative importance of ecosystem services (ES) in policy development. The Analytic Hierarchy Process (AHP) and focus group discussions were used to rank 17 ES, representing four classes of services: provisioning, regulating, habitat, and cultural. The results indicated that effective protection strategies, beyond simply increasing general tree cover, should consider specific benefits trees provide to local communities. We discuss the role of objective prioritization of ES delivered by trees in urban and rural areas and their consequences for decision-making processes.

Expert or experiential knowledge? How knowledge informs situated action in childcare practices

The study examines how alternative health information affects the professional authority of doctors. Drawing on in-depth interviews with mothers in Hong Kong and focusing on child-rearing practices, we find that mothers glean expert knowledge from doctors and experiential knowledge from online resources, social networks, and direct observations. Despite the prevalence of information online and traditional Chinese remedies, mothers do not use experiential knowledge to challenge doctors. Instead, they self-interpret medical advice and set self-determined courses of action based on their own practical situations. Generally, they dichotomize child-rearing and caring issues into medical versus non-medical domains to which they apply expert and experiential knowledge, respectively. How a condition is categorized depends on whether their individualized experiential knowledge is adequate to allow them to manage the health of their child. This study concludes that mothers with alternative health information still respect professional authorities in clinical interactions, which accords with previous sociological studies, but mothers often consider expert knowledge overly generic, so they take initiative to translate generic health-related knowledge into individualized knowledge for their child and determine their own course of action. Our theoretical contribution is to bring situational concerns into the debate of professional authority by revealing how the accumulation of experiential knowledge informs situated action.

Practitioners' best practices to Adopt, Use or Abandon Model-based Testing with Graphical models for Software-intensive Systems

Model-based testing (MBT) has been extensively researched for software-intensive systems but, despite the academic interest, adoption of the technique in industry has been sparse. This phenomenon has been observed by our industrial partners for MBT with graphical models. They perceive one cause to be a lack of evidence-based MBT guidelines that, in addition to technical guidelines, also take non-technical aspects into account. This hypothesis is supported by a lack of such guidelines in the literature. Objective: The objective of this study is to elicit, and synthesize, MBT experts' best practices for MBT with graphical models. The results aim to give guidance to practitioners and aspire to give researchers new insights to inspire future research. Method: An interview survey is conducted using deep, semi-structured, interviews with an international sample of 17 MBT experts, in different roles, from software industry. Interview results are synthesised through semantic equivalence analysis and verified by MBT experts from industrial practice. Results: 13 synthesised conclusions are drawn from which 23 best-practice guidelines are derived for the adoption, use and abandonment of the technique. In addition, observations and expert insights are discussed that help explain the lack of wide-spread adoption of MBT with graphical models in industrial practice. Conclusions: Several technical aspects of MBT are covered by the results as well as conclusions that cover process- and organizational factors. These factors relate to the mindset, knowledge, organization, mandate and resources that enable the technique to be used effectively within an organization. The guidelines presented in this work complement existing knowledge and, as a primary objective, provide guidance for industrial practitioners to better succeed with MBT with graphical models.

BargCrEx: A System for Bargaining Based Aggregation of Crowd and Expert Opinions in Crowdsourcing

Crowdsourcing and crowd voting systems are being increasingly used in societal, industry, and academic problems (labeling, recommendations, social choice, etc.) due to their possibility to exploit "wisdom of crowd" and obtain good quality solutions, and/or voter satisfaction, with high cost-efficiency. However, the decisions based on crowd vote aggregation do not guarantee high-quality results due to crowd voter data quality. Additionally, such decisions often do not satisfy the majority of voters due to data heterogeneity (multimodal or uniform vote distributions) and/or outliers, which cause traditional aggregation procedures (e.g., central tendency measures) to propose decisions with low voter satisfaction. In this research, we propose a system for the integration of crowd and expert knowledge in a crowdsourcing setting with limited resources. The system addresses the problem of sparse voting data by using machine learning models (matrix factorization and regression) for the estimation of crowd and expert votes/grades. The problem of vote aggregation under multimodal or uniform vote distributions is addressed by the inclusion of expert votes and aggregation of crowd and expert votes based on optimization and bargaining models (Kalai-Smorodinsky and Nash) usually used in game theory. Experimental evaluation on real world and artificial problems showed that the bargaining-based aggregation outperforms the traditional methods in terms of cumulative satisfaction of experts and crowd. Additionally, the machine learning models showed satisfactory predictive performance and enabled cost reduction in the process of vote collection.

