Decision Support Tool Research Articles

A Nursing Clinical Care Services Platform to Support Leadership Decision Making.

Like most organizations, The Children's Hospital of Philadelphia (CHOP) generates a lot of data of varying types, from clinical to staffing and operations. However, these datasets are separated, preventing effective analytics. To improve workforce management and care delivery, CHOP's Nursing department collaborated with the Data & Analytics team to create a dashboard-based platform for analyzing workforce metrics and patient outcomes, aiding nursing leaders in decision-making. This project involved multiple phases with contributions from Nursing, Data & Analytics, CHOP's Data Trust Office, and Human Resources. Key performance indicators were identified, and a variety of data sources were aggregated to provide a comprehensive view of the nursing enterprise. The resulting platform offers automated, current, and reliable analytics on various aspects, including nursing demographics, education, survey results, staffing actuals by job group, and patient and family experience data. The platform's usability was assessed using a modified Health Information Technology Usability Evaluation Scale survey, with a 29% response rate primarily from senior directors and managers. The findings showed high usability and satisfaction, indicating the dashboard is a valuable decision-support tool. Lessons learned include the need for analytics education for nurses and mid-managers, the inclusion of critical nursing-specific metrics (and development of the data pipelines making them possible), and the integration of multidisciplinary team metrics for comprehensive nursing analytics.

Accounting for minimum data required to train a machine learning model to accurately monitor Australian dairy pastures using remote sensing

Precision in grazing management is highly dependent on accurate pasture monitoring. Typically, this is often overlooked because existing approaches are labour-intensive, need calibration, and are commonly perceived as inaccurate. Machine-learning processes harnessing big data, including remote sensing, can offer a new era of decision-support tools (DST) for pasture monitoring. Its application on-farm remains poor because of a lack of evidence about its accuracy. This study aimed at evaluating and quantifying the minimum data required to train a machine-learning satellite-based DST focusing on accurate pasture biomass prediction using this approach. Management data from 14 farms in New South Wales, Australia and measured pasture biomass throughout 12 consecutive months using a calibrated rising plate meter (RPM) as well as pasture biomass estimated using a DST based on high temporal/spatial resolution satellite images were available. Data were balanced according to farm and week of each month and randomly allocated for model evaluation (20%) and for progressive training (80%) as follows: 25% training subset (1W: week 1 in each month); 50% (2W: week 1 and 3); 75% (3W: week 1, 3, and 4); and 100% (4W: week 1 to 4). Pasture biomass estimates using the DST across all training datasets were evaluated against a calibrated rising plate meter (RPM) using mean-absolute error (MAE, kg DM/ha) among other statistics. Tukey’s HSD test was used to determine the differences between MAE across all training datasets. Relative to the control (no training, MAE: 498 kg DM ha−1) 1W did not improve the prediction accuracy of the DST (P > 0.05). With the 2W training dataset, the MAE decreased to 342 kg DM ha−1 (P < 0.001), while for the other training datasets, MAE decreased marginally (P > 0.05). This study accounts for minimal training data for a machine-learning DST to monitor pastures from satellites with comparable accuracy to a calibrated RPM which is considered the ‘gold standard’ for pasture biomass monitoring.

