Real-time Decision Support Research Articles

Which models for extreme flood events in Mediterranean catchments?

Over the last years, most of the Mediterranean countries have been affected by catastrophic flood events generated by never observed before extreme rainfalls. Those events are frequently convective and stationary for several hours. The recorded intensities have frequently values exceeding 100 mm/hour and can last for several hours. Under those specific conditions as during the storm ALEX of October 2020, the runoff processes are particularly intense and can generate massive floods. Mitigation strategies should be developed and based on modeling that can support the understanding of the processes and the forecast the magnitude of the flood events and the associated impacts. The choice of the relevant hydrological modeling tools is a key element. The selected ones should be able to provide the relevant information, on time and with the relevant accuracy for the decision makers. The hydrological deterministic distributed models have reached a sufficient maturity to provide an accurate representation of the processes. The storm ALEX was generating more than 500 mm in less than 6 hours and producing a devastating flood within the Vésubie valley (France) in October 2020. The analysis of the event has demonstrated the difficulty to obtain consistent field observations and the limitations of many stochastic hydrological tools. The application of the AquaVar approach, combining several distributed deterministic models, has demonstrated its capacity to generate meaningful hydrographs and to quantify process with the catchment. The proposed solution is currently implemented within a real-time decision support system and can be used in real-time.

Verification of the NACAC atmospheric dispersion calculation using a hypothetical accident in a neighboring nuclear power plant

Calculating atmospheric dispersion in a hypothetical nuclear accident plays an essential role as a predictive tool in nuclear emergency planning. The Nuclear Accident Consequence Analysis Code (NACAC) has been developed as an in-house code by the Thailand Institute of Nuclear Technology to develop its capability for understanding the transboundary radiological effect of a hypothetical nuclear accident. To verify the performance of the NACAC, a comparison between the calculated results using the NACAC with those from the well-developed Java-based Real-time On-line Decision Support (JRODOS) code is performed with representative cases of all seasons in a year. Two hypothetical severe accidents at the Fangchenggang pressurized water reactor, namely, (i) a loss of off-site power and (ii) a large-break loss of coolant, are utilized as the initial boundary conditions. The air concentration characteristic maps obtained from both codes are analyzed, revealing that the dominant radionuclide dispersion pathways are similar in all study cases. The air concentration, ground concentration, and total effective dose rate at several locations up to 500 km away from the release point are directly compared. Typically, the calculated results from the two codes are in reasonable agreement. However, for specific locations and directions, the NACAC predictions of the concentrations and total effective dose rate are lower than those from the JRODOS. These differences may be influenced by the different trajectory calculations between the two codes, which resulted in varying plume densities in certain areas. However, it does not significantly affect the radiation effect assessment. The NACAC still provides a total effective dose rate corresponding to the JRODOS, lower than one mSv throughout the main dispersion direction.

916. Increased efficiency and impact of implementing ILUM insight within an antimicrobial stewardship program (ASP) at an academic medical center

Abstract Background An ASP is mandated for all hospitals and requires extensive resources with multidisciplinary collaboration. We measured the impact of implementing real-time decision support software (ILUM Insight) within our ASP. Methods Our ASP has relied on prior authorization since 2002 and focused audit and feedback since 2015. In August 2021 we implemented to bring actionable data to front-line stewards. ILUM provides real-time notifications, organizes communications, and tracks patient-and provider-level data. We hypothesized that ILUM would increase the efficiency of ASP workflow and result in decreased antimicrobial utilization. We compared data 6 months before (8/20 – 1/21) and after (8/21 – 1/22) implementation. There were no significant staffing changes during either period. Results Existing notifications within ILUM were tailored to local practices, including alerts with intervention for positive blood cultures, antibiotic de-escalation, and bug-drug mismatches. New notifications were built for restricted antimicrobials, antibiotic timeouts, and MRSA screening. ASP pharmacists and physicians received training in July and November, respectively. A breakdown of all notifications received during the post-implementation period is provided in Fig 1. With increased ILUM usage, the number of interventions made by our ASP increased while missed opportunities decreased (Fig 2.). During the same time period, ASP communications rose from 205 to 1200 per month. Comparing pre- and post-implementation periods, antimicrobial days of therapy (DOT) per 1,000 patient days (PD) decreased by 14.5% from a median of 969 to 846 per month (Fig 3;P=0.002). Antimicrobial expenditures were decreased by a median 21% per month during the post-intervention period compared to baseline. Among patients prescribed antimicrobials during an index admission, 30-day re-admissions decreased from 330 to 262 and re-admissions associated with re-ordering of antimicrobials decreased from 235 to 182 (Fig 4). Conclusion Custom-designed, task-specific software improves the efficiency of daily ASP workflow and significantly decreased antimicrobial utilization without the need for additional ASP team members. Disclosures Ryan K. Shields, PharmD, MS, Infectious Disease Connect: Advisor/Consultant|Merck: Advisor/Consultant|Merck: Grant/Research Support|Roche: Grant/Research Support J Ryan Bariola, MD, Infectious Disease Connect: Salary support|Merck: Grant/Research Support Caley Yakemowicz, n/a, Infectious Disease Connect: Employee Courtney Simonick, n/a, Infectious Disease Connect: Stocks/Bonds Riaan Erwee, na, Infectious Disease Connect: Employee Erin K. McCreary, PharmD, Infectious Disease Connect: Advisor/Consultant Rima Abdel-Massih, MD, Infectious Disease Connect: Co founder and Chief Medical Officer|Infectious Disease Connect: Ownership Interest.