A Bayesian Modelling Framework for Integration of Ecosystem Services into Freshwater Resources Management

Models of ecological response to multiple stressors and of the consequences for ecosystem services (ES) delivery are scarce. This paper describes a methodology for constructing a BBN combining catchment and water quality model output, data, and expert knowledge that can support the integration of ES into water resources management. It proposes "small group" workshop methods for elucidating expert knowledge and analyses the areas of agreement and disagreement between experts. The model was developed for four selected ES and for assessing the consequences of management options relating to no-change, riparian management, and decreasing or increasing livestock numbers. Compared with no-change, riparian management and a decrease in livestock numbers improved the ES investigated to varying degrees. Sensitivity analysis of the expert information in the BBN showed the greatest disagreements between experts were mainly for low probability situations and thus had little impact on the results. Conversely, in our applications, the best agreement between experts tended to occur for the higher probability, more likely, situations. This has implications for the practical use of this type of model to support catchment management decisions. The complexity of the relationship between management measures, the water quality and ecological responses and resulting changes in ES must not be a barrier to making decisions in the present time. The interactions of multiple stressors further complicate the situation. However, management decisions typically relate to the overall character of solutions and not their detailed design, which can follow once the nature of the solution has been chosen, for example livestock management or riparian measures or both.

Reconciling humans and birds when designing ecological corridors and parks within urban landscapes

Considering the necessity of interdisciplinary approaches for planning and managing the expansion of urban landscapes worldwide, this study aimed to (1) assess landscape permeability for birds and people inhabiting a Neotropical city and (2) propose priority streets and areas for the implementation of a green infrastructure project that could benefit both. To reach these goals, we generated resistance surfaces using expert knowledge to simulate multiple least-cost corridors (MLCC) between parks and green spaces within an urban landscape for people and seven bird species. We compared the solutions using a corridors' spatial agreement analysis, which allow us to identify the overlap between modeled corridors for all organisms or functional groups of interest. We also identified the streets most selected by the simulated MLCC and then identified a green space which is a convergence point of corridors modeled for both people and bird species. Finally, we suggested priority streets for planting trees and proposed interventions to turn the green space into a multifunctional park, conciliating social and ecological perspectives.

Identifying Priorities, Targets, and Actions for the Long-term Social and Ecological Management of Invasive Non-Native Species

Formulating effective management plans for addressing the impacts of invasive non-native species (INNS) requires the definition of clear priorities and tangible targets, and the recognition of the plurality of societal values assigned to these species. These tasks require a multi-disciplinary approach and the involvement of stakeholders. Here, we describe procedures to integrate multiple sources of information to formulate management priorities, targets, and high-level actions for the management of INNS. We follow five good-practice criteria: justified, evidence-informed, actionable, quantifiable, and flexible. We used expert knowledge methods to compile 17 lists of ecological, social, and economic impacts of lodgepole pines (Pinus contorta) and American mink (Neovison vison) in Chile and Argentina, the privet (Ligustrum lucidum) in Argentina, the yellow-jacket wasp (Vespula germanica) in Chile, and grasses (Urochloa brizantha and Urochloa decumbens) in Brazil. INNS plants caused a greater number of impacts than INNS animals, although more socio-economic impacts were listed for INNS animals than for plants. These impacts were ranked according to their magnitude and level of confidence on the information used for the ranking to prioritise impacts and assign them one of four high-level actions-do nothing, monitor, research, and immediate active management. We showed that it is possible to formulate management priorities, targets, and high-level actions for a variety of INNS and with variable levels of available information. This is vital in a world where the problems caused by INNS continue to increase, and there is a parallel growth in the implementation of management plans to deal with them.

A knowledge elicitation study to inform the development of a consequence model for Arctic ship evacuations: Qualitative and quantitative data

Expert knowledge was elicited to develop a life-safety consequence severity model for Arctic ship evacuations (Browne et al., 2021). This paper presents the associated experimental design and data. Through semi-structured interviews, participants identified factors that influence consequence severity. Through a survey, participants evaluated consequence severity of different ship evacuation scenarios. The methodology represents a two-phased mixed methods design. Life-safety consequence severity is measured as the expected number of fatalities resulting from an evacuation. Participants of the study were experts in various fields of the Arctic maritime industry. Sixteen experts participated in the interviews and the survey (sample size: n = 16). Sample size for the interviews was based on thematic data saturation. Predominantly the same group of experts participated in the survey. Interviews were analysed using thematic analysis. Interview data informed the development of evacuation scenarios defined in the survey. The interview guide and survey questions are presented. Data tables present the codes that emerged through thematic analysis, including code reference counts and code intersection counts. Data tables present the raw data of participant responses to the survey. This data can support further investigation of factors that influence consequence severity, definition of a broader range of evacuation scenarios, and establishment of associated consequence severities. This data has value to Arctic maritime policy-makers, researchers, and other stakeholders engaged in maritime operational risk management.