Using chat GPT to evaluate police threats, risk and harm

General purpose artificial intelligence (GPAI) is a form of advanced AI system that includes the recently introduced ChatGPT. GPAI is known for its capacity to understand and emulate human responses, and potentially offers an opportunity to reduce human error when conducting tasks that involve analysis, judgement, and reasoning. To support officers to do this, the police presently use a range of decision-making support tools, one of which is called THRIVE (Threat, Harm, Risk, Investigation, Vulnerability, and Engagement). THRIVE is designed to provide police practitioners with a model to improve their identification and response to vulnerability. Despite the existence of such decision models, a 2020 meta-analysis of police cases resulting in death or serious injury identified contributory failures that included poor risk identification, risk management, failure to adhere to evidentiary processes, poor criminal investigations, and inadequate police engagement with victims, including the level of care and assistance provided (Allnock, et al, 2020). Importantly, this report outlined human error as being a major underpinning factor of the failures.Although GPAI offers an opportunity to improve analysis, judgement, and reasoning, such systems have not yet been tested in policing, a field where any reduction in human error, particularly in the assessment of threat, harm, risk, and vulnerability can potentially save lives. This study is the first attempt to do this by using the chain-of-thought prompt methodology to test the GPAI ChatGPT (3.5 vs 4) in a controlled environment using 30 life-like police scenarios, crafted, and analyzed by expert practitioners. In doing so, we identify that ChatGPT 4 significantly outperforms its 3.5 predecessor, indicating that GPAI presents considerable opportunity in policing. However, systems that use this technology require extensive directional prompting to ensure outputs that can be considered accurate, and therefore, potentially safe to utilize in an operational setting. The article concludes by discussing how practitioners and researchers can further refine police related chain-of-thought prompts or use application programming interfaces (APIs) to improve responses provided by such GPAI.

The classification algorithms to support the management of the patient with femur fracture

Effectiveness in health care is a specific characteristic of each intervention and outcome evaluated. Especially with regard to surgical interventions, organization, structure and processes play a key role in determining this parameter. In addition, health care services by definition operate in a context of limited resources, so rationalization of service organization becomes the primary goal for health care management. This aspect becomes even more relevant for those surgical services for which there are high volumes. Therefore, in order to support and optimize the management of patients undergoing surgical procedures, the data analysis could play a significant role. To this end, in this study used different classification algorithms for characterizing the process of patients undergoing surgery for a femoral neck fracture. The models showed significant accuracy with values of 81%, and parameters such as Anaemia and Gender proved to be determined risk factors for the patient’s length of stay. The predictive power of the implemented model is assessed and discussed in view of its capability to support the management and optimisation of the hospitalisation process for femoral neck fracture, and is compared with different model in order to identify the most promising algorithms. In the end, the support of artificial intelligence algorithms laying the basis for building more accurate decision-support tools for healthcare practitioners.

Enhancing irrigation water productivity using short-range ensemble weather forecasts at basin scale: A novel framework for regions with high hydro-climatic variability

Integrating short-term weather forecasts with crop growth models has emerged as a valuable decision-support tool for enhancing irrigation water productivity in water-intensive crops. Our primary focus lies in formulating a simplified Quasi-Farmer Behavior Routine to utilize ensemble weather forecasts to provide irrigation guidance. The study proposes the concept of Rainfall Confidence Quotient (RCQ) that is optimized based on the forecast uncertainty levels for rainfall categories that affect the irrigation decision. We utilize two publicly available ensemble weather forecasts from NCEP-GEFS and ECM and a regionally fine-tuned high-resolution Weather Research and Forecasting (WRF) ensemble forecast system with a 3-day lead time. We have refined our methodology by conducting 800 simulation experiments across diverse scenarios, each reflecting distinct climate regimes, soil types, cropping seasons, and management practices. The proposed method consists of a sequential 3-checkpoint algorithm incorporating ensemble rainfall forecasts, antecedent soil moisture, and evapotranspiration. During the wet crop season of 2015–16, the enhanced methodology yielded a substantial increase in irrigation water productivity (IWP), averaging around 20–30 % increase across spatial locations, in contrast to the conventional irrigation practices that do not consider weather forecasts. However, during 2016–17, a dry year, the improvement in IWP only ranged between 2–10 %. On average, water savings (at field scale) of 200–500 mm were recorded in the wet year crop season of 2015–16, while 100–300 mm of water savings were achieved during the dry year crop season of 2016–17, without any considerable reductions in the crop yield. The robustness and adaptability of the developed approach have been established through comprehensive evaluation with field observation and remote sensing techniques, suggesting its potential scalability to similar hydro-climatic typologies across the world.