An Accessible Clinical Decision Support System to Curtail Anesthetic Greenhouse Gases in a Large Health Network: Implementation Study.

Inhaled anesthetics in the operating room are potent greenhouse gases and are a key contributor to carbon emissions from health care facilities. Real-time clinical decision support (CDS) systems lower anesthetic gas waste by prompting anesthesia professionals to reduce fresh gas flow (FGF) when a set threshold is exceeded. However, previous CDS systems have relied on proprietary or highly customized anesthesia information management systems, significantly reducing other institutions' accessibility to the technology and thus limiting overall environmental benefit. In 2018, a CDS system that lowers anesthetic gas waste using methods that can be easily adopted by other institutions was developed at the University of California San Francisco (UCSF). This study aims to facilitate wider uptake of our CDS system and further reduce gas waste by describing the implementation of the FGF CDS toolkit at UCSF and the subsequent implementation at other medical campuses within the University of California Health network. We developed a noninterruptive active CDS system to alert anesthesia professionals when FGF rates exceeded 0.7 L per minute for common volatile anesthetics. The implementation process at UCSF was documented and assembled into an informational toolkit to aid in the integration of the CDS system at other health care institutions. Before implementation, presentation-based education initiatives were used to disseminate information regarding the safety of low FGF use and its relationship to environmental sustainability. Our FGF CDS toolkit consisted of 4 main components for implementation: sustainability-focused education of anesthesia professionals, hardware integration of the CDS technology, software build of the CDS system, and data reporting of measured outcomes. The FGF CDS system was successfully deployed at 5 University of California Health network campuses. Four of the institutions are independent from the institution that created the CDS system. The CDS system was deployed at each facility using the FGF CDS toolkit, which describes the main components of the technology and implementation. Each campus made modifications to the CDS tool to best suit their institution, emphasizing the versatility and adoptability of the technology and implementation framework. It has previously been shown that the FGF CDS system reduces anesthetic gas waste, leading to environmental and fiscal benefits. Here, we demonstrate that the CDS system can be transferred to other medical facilities using our toolkit for implementation, making the technology and associated benefits globally accessible to advance mitigation of health care-related emissions.

Land use suitability assessment for economic development at the provincial level: The case study of Yazd Province, Iran

• The concept of land use suitability (LUS) is intended to help stakeholders make better land-use decisions on a regional scale. • GIS application with a hybrid Multi-Criteria Decision Analysis (MCDA)-Rule-Based System (RBS) in LUS is recommended. • This study suggests the real-time Spatial Decision Support System (SDSS) in LUS analysis. • SDSS should be used in places where suitable geodatabases are not available. Iran's Yazd Province has expanded its economy during the past 40 years due to requests from investors and decisions made by the Provincial Planning and Development Council. This article aims to aid the council's decision-making process by presenting an analytical method for determining whether a region is suitable, unsuitable, or conditionally suitable for generating various economic activities in the form of individual suitability maps. Using a hybrid Multi-Criteria Decision Analysis (MCDA)-Rule Based System (RBS), the method is made up of the Analytic Hierarchy Process (AHP) and semi/non-compensatory decision rules in combination with GIS spatial analysis. The findings show that the province has exceeded its environmental carrying capabilities since a significant portion of its overall land area is unsuitable for most industrial and agricultural activities. The Provincial Planning and Development Council seeks to have even fewer options than to concentrate on high-tech and service sector development along major and dense corridors while relocating other economic activity to the periphery to address issues with water scarcity, air pollution, uneven infrastructure distribution, and environmental limitations. The proposed model of this study can be utilized as a real-time Spatial Decision Support System (SDSS) for the Yazd Provincial Planning and Development Council. In sequential decision-making processes, the model can examine the effects of previous decisions and adjust its findings/conclusions as needed.