Lessons Learned from a Two-Round Delphi-based Scenario Study

The Delphi technique is a suitable methodology for structuring group communication to answer current and prospective research questions within several rounds. The method is used in many disciplines and characterized by anonymity, iteration, controlled feedback, and statistical “group response” (Rowe & Wright, 2001). This technical paper presents practical details and lessons learned from a two-round Delphi-based scenario study in which projections (Delphi statements, questions or hypotheses) were developed with findings from expert interviews and an expert workshop. This Delphi study provides answers to future-related questions for which other research methods are inappropriate. This is depicted as a five-step process, making it easy to follow and replicable, for example to help first-time Delphi-method researchers. In doing so, the authors aim to provide the community with valuable technical insights and guidance for studies applying the Delphi technique both to prospective questions and in other research settings.•

Conducting a Delphi study can be a slow process with respect to receiving feedback from the panelists. Planning an appropriate period for distributing the questionnaire may produce a higher return rate. A sufficient time buffer should be incorporated into project planning.

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Projections that create dissent among the panelists may provide valuable results.

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Data analytics, software programs and online social networks can support researchers, save time and resources, and provide further insights in the process of conducting a Delphi study.

Monitoring of the invasion of Spartina alterniflora from 1985 to 2015 in Zhejiang Province, China

Background

Spartina alterniflora is an invasive plant on the coast of China that replaces native vegetation and has a serious negative impact on local ecosystems. Monitoring the spatial distribution of S. alterniflora and its changes over time can reveal its expansion mechanism, which is crucial for the management of coastal ecosystems. The purpose of this study was to map the distribution of S. alterniflora in Zhejiang Province from 1985 to 2015 using a time series of Landsat TM/OLI images and analyze the temporal and spatial patterns of expansion of this species.

Results

After analyzing the distribution of coastal vegetation, the vegetation index was calculated based on Landsat images for 4 years (1985, 1995, 2005 and 2015). According to a threshold determined based on expert knowledge, the distribution of S. alterniflora in Zhejiang Province was extracted, and the temporal and spatial changes in the distribution of S. alterniflora were analyzed. The classification accuracy was 90.3%. S. alterniflora has expanded rapidly in recent decades after being introduced into southern Zhejiang. Between 1985 and 2015, S. alterniflora increased its area of distribution by 10,000 hm², and it replaced native vegetation to become the most abundant halophyte in tidal flats. Overall, S. alterniflora expanded from south to north over the decades of the study, and the fastest expansion rate was 463.64 hm²/year, which occurred between 1995 and 2005. S. alterniflora was widely distributed in the tidal flats of bays and estuaries and expanded outward as sediment accumulated.

Conclusions

This study reveals the changes over time in S. alterniflora cover in Zhejiang and can contribute to the control and management of this invasive plant.

Trust me? Consumer trust in expert information on food product labels

Food product labels can provide consumers with rich, specific, expert-certified product information. However, sources of label information differ. How do consumers then evaluate label trustworthiness of expert labels in comparison to other commonly used label types? We present results from a representative online survey (N = 10,000) of consumers in Japan, the USA, Germany, China and Thailand using professionally designed labels for four food types (milk, honey, oil, wine) and five different sources of food information (farmers, government/administration, producer associations, experts, and consumers). We tested label legibility through identification of the label information source and asked respondents to evaluate the trustworthiness of labels using a six-scale instrument ranging from overall label trust to purchase intent. Results show that label legibility varied between countries, with expert labels scoring lowest. Nevertheless, respondents correctly identifying all label information sources chose expert labels as the most or second-most trustworthy across all countries and food types, while consumer labels scored low. Demographic factors exhibited weak influence. Results suggest expert labels might play an important role as trusted sources of information in an increasingly complex global food system. Finally, we consider the implications of the study for a potential institutionalization of expert labels based on the Japanese context.

Lessons learned from rapid environmental risk assessments for prioritization of alien species using expert panels

Limiting the spread and impacts of invasive alien species (IAS) on biodiversity and ecosystems has become a goal of global, regional and national biodiversity policies. Evidence based management of IAS requires support by risk assessments, which are often based on expert judgment. We developed a tool to prioritize potentially new IAS based on their ecological risks, socio-economic impact and feasibility of management using multidisciplinary expert panels. Nine expert panels reviewed scientific studies, grey literature and expert knowledge for 152 species. The quality assessment of available knowledge revealed a lack of peer-reviewed data and high dependency on best professional judgments, especially for impacts on ecosystem services and feasibility of management. Expert consultation is crucial for conducting and validating rapid assessments of alien species. There is still a lack of attention for systematic and methodologically sound assessment of impacts on ecosystem services and weighting negative and positive effects of alien species.