To move or not? Tourists’ perceptions and management considerations of a beached whale carcass in a South African national park and marine protected area

The handling of beached cetacean carcasses requires social, legal, financial, ecological and logistical considerations. However, limited research on the topic hinders informed decision-making. A large humpback whale carcass that washed ashore at a South African marine protected area (MPA) provided an opportunity to gain insight into tourists’ perspectives on carcass management within a national park and MPA setting. Eighty tourists were interviewed, and a thematic analysis approach was adopted to identify key themes and quantify the responses. Chi-square tests of independence were used to determine (i) whether the respondents’ views on cetacean carcass management were dependent on their knowledge of the site’s MPA designation, and (ii) whether the provision of contextual information would influence the respondents’ perceptions. Tourists had mixed responses to whale carcass management, with half the respondents indicating that the carcass should be left to decompose naturally. Viewpoints were significantly influenced when context and information on the decision-making process were provided, with more respondents stating that no management intervention should be necessary. We propose a simple flow-diagram as a decision-support tool, which, in combination with spatial zonation maps to identify applicable management options, will help guide decision-making for management authorities. Inclusivity, transparency and consultation with experienced role-players from multiple agencies will provide legitimacy to the final management decisions.

Multi-Objective Optimization Methods for Train Load Planning in Seaport Container Terminals

In this paper we study the planning problem of loading trains in seaport container terminals, which consists in assigning containers to the wagon slots. The train load can be realized considering different objectives, in this work we focus on two very important objectives for terminal managers, that are the minimization of the number of reshuffles in the storage area and the minimization of the total distance covered by the handling equipment between the storage area and the wagons. To address this problem, some multi-objective methods are investigated in the paper, based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon$</tex-math> </inline-formula> -constraint approach and the lexicographic algorithm, allowing to find different sets of efficient solutions to be provided to train planners. The experimental tests, devised to compare the proposed multi-objective methods, show that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon$</tex-math> </inline-formula> -constraint method integrated with a lexicographic approach is the most efficient choice since it allows to obtain a higher number of efficient solutions in shorter computational times. This method can be applied in decision support systems for train planners in order to allow them to decide the train load in an efficient way. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The automation and the process optimization in seaport container terminals is a relevant issue for terminal managers in order to face the strong competitiveness in the logistics and shipping sector and the new emerging problems due to the presence of mega vessels. Among the different operations to be optimally managed in a terminal, the loading of trains is becoming more and more significant, also due to the increase of freight traffic by rail, seen as a cheaper and more sustainable option compared with road transport. The optimal assignment of containers present in a storage area to the wagon slots is a crucial decision in order to reduce operational times and costs in a container terminal. In this paper, we address the train load planning problem considering two important objectives to be minimized, i.e. the number of reshuffles and the distance covered by the handling equipment. The multi-objective methods proposed in the paper are devised to find different efficient solutions in short computational times. The proposed algorithms are intended to be implemented within a decision support tool in which the solutions found are shown to the train planner who chooses the preferred option depending on personal evaluations and on the basis of information coming in real time.

Developing a transdisciplinary tool for water risk management and decision-support in Ontario, Canada