A Real-Time Decision Support Approach for Managing Disruptions in Line-Haul Freight Transport Networks

Unexpected disruptions in road freight transport caused by poor weather conditions, traffic accidents, etc., are quite frequent and have negative effects on the whole supply chain. Therefore, an intelligent disruption management system is necessary to revise the transport plan directly after disruptions have occurred. The literature presents several approaches for managing disruptions in road freight transport. However, most of them focus on urban freight distributions where disruptions are often handled by vehicle rerouting. The current work, in contrast, addresses disruption management in a line-haul freight transport network that connects urban distribution systems. We present a novel hybrid approach combining a simulation model, optimization algorithms, and a cost-effectiveness analysis. When a disruption occurs, the proposed approach can be used to analyze the impacts of the disruption, identify the affected trips, and revise their plan quickly in real time. Six re-planning strategies are proposed to handle the disruptions and are evaluated in terms of cost, reliability (expressed in time delays), and CO2 emissions. Cost-effectiveness analyses are conducted to rank the obtained solutions and identify the best strategy. Moreover, we suggest a decision support system architecture, based on the proposed approach, to enable disruption management in real-time settings. Real data is used to evaluate the proposed approach in different disruption scenarios. The results provide transport planners with useful insights into possible re-planning strategies and how to identify the best cost-effective strategy to minimize the disruptions’ effects and be more economically sustainable. This work also supports carriers in the transition towards intelligent disruption management.

An Outlook of Digital Twins in Offensive Military Cyber Operations

The outlook of military cyber operations is changing due to the prospects of data generation and accessibility, continuous technological advancements and their (public) availability, technological and human (inter)connections increase, plus the dynamism, needs, diverse nature, perspectives, and skills of experts involved in their planning, execution, and assessment phases respecting (inter)national aims, demands, and trends. Such operations are daily conducted and recently empowered by AI to reach or protect their targets and deal with the unintended effects produced through their engagement on them and/or collateral entities. However, these operations are governed and surrounded by different uncertainty levels e.g., intended effects prediction, consideration of effective alternatives, and understanding new dimensions of possible (strategic) future(s). Hence, the legality and ethicality of such operations should be assured; particularly, in Offensive Military Cyber Operations (OMCO), the agents involved in their design/deployment should consider, develop, and propose proper (intelligent) measures/methods. Such mechanisms can be built embedding intelligent techniques based on hardware, software, and communication data plus expert-knowledge through novel systems like digital twins. While digital twins find themselves in their infancy in military, cyber, and AI academic research and discourses, they started to show their modelling and simulation potential and effective real-time decision support in different industry applications. Nevertheless, this research aims to (i) understand what digital twins mean in OMCO context while embedding explainable AI and responsible AI perspectives, and (ii) capture challenges and benefits of their development. Accordingly, a multidisciplinary stance is considered through extensive review in the domains involved packaged in a design framework meant to assist the agents involved in their development and deployment.