Just a small bunch of flowers: the botanical knowledge of students and the positive effects of courses in plant identification at German universities

BACKGROUND

In the light of the ongoing loss of species the knowledge about and the ability to identify species becomes increasingly important for effective monitoring and conservation measures. Learning about identifying biodiversity is a central task for future biologists and biology teachers and universities play an important role in educating future experts and multipliers. It builds one basis for conservation literacy.

METHODS

We analyzed undergraduate students' prior knowledge on plant species, identification and their knowledge gain from introductory plant identification courses at eight German universities. Using the Visual Classification Method-a combination of a presentation and standardized questionnaires-we evaluated the learning success of more than 500 students regarding (a) 'declarative species knowledge' of plant species names and (b) 'taxonomic concept knowledge', which is seen as knowledge on a higher level of complexity. From comparison of paired pre- and post-tests we calculated the individual knowledge gain. Using Linear Mixed Effects Models (LMMs) we analyzed effects of knowledge levels, learner-specific resources and learning environment on the knowledge gain.

RESULTS

We found that university course instructors have to start teaching at an almost zero level with respect to undergraduates' prior knowledge: on average 2.6 of 32 common plant species were known. Overall, the introductory courses resulted in a significant but weak knowledge gain. We detected a higher knowledge gain in 'taxonomic concept knowledge' than in 'declarative species knowledge'. We showed that the learning success was influenced by learner-specific resources, such as prior knowledge or aspects of motivation towards the subject matter, and by learning environment such as teaching methodology.

DISCUSSION

We discuss didactical demands and aspects of teaching methodologies that could facilitate learning the complex task of plant identification in university courses. Plant identification should be taught and supervised by experienced, highly motivated course instructors with profound expertise and outstanding didactical skills. In order to qualify future generations of biologists, biology teachers, or conservationists universities should aim at and encourage high-quality teaching.

Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation

Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. Reducing model size without significant loss in performance metrics is crucial for time and memory-efficient automated image-based decision-making. Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations. Recently, there has been interest in introducing other forms of expert knowledge into deep learning architecture design. This is the approach considered in the paper where we propose to combine ultrasound video with point-of-gaze tracked for expert sonographers as they scan to train memory-efficient ultrasound image analysis models. Specifically we develop teacher-student knowledge transfer models for the exemplar task of frame classification for the fetal abdomen, head, and femur. The best performing memory-efficient models attain performance within 5% of conventional models that are 1000× larger in size.

Indicators of marine ecosystem integrity for Canada's Pacific: An expert-based hierarchical approach

There is great interest and rapid progress around the world in developing sets of indicators of marine ecosystem integrity for assessment and management. However, the complexity of coastal marine ecosystems can challenge such efforts. To address this challenge, an expert-based, hierarchical, and adaptive approach was developed with the objectives of healthy marine ecosystems and community partnerships in monitoring and management. Small sets of the top-ranked indicators of ecosystem integrity and associated human pressures were derived from expert-rankings of lists of identified candidate indicators of the status of, and pressures on, each of 17 ecosystem features, organized within 8 elements in turn within 3 overlapping aspects of ecosystem health. Over 200 experts played a role in rating the relative value of 1035 candidate indicators. A panel of topic experts was assigned to each of the 17 ecosystem features to apply 21 weighted indicator selection criteria. Selection criteria and candidate indicators were identified through literature reviews, expert panels, and surveys, and they were evaluated in terms of the experts' judgements of importance to the health of Canada's Pacific marine ecosystems. This produced a flexible, robust, and adaptable approach to identifying representative sets of indicators for any scale and for any management unit within Canada's Pacific. At the broadest scale, it produced a top 20 list of ecosystem state and pressure indicators. These top indicators, or other sets selected for smaller regions, can then guide the development of both regional and nested local monitoring programs in a way that maximizes continuity while including locally unique values. This hierarchical expert-based approach was designed to address challenges of complexity and scale and to enable efficient selection of useful and representative sets of indicators of ecosystem integrity while also enabling the participation of broad government and stakeholder communities.

A data-driven and practice-based approach to identify risk factors associated with hospital-acquired falls: Applying manual and semi- and fully-automated methods

BACKGROUND AND PURPOSE

Electronic health record (EHR) data provides opportunities for new approaches to identify risk factors associated with iatrogenic conditions, such as hospital-acquired falls. There is a critical need to validate and translate prediction models that support fall prevention clinical decision-making in hospitals. The purpose of this study was to explore a combined data-driven and practice-based approach to identify risk factors associated with falls.