Extant literature reveals limited examination of risk management strategies and tools to support decision-making for sustainable water management in the private sector in Ontario, Canada. Moreover, a gap persists in understanding how water risks are prioritized and managed in the private sector. Addressing these gaps, this transdisciplinary study applied a novel normative-analytical risk governance theoretical framework to water security risks, which combines analytical risk estimation with normative priorities and insights of practitioners, to examine contextually-attuned water risk management strategies and develop a decision-support tool. Using mixed methods, the study first employed a survey to elicit practitioner priorities for seven water risk indicators and investigated water risk management approaches. Then, interviews were conducted to obtain in-depth understanding about the priorities, strategies, opportunities, and role of trust in water risk management. The study found that a combination of regulatory, voluntary, and multi-stakeholder participatory approaches is needed, contingent on the severity of water risks, sector, location, and context. Moreover, the criteria of flexibility, efficiency, strategic incentives, and economic and regulatory signals, are essential. Finally, using secondary data analysis, the study integrated interdisciplinary risk data with practitioner priorities to develop a first-of-a-kind decision-support tool for water risk management in Ontario, ‘WATR-DST’. WATR-DST is an automated tool that applies the study’s findings and assists multi-sector water-related decisions, practices, and investments by providing contextually-attuned risk information in a user-friendly format. Based on the user inputs (location, sector, and source type), it displays the severity of seven water risks, qualitative themes under public and media attention, and recommends water risk management strategies. Thus, the study contributes to knowledge in sustainability management, risk analysis, and environmental management by demonstrating the novel application of the normative-analytical framework for water risk management in the private sector. WATR-DST is a key contribution envisioned to improve multi-sector water-related decisions in Ontario.

Identifying, Understanding, and Minimizing Unconscious Cognitive Biases in Perioperative Crisis Management: A Narrative Review.

Rapid clinical decision-making behavior is often based on pattern recognition and other mental shortcuts. Although such behavior is often faster than deliberative thinking, it can also lead to errors due to unconscious cognitive biases (UCBs). UCBs may contribute to inaccurate diagnoses, hamper interpersonal communication, trigger inappropriate clinical interventions, or result in management delays. The authors review the literature on UCBs and discuss their potential impact on perioperative crisis management. Using the Scale for the Assessment of Narrative Review Articles (SANRA), publications with the most relevance to UCBs in perioperative crisis management were selected for inclusion. Of the 19 UCBs that have been most investigated in the medical literature, the authors identified 9 that were judged to be clinically relevant or most frequently occurring during perioperative crisis management. Formal didactic training on concepts of deliberative thinking has had limited success in reducing the presence of UCBs during clinical decision-making. The evolution of clinical decision support tools (CDSTs) has demonstrated efficacy in improving deliberative clinical decision-making, possibly by reducing the intrusion of maladaptive UCBs and forcing reflective thinking. Anesthesiology remains a leader in perioperative crisis simulation and CDST implementation, but spearheading innovations to reduce the adverse impact of UCBs will further improve diagnostic precision and patient safety during perioperative crisis management.

Forecasting potential invaders to prevent future biological invasions worldwide.

The ever-increasing and expanding globalisation of trade and transport underpins the escalating global problem of biological invasions. Developing biosecurity infrastructures is crucial to anticipate and prevent the transport and introduction of invasive alien species. Still, robust and defensible forecasts of potential invaders are rare, especially for species without known invasion history. Here, we aim to support decision-making by developing a quantitative invasion risk assessment tool based on invasion syndromes (i.e., generalising typical attributes of invasive alien species). We implemented a workflow based on 'Multiple Imputation with Chain Equation' to estimate invasion syndromes from imputed datasets of species' life-history and ecological traits and macroecological patterns. Importantly, our models disentangle the factors explaining (i) transport and introduction and (ii) establishment. We showcase our tool by modelling the invasion syndromes of 466 amphibians and reptile species with invasion history. Then, we project these models to amphibians and reptiles worldwide (16,236 species [c.76% global coverage]) to identify species with a risk of being unintentionally transported and introduced, and risk of establishing alien populations. Our invasion syndrome models showed high predictive accuracy with a good balance between specificity and generality. Unintentionally transported and introduced species tend to be common and thrive well in human-disturbed habitats. In contrast, those with established alien populations tend to be large-sized, are habitat generalists, thrive well in human-disturbed habitats, and have large native geographic ranges. We forecast that 160 amphibians and reptiles without known invasion history could be unintentionally transported and introduced in the future. Among them, 57 species have a high risk of establishing alien populations. Our reliable, reproducible, transferable, statistically robust and scientifically defensible quantitative invasion risk assessment tool is a significant new addition to the suite of decision-support tools needed for developing a future-proof preventative biosecurity globally.