An Outlook of Digital Twins in Offensive Military Cyber Operations

The outlook of military cyber operations is changing due to the prospects of data generation and accessibility, continuous technological advancements and their (public) availability, technological and human (inter)connections increase, plus the dynamism, needs, diverse nature, perspectives, and skills of experts involved in their planning, execution, and assessment phases respecting (inter)national aims, demands, and trends. Such operations are daily conducted and recently empowered by AI to reach or protect their targets and deal with the unintended effects produced through their engagement on them and/or collateral entities. However, these operations are governed and surrounded by different uncertainty levels e.g., intended effects prediction, consideration of effective alternatives, and understanding new dimensions of possible (strategic) future(s). Hence, the legality and ethicality of such operations should be assured; particularly, in Offensive Military Cyber Operations (OMCO), the agents involved in their design/deployment should consider, develop, and propose proper (intelligent) measures/methods. Such mechanisms can be built embedding intelligent techniques based on hardware, software, and communication data plus expert-knowledge through novel systems like digital twins. While digital twins find themselves in their infancy in military, cyber, and AI academic research and discourses, they started to show their modelling and simulation potential and effective real-time decision support in different industry applications. Nevertheless, this research aims to (i) understand what digital twins mean in OMCO context while embedding explainable AI and responsible AI perspectives, and (ii) capture challenges and benefits of their development. Accordingly, a multidisciplinary stance is considered through extensive review in the domains involved packaged in a design framework meant to assist the agents involved in their development and deployment.

Making Complex Cancer Reports Easier Using Self-Built Interactive Templates

Abstract Surgical pathology reports are becoming increasingly complex, especially with neoplastic cases. Pathologists need to verify all elements are addressed in the CAP synoptics, order proper reflex molecular testing or immunohistochemistry depending on care standards and institutional guidelines, and address key points in the report for clinicians. For example, breast cancer cases at our institution need repeat biomarkers for all cases status post neoadjuvant therapy, oncotype reflex testing for pT2N0, estrogen receptor positive tumors, and tumor bed size with residual tumor burden calculations for all breast cancer only after neoadjuvant chemotherapy. For residents and faculty alike, these expansive reports are challenging, especially for those new to the institution’s policies and conventions. Previous workflow included using Microsoft Word documents with previously signed out cases had significant risk for errors that stem from new updates in the CAP protocols, missing or different reflex testing from the previous case, and updated institutional guidelines. We set out to create a customized library of decision support and interactive templates using a software called PhraseExpander. We created PhraseExpander templates for all cancer resections. The templates included the associated synoptics required for accreditation by CAP, implemented automated staging using logic statements, and provided conditional reminders when reflex testing was indicated. Variable labels were utilized to help clarify notes in the CAP synoptic such as how to grade specific tumors and how many high-power fields to count. The tools were distributed to all surgical pathology fellows, residents, and select faculty who expressed interest in participating. The templates were successfully implemented into the routine workflow for surgical pathology sign out. End-user feedback was overwhelmingly positive. First year and upper level residents appreciate the detailed descriptions on how to complete the report, most notably the reflex molecular testing reminders and many conditional aspects such as in our breast cancer reports. Fellows and attendings have also appreciated these aspects as well as the automated staging and reminders to do administrative tasks, such as adding the case to the Laboratory Information System tumor registry. Interactive templates with built in conditions and calculators are powerful tools that can be used for generating the increasingly complex reports in pathology. They provide pathologists real-time and non-interruptive clinical decision support, allow them to spend less time typing and reduce reliance on remembering the required reflex testing. The most notable disadvantage in coding these complex templates is the considerable build and maintenance time. We plan to expand use to flow cytometry reports, biopsy reports, and routine non-neoplastic cases while incorporating labels to assist in resident education.

Internet of Things based Decision Support System for Green Logistics

This work proposes an IoT-based Real-Time Decision Support System for Perishable Products. The proposed system collects data during the transportation process and will interfere in the case of failure. Three different simulation models corresponding to different configurations and mitigation plans are built. The simulation models consider decisions such as stopping transportation and rerouting shipments to minimize losses in case of failure. The three different supply chain simulation models are implemented through a case study that considers transporting a perishable fruit in the intercontinental United States. A financial and environmental analysis is conducted to show the benefits of the proposed system.

Building an Augmented Reality Experience on Top of a Smart Pavement Management System

Pavement Management Systems (PMS) offers a systematic collection, storage, analysis, and modeling of road condition data to optimize resources across a road network. Adding artificial intelligence (AI) and augmented reality (AR) to PMS could improve their technical or visual aspects. This paper tries to identify a method to improve the understanding of the consequences of the city council’s decisions in the urban pavement management system field. This paper establishes the potential of AR. It provides future maintenance and rehabilitation (M&R) actions needed based on the recommendation of the future distress in the study area. The road cracks are discovered through technical analysis, and a CityEngine model is established based on the PMS results. Additionally, in terms of visualization, this paper’s unique feature delivers the result as an AR experience. Applying the Unity game engine and importing the built CityEngine model and the embedded textures as input empowered us to provide a dynamic product in terms of data and analysis and a real-time Decision Support System (DSS) for the final users. This paper concludes that researchers need many different modules to design and implement an efficient PMS to move toward a smart PMS. The smart city concept is meaningless without a tight collaboration between all distinctive parts of each urban infrastructure management system. Additionally, this paper attempts to provide answers for researchers and an outlook for future research, the development of the proposed method, and its application in other fields