PROCEDURES

We conducted an observational case-control study of EHR data from January 1, 2013 to October 31, 2013 from 14 medical-surgical units of a tertiary referral teaching hospital. Patients aged 21 or older admitted to medical surgical units were included in the study. Manual and semi- and fully-automated methods were used to identify fall risk factors across four prediction models. Sensitivity, specificity, and the Area under the Receiver Operating Characteristic (AUROC) curve were calculated for all models using 10-fold cross validation.

FINDINGS

We confirmed the significance of a set of valid fall risk factors (i.e., age, gender, fall risk assessment, history of falling, mental status, mobility, and confusion) and identified set of new risk factors (i.e., # of fall risk increasing drugs, hemoglobin level, physical therapy initiation, Charlson Comorbity Index, nurse skill mix, and registered nurse staffing ratio) based on the most precise prediction approach, namely stepwise regression.

CONCLUSIONS

The use of semi- and fully-automated approaches with expert clinical knowledge over expert or data-driven only approaches can significantly improve identifying patient, clinical, and organizational risk factors of iatrogenic conditions, including hospital-acquired falls.

Prediction models for clustered data with informative priors for the random effects: a simulation study

BACKGROUND

Random effects modelling is routinely used in clustered data, but for prediction models, random effects are commonly substituted with the mean zero after model development. In this study, we proposed a novel approach of including prior knowledge through the random effects distribution and investigated to what extent this could improve the predictive performance.

METHODS

Data were simulated on the basis of a random effects logistic regression model. Five prediction models were specified: a frequentist model that set the random effects to zero for all new clusters, a Bayesian model with weakly informative priors for the random effects of new clusters, Bayesian models with expert opinion incorporated into low informative, medium informative and highly informative priors for the random effects. Expert opinion at the cluster level was elicited in the form of a truncated area of the random effects distribution. The predictive performance of the five models was assessed. In addition, impact of suboptimal expert opinion that deviated from the true quantity as well as including expert opinion by means of a categorical variable in the frequentist approach were explored. The five models were further investigated in various sensitivity analyses.

RESULTS

The Bayesian prediction model using weakly informative priors for the random effects showed similar results to the frequentist model. Bayesian prediction models using expert opinion as informative priors showed smaller Brier scores, better overall discrimination and calibration, as well as better within cluster calibration. Results also indicated that incorporation of more precise expert opinion led to better predictions. Predictive performance from the frequentist models with expert opinion incorporated as categorical variable showed similar patterns as the Bayesian models with informative priors. When suboptimal expert opinion was used as prior information, results indicated that prediction still improved in certain settings.

CONCLUSIONS

The prediction models that incorporated cluster level information showed better performance than the models that did not. The Bayesian prediction models we proposed, with cluster specific expert opinion incorporated as priors for the random effects showed better predictive ability in new data, compared to the frequentist method that replaced random effects with zero after model development.

Integrating expert knowledge and ecological niche models to estimate Mexican primates' distribution

Ecological niche modeling is used to estimate species distributions based on occurrence records and environmental variables, but it seldom includes explicit biotic or historical factors that are important in determining the distribution of species. Expert knowledge can provide additional valuable information regarding ecological or historical attributes of species, but the influence of integrating this information in the modeling process has been poorly explored. Here, we integrated expert knowledge in different stages of the niche modeling process to improve the representation of the actual geographic distributions of Mexican primates (Ateles geoffroyi, Alouatta pigra, and A. palliata mexicana). We designed an elicitation process to acquire information from experts and such information was integrated by an iterative process that consisted of reviews of input data by experts, production of ecological niche models (ENMs), and evaluation of model outputs to provide feedback. We built ENMs using the maximum entropy algorithm along with a dataset of occurrence records gathered from a public source and records provided by the experts. Models without expert knowledge were also built for comparison, and both models, with and without expert knowledge, were evaluated using four validation metrics that provide a measure of accuracy for presence-absence predictions (specificity, sensitivity, kappa, true skill statistic). Integrating expert knowledge to build ENMs produced better results for potential distributions than models without expert knowledge, but a much greater improvement in the transition from potential to realized geographic distributions by reducing overprediction, resulting in better representations of the actual geographic distributions of species. Furthermore, with the combination of niche models and expert knowledge we were able to identify an area of sympatry between A. palliata mexicana and A. pigra. We argue that the inclusion of expert knowledge at different stages in the construction of niche models in an explicit and systematic fashion is a recommended practice as it produces overall positive results for representing realized species distributions.

A virtual pointer to support the adoption of professional vision in laparoscopic training

PURPOSE

To assess a virtual pointer in supporting surgical trainees' development of professional vision in laparoscopic surgery.