Using machine-learning models to predict extubation failure in neonates with bronchopulmonary dysplasia

AimTo develop a decision-support tool for predicting extubation failure (EF) in neonates with bronchopulmonary dysplasia (BPD) using a set of machine-learning algorithms.MethodsA dataset of 284 BPD neonates on mechanical ventilation was used to develop predictive models via machine-learning algorithms, including extreme gradient boosting (XGBoost), random forest, support vector machine, naïve Bayes, logistic regression, and k-nearest neighbor. The top three models were assessed by the area under the receiver operating characteristic curve (AUC), and their performance was tested by decision curve analysis (DCA). Confusion matrix was used to show the high performance of the best model. The importance matrix plot and SHapley Additive exPlanations values were calculated to evaluate the feature importance and visualize the results. The nomogram and clinical impact curves were used to validate the final model.ResultsAccording to the AUC values and DCA results, the XGboost model performed best (AUC = 0.873, sensitivity = 0.896, specificity = 0.838). The nomogram and clinical impact curve verified that the XGBoost model possessed a significant predictive value. The following were predictive factors for EF: pO2, hemoglobin, mechanical ventilation (MV) rate, pH, Apgar score at 5 min, FiO2, C-reactive protein, Apgar score at 1 min, red blood cell count, PIP, gestational age, highest FiO2 at the first 24 h, heart rate, birth weight, pCO2. Further, pO2, hemoglobin, and MV rate were the three most important factors for predicting EF.ConclusionsThe present study indicated that the XGBoost model was significant in predicting EF in BPD neonates with mechanical ventilation, which is helpful in determining the right extubation time among neonates with BPD to reduce the occurrence of complications.

Multitask Neural Networks to Predict Antimicrobial Susceptibility Results of Escherichia coli Clinical Isolates

Background: Machine-learning (ML) models, such as neural networks (NNs), have been proposed to predict antimicrobial susceptibility at the patient level while incorporating patient-level information from electronic medical record (EMR) systems. However, NNs often do not perform well in predicting rare outcomes, such as carbapenem resistance. We aimed to apply a novel multitask NN to create personalized antibiograms for individual patients with Escherichia coli clinical isolates to predict antimicrobial resistance (AMR) for four major antimicrobial classes simultaneously with improved accuracy for carbapenem resistance by using shared hidden layers (Figure 1). Methods: We analyzed all E. coli clinical isolates from the US Veterans Health Administration’s network from January 1, 2017, to December 31, 2019, focusing on AMR profiles of aminopenicillins, narrow-spectrum (NS) cephalosporins, extended-spectrum (ES) cephalosporins, and carbapenems. Patient-level clinical data (demographics, antimicrobial exposure history, previous isolates (if any), comorbidities, and recent procedures) were extracted from EMR. Antibiograms for all hospitals were generated using standard methods for the preceding calendar years. We employed logistic regression to evaluate the efficacy of conventional antibiograms in predicting AMR profiles. We adopted the ML approach using conventional NNs and novel multitask NNs on all extracted clinical data and hospital antibiograms. The models were trained with data from 2017 and 2018 and then tested on 2019 data, assessing their performance using the area under the receiver-operating curve (AUC). Results: The study included 257,968 E. coli isolates, split into 171,391 for training and 86,577 for validation. The prevalence of AMR in the test data from 2019 was 49.8% for aminopenicillins, 28.4% for NS cephalosporins, 10.7% for ES cephalosporins, and 0.2% for carbapenems, respectively. Conventional hospital antibiograms showed low prediction accuracy with AUC scores of 0.56 for aminopenicillins, 0.67 for NS cephalosporins, 0.61 for ES cephalosporin, and 0.67 for carbapenem. AUC scores from preliminary models for conventional and multitask NNs were 0.78/0.78 for aminopenicillins, 0.83/0.82 for NS cephalosporins, 0.84/0.85 for ES cephalosporins, 0.68/0.75 for carbapenems. While producing improved accuracy for carbapenem and comparable accuracies for three other classes, multitask NNs took approximately 66% less time for model training than conventional NNs. Conclusions: Integrating EMR data with NNs improved their predictive accuracy, potentially leading to a decision-support tool for better empirical antimicrobial therapy guidance in the window between species identification and confirmed susceptibilities. Multitask NNs can potentially improve the prediction accuracy of uncommon AMRs while maintaining comparable prediction accuracies for common AMRs and optimizing the efficiency of model training.Disclosure: Michi Goto: Contracted Research - Merck