Artificial Intelligence Vision Based Analysis and Key Steps Identification Feasibility Study in Laparoscopic Hysterectomies

<h3>Study Objective</h3> The analysis of surgical videos and key steps identification using artificial intelligence (AI) holds great promise for the future of healthcare. In this paper, we present a novel computer vision algorithm for surgical steps identification in laparoscopic hysterectomy. <h3>Design</h3> A retrospective analysis of surgical videos of laparoscopic hysterectomies. <h3>Setting</h3> Gynecological department in a tertiary hospital with an average of 6500 procedures a year. <h3>Patients or Participants</h3> 190 laparoscopic hysterectomies from September 2020 to April 2022. <h3>Interventions</h3> Artificial intelligence-driven surgical platform that uses advanced computer vision technology to capture video data during all surgeries, de-identify it, and upload it to a secure cloud infrastructure. <h3>Measurements and Main Results</h3> A total of 190 full-length hysterectomies videos were manually annotated with sequential steps of surgery. Of these, 115 cases served as a training dataset for algorithm development, 28 cases were used for internal validation, and 47 were used as a separate testing cohort for evaluating algorithm accuracy. Concordance between AI-enabled automated video analysis and manual human video annotation was 92%. Algorithm accuracy was highest for the vaginal vault closure step (97%) and lowest for the adhesiolysis step (71%). <h3>Conclusion</h3> A variety of AI applications are expanding in clinical systems, from databases to intraoperative video analysis. The unique nature of surgical practice puts surgeons in a strong position to contribute to the next phase of AI, focused on real-time clinical decision support designed to optimize surgeon workflow and patient care. This work validates the ability to annotate the different steps of hysterectomy using an AI vision-based algorithm. Automated surgical video analysis has immediate impact in this field and the implementation of such a system holds enormous promise for future surgical education and improvement.

A time-aware attention model for prediction of acute kidney injury after pediatric cardiac surgery.

Acute kidney injury (AKI) is a common complication after pediatric cardiac surgery, and the early detection of AKI may allow for timely preventive or therapeutic measures. However, current AKI prediction researches pay less attention to time information among time-series clinical data and model building strategies that meet complex clinical application scenario. This study aims to develop and validate a model for predicting postoperative AKI that operates sequentially over individual time-series clinical data. A retrospective cohort of 3386 pediatric patients extracted from PIC database was used for training, calibrating, and testing purposes. A time-aware deep learning model was developed and evaluated from 3 clinical perspectives that use different data collection windows and prediction windows to answer different AKI prediction questions encountered in clinical practice. We compared our model with existing state-of-the-art models from 3 clinical perspectives using the area under the receiver operating characteristic curve (ROC AUC) and the area under the precision-recall curve (PR AUC). Our proposed model significantly outperformed the existing state-of-the-art models with an improved average performance for any AKI prediction from the 3 evaluation perspectives. This model predicted 91% of all AKI episodes using data collected at 24h after surgery, resulting in a ROC AUC of 0.908 and a PR AUC of 0.898. On average, our model predicted 83% of all AKI episodes that occurred within the different time windows in the 3 evaluation perspectives. The calibration performance of the proposed model was substantially higher than the existing state-of-the-art models. This study showed that a deep learning model can accurately predict postoperative AKI using perioperative time-series data. It has the potential to be integrated into real-time clinical decision support systems to support postoperative care planning.

Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support.

This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; (ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis; (iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; (iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences.