METHODS

We developed a virtual pointing and telestration system utilizing the Microsoft Kinect movement sensor as an overlay for any imagine system. Training with the application was compared to a standard condition, i.e., verbal instruction with un-mediated gestures, in a laparoscopic training environment. Seven trainees performed four simulated laparoscopic tasks guided by an experienced surgeon as the trainer. Trainee performance was subjectively assessed by the trainee and trainer, and objectively measured by number of errors, time to task completion, and economy of movement.

RESULTS

No significant differences in errors and time to task completion were obtained between virtual pointer and standard conditions. Economy of movement in the non-dominant hand was significantly improved when using virtual pointer ([Formula: see text]). The trainers perceived a significant improvement in trainee performance in virtual pointer condition ([Formula: see text]), while the trainees perceived no difference. The trainers' perception of economy of movement was similar between the two conditions in the initial three runs and became significantly improved in virtual pointer condition in the fourth run ([Formula: see text]).

CONCLUSIONS

Results show that the virtual pointer system improves the trainer's perception of trainee's performance and this is reflected in the objective performance measures in the third and fourth training runs. The benefit of a virtual pointing and telestration system may be perceived by the trainers early on in training, but this is not evident in objective trainee performance until further mastery has been attained. In addition, the performance improvement of economy of motion specifically shows that the virtual pointer improves the adoption of professional vision- improved ability to see and use laparoscopic video results in more direct instrument movement.

Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning

Background

Decision-making in mental health systems should be supported by the evidence-informed knowledge transfer of data. Since mental health systems are inherently complex, involving interactions between its structures, processes and outcomes, decision support systems (DSS) need to be developed using advanced computational methods and visual tools to allow full system analysis, whilst incorporating domain experts in the analysis process. In this study, we use a DSS model developed for interactive data mining and domain expert collaboration in the analysis of complex mental health systems to improve system knowledge and evidence-informed policy planning.

Methods

We combine an interactive visual data mining approach, the self-organising map network (SOMNet), with an operational expert knowledge approach, expert-based collaborative analysis (EbCA), to develop a DSS model. The SOMNet was applied to the analysis of healthcare patterns and indicators of three different regional mental health systems in Spain, comprising 106 small catchment areas and providing healthcare for over 9 million inhabitants. Based on the EbCA, the domain experts in the development team guided and evaluated the analytical processes and results. Another group of 13 domain experts in mental health systems planning and research evaluated the model based on the analytical information of the SOMNet approach for processing information and discovering knowledge in a real-world context. Through the evaluation, the domain experts assessed the feasibility and technology readiness level (TRL) of the DSS model.

Results

The SOMNet, combined with the EbCA, effectively processed evidence-based information when analysing system outliers, explaining global and local patterns, and refining key performance indicators with their analytical interpretations. The evaluation results showed that the DSS model was feasible by the domain experts and reached level 7 of the TRL (system prototype demonstration in operational environment).

Conclusions

This study supports the benefits of combining health systems engineering (SOMNet) and expert knowledge (EbCA) to analyse the complexity of health systems research. The use of the SOMNet approach contributes to the demonstration of DSS for mental health planning in practice.

Electronic supplementary material

The online version of this article (10.1186/s12961-018-0308-y) contains supplementary material, which is available to authorized users.

Expert Knowledge Influences Decision-Making for Couples Receiving Positive Prenatal Chromosomal Microarray Testing Results

To assess how participants receiving abnormal prenatal genetic testing results seek information and understand the implications of results, 27 US female patients and 12 of their male partners receiving positive prenatal microarray testing results completed semi-structured phone interviews. These interviews documented participant experiences with chromosomal microarray testing, understanding of and emotional response to receiving results, factors affecting decision-making about testing and pregnancy termination, and psychosocial needs throughout the testing process. Interview data were analyzed using a modified grounded theory approach. In the absence of certainty about the implications of results, understanding of results is shaped by biomedical expert knowledge (BEK) and cultural expert knowledge (CEK). When there is a dearth of BEK, as in the case of receiving results of uncertain significance, participants rely on CEK, including religious/spiritual beliefs, "gut instinct," embodied knowledge, and social network informants. CEK is a powerful platform to guide understanding of prenatal genetic testing results. The utility of culturally situated expert knowledge during testing uncertainty emphasizes that decision-making occurs within discourses beyond the biomedical domain. These forms of "knowing" may be integrated into clinical consideration of efficacious patient assessment and counseling.