A machine learning-based protocol to support visual tree assessment and risk of failure classification on a university campus

Tree failure risk assessment involves visually evaluating trees by considering three essential factors: identifying potential targets that may be affected if the tree falls, assessing the potential consequences of the fall, and determining the likelihood of tree failure. This assessment was used to evaluate the safety of trees in a study area at Thammasat University Rangsit Center, Thailand. In two priority-selected areas for tree risk management, 3659 trees representing 139 species were assessed, and to understand the spatial patterns of tree health conditions and risks, the study employed a GIS-based mapping methodology to manage tree inventory and analyze the spatial patterns of tree health conditions and risks. A decision tree protocol based on the chi-squared automatic interaction detector (CHAID) algorithm, which employs machine learning, was used to evaluate the risk of tree failure. Our study successfully identified seven variables that are crucial in assessing the risk of tree failure. According to the findings, the overall accuracy rate of failure risk classification was 87.35 %, and of all the trees evaluated, 280 trees (7.65 % of the total) representing 34 different species were at high risk. It is recommended to start the assessment process by evaluating important variables such as tree cavities, pest infestations, mechanical damage, dead branches, and epicormic growth. Machine learning protocols, integrated with GIS, are shown to be effective, spatially-explicit, decision-support tools for detecting tree failure potential and assessing risk ratings. Application of these tools improves tree risk management practices.

MEMF: Multi-entity multimodal fusion framework for sales prediction in live streaming commerce

Live streaming commerce thrives with a rich tapestry of multimodal information that intertwines with various entities, including the anchor, the commodities, and the live streaming environment. Despite the wealth of data at hand, the synthesis and analysis of this information to predict sales remains a significant challenge. This study introduces a framework for multi-entity multimodal fusion, which is characterized by the effective synthesis of multimodal data and its prioritization of entity-level fusion, thereby providing a comprehensive feature representation for improving predictive performance. In addressing the multimodal data associated with a diverse range of products, our framework improves the Transformer architecture to initially capture the intra-product modal features and subsequently integrate the inter-product features. Data experiments are conducted on a real-world dataset from Taobao Live. The framework outperforms both traditional machine learning methods and state-of-the-art multimodal fusion methods, which affirms its value as a robust decision-support tool for sales prediction, enabling more accurate pre-event predictions and strategic planning. We also examine the impact of different types of information in accurate sales prediction. It is found that harnessing a comprehensive suite of data leads to optimal performance across all evaluation metrics. Commodity-related data is primary factor in determining the prediction accuracy, followed by video data and streaming room-related data, providing insights regarding the resource allocation for collecting and analyzing multimodal data from live streaming platforms.

Use of digital patient decision-support tools for atrial fibrillation treatments: a systematic review and meta-analysis

ObjectivesTo assess the effects of digital patient decision-support tools for atrial fibrillation (AF) treatment decisions in adults with AF.Study designSystematic review and meta-analysis.Eligibility criteriaEligible randomised controlled trials (RCTs) evaluated digital...

Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging.

Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of subgroup performance disparities. However, since not all sources of bias in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess their impacts. In this article, we introduce an analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models. Our framework utilizes synthetic neuroimages with known disease effects and sources of bias. We evaluated the impact of bias effects and the efficacy of 3 bias mitigation strategies in counterfactual data scenarios on a convolutional neural network (CNN) classifier. The analysis revealed that training a CNN model on the datasets containing bias effects resulted in expected subgroup performance disparities. Moreover, reweighing was the most successful bias mitigation strategy for this setup. Finally, we demonstrated that explainable AI methods can aid in investigating the manifestation of bias in the model using this framework. The value of this framework is showcased in our findings on the impact of bias scenarios and efficacy of bias mitigation in a deep learning model pipeline. This systematic analysis can be easily expanded to conduct further controlled in silico trials in other investigations of bias in medical imaging AI. Our novel methodology for objectively studying bias in medical imaging AI can help support the development of clinical decision-support tools that are robust and responsible.

A climatic suitability indicator to support Leishmania infantum surveillance in Europe: a modelling study

Leishmaniases are neglected diseases transmitted by sand flies. They disproportionately affect vulnerable groups globally. Understanding the relationship between climate and disease transmission allows the development of relevant decision-support tools for public health policy and surveillance. The aim of this modelling study was to develop an indicator that tracks climatic suitability for Leishmania infantum transmission in Europe at the subnational level. Historical records of sand fly vectors, human leishmaniasis, bioclimatic indicators, and environmental variables were integrated in a machine learning framework (XGBoost) to predict suitability in two past periods (2001-2010 and 2011-2020). We further assessed if predictions were associated with human and animal disease data from selected countries (France, Greece, Italy, Portugal, and Spain). An increase in the number of climatically suitable regions for leishmaniasis was detected, especially in southern and eastern countries, coupled with a northward expansion towards central Europe. The final model had excellent predictive ability (AUC=0.970 [0.947-0.993]), and the suitability predictions were positively associated with human leishmaniasis incidence and canine seroprevalence for Leishmania. This study demonstrates how key epidemiological data can be combined with open-source climatic and environmental information to develop an indicator that effectively tracks spatiotemporal changes in climatic suitability and disease risk. The positive association between the model predictions and human disease incidence demonstrates that this indicator could help target leishmaniasis surveillance to transmission hotspots. European Union Horizon Europe Research and Innovation Programme (European Climate-Health Cluster), United Kingdom Research and Innovation.

“I Didn't Know What I Didn't Know”: Assessment of Adolescent Oncology Patient Engagement in Decision-Making

IntroductionLittle is known about the best way to engage an adolescent oncology patient in decision-making. To integrate adolescent oncology patients most effectively in decision-making, it is important to understand their perceptions not only as adolescents but also as adult survivors who have had time to reflect upon their experience. The purpose of this study is to explore perceptions of survivors of pediatric cancer to better understand their attitudes toward participation in decision-making, decisional regret, and use of decision-support tools. MethodsAn electronic survey tool, containing open- and closed-ended responses, was distributed to adult cancer survivors diagnosed and treated for cancer between the ages of 9 and 18 at a Midwestern, Comprehensive Cancer Center within a tertiary care academic hospital. ResultsSeventy responses were received. Nearly all respondents (96%) reported that decisions made on their behalf were consistent with their desires most/all the time. Almost one-fifth felt that increased involvement would have intensified their anxiety. Eighty-five percent did not regret choices made about their cancer treatment, and 88% would have made the same choices again. Respondents desired more targeted information on the long-term impacts of treatment (i.e., infertility, memory difficulties, mental health concerns), and they highlighted the importance of communication in the decision-making process. ConclusionsAdolescents want to participate in the decision-making process in a way that accommodates their understanding of the pathology and potential impacts of treatment. Our results suggest that developing a simple intervention to help facilitate provision of such anticipatory guidance may be helpful.