Bayesian Non-Parametric Thermal Thresholds for Helicoverpa armigera and Their Integration into a Digital Plant Protection System

The development of temperature-driven pest risk thresholds is a prerequisite for the buildup and implementation of smart plant protection solutions. However, the challenge is to convert short and abrupt phenology data with limited distributional information into ecological relevant information. In this work, we present a novel approach to analyze phenology data based on non-parametric Bayesian methods and develop degree-day (DD) risk thresholds for Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) to be used in a decision support system for dry bean (Phaseolus vulgaris L.) production. The replication of each Bayesian bootstrap generates a posterior probability for each sampling set by considering that the prior unknown distribution of pest phenology is Dirichlet distribution. We computed R = 10,000 temperature-driven pest phenology replicates, to estimate the 2.5%, 50% and 95.5% percentiles (PC) of each flight generation peak in terms of heat summations. The related DD thresholds were: 114.04 (PC 2.5%) 131.8 (PC 50%) and 150.9 (PC 95.5%) for the first, 525.8 (PC 2.5%), 551.7 (PC 50%) and 577.6 (PC 95.5%) for the second and 992.7 (PC 2.5%), 1021.5 (PC 50%) and 1050 (PC 95.5%) for the third flight, respectively. The thresholds were evaluated by estimating the posterior differences between the predicted (2021) and observed (2022) phenology metrics and are in most cases in acceptable levels. The bootstrapped Bayesian risk thresholds have the advantage to be used in modeling short and noisy data sets providing tailored pest forecast without any parametric assumptions. In a second step the above thresholds were integrated to a sub-module of a digital weather-driven real time decision support system for precise pest management for dry bean crops. The system consists of a customized cloud based telemetric meteorological network, established over the border area of the Prespa National Park in Northern Greece, and delivers real time data and pest specific forecast to the end user.

Deep learning for real-time auxiliary diagnosis of pancreatic cancer in endoscopic ultrasonography.

In recent year, many deep learning have been playing an important role in the detection of cancers. This study aimed to real-timely differentiate a pancreatic cancer (PC) or a non-pancreatic cancer (NPC) lesion via endoscopic ultrasonography (EUS) image. A total of 1213 EUS images from 157 patients (99 male, 58 female) with pancreatic disease were used for training, validation and test groups. Before model training, regions of interest (ROIs) were manually drawn to mark the PC and NPC lesions using Labelimage software. Yolov5m was used as the algorithm model to automatically distinguish the presence of pancreatic lesion. After training the model based on EUS images using YOLOv5, the parameters achieved convergence within 300 rounds (GIoU Loss: 0.01532, Objectness Loss: 0.01247, precision: 0.713 and recall: 0.825). For the validation group, the mAP0.5 was 0.831, and mAP@.5:.95 was 0.512. In addition, the receiver operating characteristic (ROC) curve analysis showed this model seemed to have a trend of more AUC of 0.85 (0.665 to 0.956) than the area under the curve (AUC) of 0.838 (0.65 to 0.949) generated by physicians using EUS detection without puncture, although pairwise comparison of ROC curves showed that the AUC between the two groups was not significant (z= 0.15, p = 0.8804). This study suggested that the YOLOv5m would generate attractive results and allow for the real-time decision support for distinction of a PC or a NPC lesion.

What drives performance in machine learning models for predicting heart failure outcome?

The development of acute heart failure (AHF) is a critical decision point in the natural history of the disease and carries a dismal prognosis. The lack of appropriate risk-stratification tools at hospital discharge of AHF patients significantly limits clinical ability to precisely tailor patient-specific therapeutic regimen at this pivotal juncture. Machine learning-based strategies may improve risk stratification by incorporating analysis of high-dimensional patient data with multiple covariates and novel prediction methodologies. In the current study, we aimed at evaluating the drivers for success in prediction models and establishing an institute-tailored artificial Intelligence-based prediction model for real-time decision support. We used a cohort of all 10 868 patients AHF patients admitted to a tertiary hospital during a 12 years period. A total of 372 covariates were collected from admission to the end of the hospitalization. We assessed model performance across two axes: (i) type of prediction method and (ii) type and number of covariates. The primary outcome was 1-year survival from hospital discharge. For the model-type axis, we experimented with seven different methods: logistic regression (LR) with either L1 or L2 regularization, random forest (RF), Cox proportional hazards model (Cox), extreme gradient boosting (XGBoost), a deep neural-net (NeuralNet) and an ensemble classifier of all the above methods. We were able to achieve an area under receiver operator curve (AUROC) prediction accuracy of more than 80% with most prediction models including L1/L2-LR (80.4%/80.3%), Cox (80.2%), XGBoost (80.5%), NeuralNet (80.4%). RF was inferior to other methods (78.8%), and the ensemble model was slightly superior (81.2%). The number of covariates was a significant modifier (P < 0.001) of prediction success, the use of multiplex-covariates preformed significantly better (AUROC 80.4% for L1-LR) compared with a set of known clinical covariates (AUROC 77.8%). Demographics followed by lab-tests and administrative data resulted in the largest gain in model performance. The choice of the predictive modelling method is secondary to the multiplicity and type of covariates for predicting AHF prognosis. The application of a structured data pre-processing combined with the use of multiple-covariates results in an accurate, institute-tailored risk prediction in AHF.