Knowing More by Knowing Less? A Reading of Give Me Everything You Have. On Being Stalked by James Lasdun, London: Jonathan Cape, 2013

James Lasdun's memoir of being stalked, Give Me Everything You Have, has provoked considerable controversy. Whilst the quality of the writing is widely praised, some critics object to the way Lasdun documents in unsparing detail his experiences without taking any account of the stalker's apparent mental health problems. There are ethical and conceptual problems with Lasdun's approach, but side-stepping medical knowledge and relying on what we might call common sense help Lasdun to find ways to interpret his stalker's actions as meaningful and human. I suggest three interlinked implications concerning: medicalization, stigma, and the relationship between ethics and scientific knowledge.

Investigating Public trust in Expert Knowledge: Narrative, Ethics, and Engagement

"Public Trust in Expert Knowledge: Narrative, Ethics, and Engagement" examines the social, cultural, and ethical ramifications of changing public trust in the expert biomedical knowledge systems of emergent and complex global societies. This symposium was conceived as an interdisciplinary project, drawing on bioethics, the social sciences, and the medical humanities. We settled on public trust as a topic for our work together because its problematization cuts across our fields and substantive research interests. For us, trust is simultaneously a matter of ethics, social relations, and the cultural organization of meaning. We share a commitment to narrative inquiry across our fields of expertise in the bioethics of transformative health technologies, public communications on health threats, and narrative medicine. The contributions to this symposium have applied, in different ways and with different effects, this interdisciplinary mode of inquiry, supplying new reflections on public trust, expertise, and biomedical knowledge.

Integrating expert knowledge in a GIS to optimize siting decisions for small-scale healthy food retail interventions

BACKGROUND

The availability of healthy foods in a neighborhood remains a key determinant of diet and diet-related disease in disadvantaged communities. Innovative solutions to the 'food desert' problem include the deployment of mobile markets and healthy corner store initiatives. Such initiatives, however, do not always capitalize on the principles guiding retail development and the possibilities of GIS-based data. Simultaneously, community partners are not always engaged effectively in the planning for such interventions, which limits acceptability and suitability of such work.

METHODS

This paper highlights the results of a participatory mapping exercise to optimize the siting of a planned healthy food retail intervention in Flint, Michigan. Potential sites are chosen by engaging experts in a three-stage mapping process that includes the analytic hierarchy process and point allocation of five key variables (including food access, socioeconomic distress, population density, access to transit, and proximity to neighborhood centers), as well as direct mapping of suitable sites.

RESULTS

Results suggest a discrete set of areas-primarily in the northwestern quadrant of the city-where small-scale healthy food retail interventions might be most strategically located. Areas with the most consistent overlap between directly mapped sites and very high levels of suitability align well with neighborhoods which are distant from existing grocery stores.

CONCLUSIONS

As a community-based strategy, this increases the opportunity for effectively improving neighborhood access to healthy foods by optimizing the potential sites for healthy food interventions. Community partners have already been active in using these results in project planning for just such an intervention.

From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support

OBJECTIVES

(1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; (2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; (3) To ensure the BN model can be used for interventional analysis; (4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available.

METHOD

The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P.

RESULTS

When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence.

CONCLUSIONS

This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way.

Definition of sampling units begets conclusions in ecology: the case of habitats for plant communities

In ecology, expert knowledge on habitat characteristics is often used to define sampling units such as study sites. Ecologists are especially prone to such approaches when prior sampling frames are not accessible. Here we ask to what extent can different approaches to the definition of sampling units influence the conclusions that are drawn from an ecological study? We do this by comparing a formal versus a subjective definition of sampling units within a study design which is based on well-articulated objectives and proper methodology. Both approaches are applied to tundra plant communities in mesic and snowbed habitats. For the formal approach, sampling units were first defined for each habitat in concave terrain of suitable slope using GIS. In the field, these units were only accepted as the targeted habitats if additional criteria for vegetation cover were fulfilled. For the subjective approach, sampling units were defined visually in the field, based on typical plant communities of mesic and snowbed habitats. For each approach, we collected information about plant community characteristics within a total of 11 mesic and seven snowbed units distributed between two herding districts of contrasting reindeer density. Results from the two approaches differed significantly in several plant community characteristics in both mesic and snowbed habitats. Furthermore, differences between the two approaches were not consistent because their magnitude and direction differed both between the two habitats and the two reindeer herding districts. Consequently, we could draw different conclusions on how plant diversity and relative abundance of functional groups are differentiated between the two habitats depending on the approach used. We therefore challenge ecologists to formalize the expert knowledge applied to define sampling units through a set of well-articulated rules, rather than applying it subjectively. We see this as instrumental for progress in ecology as only rules based on expert knowledge are transparent and lead to results reproducible by other ecologists.