A Sensemaking Framework for Defensive Cyber Operations: Filling the Void in Leadership Discourse

In the realm of contemporary warfare dominated by cyber threats, Defensive Cyber Operations (DCO) serve as a linchpin for mitigating risks and ensuring mission assurance. This article delves into the intricate landscape of DCO, focusing on the critical role played by Defensive Cyberspace Forces (DCFs). Despite their significance, the absence of a unified sensemaking framework poses a challenge for leaders responsible for the nuanced development and strategic employment of DCFs. The lacuna in the existing literature revolves around the lack of a comprehensive sensemaking framework tailored for operational and DCF leaders. The inadequacies of current frameworks, either overly broad or excessively specific, hinder effective dialogue and understanding. This deficiency not only obstructs the planning efforts and operational tempo of DCO but also restrains the maturation of DCFs, amplifying residual risks faced by commanders. This paper endeavours to present a purpose-built sensemaking framework crafted for leaders engaged in the dynamic realms of DCF development. Integrating well-established risk mitigation principles with the unique organizational structures and missions of DCFs, the framework fills a crucial void in the literature. Beyond being a decision-support tool, it strives to foster a shared mental model, providing a nuanced lens for leaders to contextualize and prioritize their efforts in the complex landscape of DCO. Through a meticulous critique of existing frameworks, this article introduces a tailored model designed to address identified shortcomings. Emphasizing the practical utility of the proposed framework, the discussion unfolds to elucidate how it not only facilitates the development and employment of DCF but also contributes to organizational resilience and risk mitigation. This article contributes a novel sensemaking framework to the academic discourse on DCO. While acknowledging limitations imposed by an unclassified context, the framework provides valuable insights into the strategic dimensions of DCF development and employment, DCO planning intricacies, and organizational analyses. Future avenues for research include the integration of classified information to refine the framework, ensuring its applicability across diverse DCO mission types and aligning DCF core functions with specific threats, thereby enhancing the efficacy of defensive cyber strategies.

Synthetic data for reef modelling

Synthetic data mimics the statistical properties of real-world datasets while removing reference to sensitive or confidential information in the original dataset (Quintana, 2020). Synthetic data is also useful for general model testing and development, with many methods available for generating data from machine learning models (Raghunathan, 2021). Although not widely used in the context of ecological and environmental modelling, synthetic data can support and accelerate model testing and analyses where rightsholders are sensitive to data disclosure for study areas, or data collection is expensive.In the context of reef modelling, synthetic data can be used to support model analyses that can be published without referring to specific sites, reefs, or study areas. This is desirable in the context of decision support for restoration of the Great Barrier Reef. The Reef has many stakeholders and release of early modelling results for intervention scenarios for specific areas would be premature until management or intervention strategy options have been discussed with stakeholders and/or rightsholders. Synthetic data allows a path to publish model and method demonstrations to share knowledge with the reef decision support community without prematurely suggesting policy recommendations for reefs which are sensitive to rightsholders or stakeholders.We showcase a synthetic data pipeline developed for the reef decision-support system ADRIA (Adaptive Dynamic Reef Intervention Algorithms), using methods from the Python package Synthetic Data Vault (Patki et al., 2016) and others. The synthetic data models are developed to emulate the statistics of case-study reefs for publishing decision-support tool demonstrations, testing and method validation without revealing sensitive reef site information. This pipeline includes developing models for tabular (benthic/compositional reef data), spatial-temporal (wave and heat stress data) and spatial network data (coral larval connectivity). Conditional sampling methods which connect spatial relationships across datasets are used to develop synthetic reef data packages which mimic the statistical properties of the original dataset. The utility of the synthetic data is demonstrated on a sample reef data package, and methods used for anonymizing the data are detailed. The results are discussed in the context of formalizing synthetic data for reef modelling. All synthetic data code is available at ADRIA-synthetic-data/README.md at v0.1.0 · open-AIMS/ADRIA-synthetic-data (github.com), DOI: https://doi.org/10.5281/zenodo.10158323.