Information Systems Research for Smart Sustainable Mobility: A Framework and Call for Action

Transportation is a backbone of modern globalized societies. It also causes approximately one third of all European Union and U.S. greenhouse gas emissions, represents a major health hazard for global populations, and poses significant economic costs. However, rapid innovation in vehicle technology, mobile connectivity, computing hardware, and artificial intelligence (AI)-powered information systems heralds a deep socio-technical transformation of the sector. The emergence of connected, autonomous, shared, and electric (CASE) vehicle technology has created a digital layer that resides on top of the traditional physical mobility system. This article contributes a framework to direct research and practice toward leveraging the opportunities afforded by CASE for a more efficient and less environmentally problematic mobility system. The authors propose seven overarching dimensions of action. These range from designing real-time digital coordination mechanisms for the management of mobility systems to developing AI-powered real-time decision support for mobility resource planning and operations. Per each dimension, concrete angles of attack are suggested which, we hope, will spur structured engagement from both researchers and practitioners in the field.

Automatic verification of urban index compliance: A case study for Brazilian buildings

Urban planning has become an essential tool for regulating the cities’ growth, maintaining the local urban identity and providing a good quality of life for its inhabitants. In Brazil, the master plan is the primary policy instrument for urban development and expansion. It defines the common requirements that designers must follow when preparing building projects. Up to date, such projects are verified in project latter stages and eventually approved by the city halls, with a manual calculation and assessment process. This procedure creates both the need to assess project compliance earlier to avoid rework and changes, as well as to automate project verification compliance, to reduce human errors. With the emergence of new technologies and computational systems, such as Building Information Modelling (BIM), creates the opportunity for process automation, by providing the required data to support decision making, as well as to automate the calculation process both for designers and municipalities. Thus, this research aims to develop an assessment procedure which automates the compliance assessment of a set of urban requirements from Vila Velha, Brazil. A BIM model was developed in Autodesk Revit to prove the procedure functionality and Dynamo was used to automate data collection and the calculation of three different indexes from Vila Velha’s building code. By providing a fast and reliable analysis, the research framework provides designers with a real-time decision support tool, which indicates building compliance in the project’s early stages. It also provides municipalities with a calculation tool, which can be used to assess the compliance of submitted BIM models, sparing time and avoiding assessment errors. Overall, the procedure has the potential to support building project design, increase process efficiency and accelerate the verification of mandatory requirements. It also offers the possibility for replication by adapting the routine to the mandatory requirements of each location.

FPGA Implementation of Expert System for Medical Diagnosis of Disc Hernia Diagnosis Based on Bayes Theorem

The aim of this research is to create a medical expert system based on Bayes theorem to diagnose level of disc hernia based on real foot force measurement signals obtained using sensors and implement the whole system on field programable gate array (FPGA). We have created a database of attributes based on recorded foot force values of 33 patients pre-diagnosed with herniated disc on levels L4/L5 or L5/S1 on the left or right side. The results obtained by software (Matlab) and hardware (FPGA simulation) are matching well, achieving high accuracy, which shows that VHDL implementation of Naïve Bayes theorem for disc hernia diagnostics is adequate. The output on FPGA is easy to understand for any user, as it is implemented as four-bit output where the position of bit value 1 indicates the level of disc herniation. The system is able to distinguish between the healthy subjects and subjects with disc herniation and is able to detect if improvement in stability is present after surgery or physical therapy. Our proposed measurement platform can be coupled with FPGA to create a portable and not expensive tool for real time signal acquisition, processing and decision support system in disc hernia diagnosis and post-surgical recovery.