Bayesian networks for maritime traffic accident prevention: benefits and challenges

Bayesian networks are quantitative modeling tools whose applications to the maritime traffic safety context are becoming more popular. This paper discusses the utilization of Bayesian networks in maritime safety modeling. Based on literature and the author's own experiences, the paper studies what Bayesian networks can offer to maritime accident prevention and safety modeling and discusses a few challenges in their application to this context. It is argued that the capability of representing rather complex, not necessarily causal but uncertain relationships makes Bayesian networks an attractive modeling tool for the maritime safety and accidents. Furthermore, as the maritime accident and safety data is still rather scarce and has some quality problems, the possibility to combine data with expert knowledge and the easy way of updating the model after acquiring more evidence further enhance their feasibility. However, eliciting the probabilities from the maritime experts might be challenging and the model validation can be tricky. It is concluded that with the utilization of several data sources, Bayesian updating, dynamic modeling, and hidden nodes for latent variables, Bayesian networks are rather well-suited tools for the maritime safety management and decision-making.

Expert and experiential knowledge in the same place: patients' experiences with online communities connecting patients and health professionals

OBJECTIVE

To explore patients' experiences with online health communities in which both physicians and patients participate (i.e. patient-to-doctor or 'P2D' communities).

METHODS

A qualitative content analysis was conducted, based on observations in five P2D communities ranging from 8 to 21 months, and semi-structured interviews (N=17) with patients.

RESULTS

Patients consider information from physicians and peers as two distinct sources, value both sources differently and appreciate accessing both in the same web space. According to respondents, physicians can provide 'reliable' and evidence-based information, while patients add experience-based information. Patients use this information for multiple purposes, including being informed about scientific research and personal reflection.

CONCLUSION

Patients find P2D communities beneficial because they help patients to collect information from both medical experts and experiential experts in one place.

PRACTICE IMPLICATIONS

Patients use P2D communities to perform medical, emotional and lifestyle activities. The presence of physicians in P2D communities may inadvertently suggest that the quality of information used for the activities, is controlled. When information is not officially being checked, this should be stated explicitly on the website and supplemented with a statement that information is only indicative and that patients should at all times contact their own physicians.

Emergent authority and expert knowledge: psychiatry and criminal responsibility in the UK

In the UK context, the rise of the discipline and practice of forensic psychiatry is intimately connected with the concurrent development of principles and practices relating to criminal responsibility. In this article, we seek to chart the relationship between psychiatry and the principles and practices of criminal responsibility in the UK over the early modern, modern and late modern periods. With a focus on claims about authority and expert knowledge around criminal responsibility, we suggest that these claims have been in a state of perpetual negotiation and that, as a result, claims to authority over and knowledge about criminal non-responsibility on the part of psychiatrists and psychiatry are most accurately understood as emergent and contingent. The apparent formalism of legal discourse has tended to conceal the extent to which legal policy has been preoccupied with maintaining the primacy of lay judgments in criminal processes of evaluation and adjudication. While this policy has been somewhat successful in the context of the trial - particularly the murder trial - it has been undermined by administrative procedures surrounding the trial, including those that substitute treatment for punishment without, or in spite of, a formal determination of criminal responsibility.

Expert knowledge sourcing for public health surveillance: national tsetse mapping in Uganda

In much of sub-Saharan Africa, availability of standardized and reliable public health data is poor or negligible. Despite continued calls for the prioritization of improved health datasets in poor regions, public health surveillance remains a significant global health challenge. Alternate approaches to surveillance and collection of public health data have thus garnered increasing interest, though there remains relatively limited research evaluating these approaches for public health. Herein, we present a case study applying and evaluating the use of expert knowledge sources for public health dataset development, using the case of vector distributions of Human African Trypanosomiasis (HAT) in Uganda. Specific objectives include: 1) Review the use of expert knowledge sourcing methods for public health surveillance, 2) Review current knowledge on tsetse vector distributions of public health importance in Uganda and the methods used for tsetse mapping in Africa; 3) Quantify confidence of the presence or absence of tsetse flies in Uganda based on expert informant reports, and 4) Assess the reliability and potential utility of expert knowledge sourcing as an alternative or complimentary method for public health surveillance in general and tsetse mapping in particular. Information on tsetse presence or absence, and associated confidence, was collected through interviews with District Entomologist and Veterinary Officers to develop a database of tsetse distributions for 952 sub-counties in Uganda. Results show high consistency with existing maps, indicating potential reliability of modeling approaches, though failing to provide evidence for successful tsetse control in past decades. Expert-sourcing methods provide a novel, low-cost and rapid complimentary approach for triangulating data from prediction modeling where field-based validation is not feasible. Data quality is dependent, however, on the level of expertise and documentation to support confidence levels for data reporting. Results highlight the need for increased evaluation of alternate approaches and methods to data collection.