• Digital Twins in Health
• Open Access close

Caso de uso aplicado para un ejemplo real en el que CTN se ha basado para capacitarse en la implementación de algoritmos de minería de procesos sobre un conjunto de datos real del sector sanitario. Concretamente, nos centramos en las diferentes fases por las que atraviesa un paciente cuando realiza una consulta médica, de modo que el conjunto de datos se conforma de un listado de 100 pacientes con sus correspondientes citas médicas con los respectivos doctores y las diferentes pruebas a las que se someten. Estos datos se han recopilado para el proyecto MINEPRAFDT - Mining process & analysis for Digital Twin - Aplicación del Process Mining al modelado y análisis de proceso industriales con una alta componente manual para la creación de su Gemelo Digital, financiado por el Instituto de Fomento de la Región de Murcia con el apoyo de los Fondos FEDER. {"references": ["https://medium.com/@c3_62722/process-mining-with-python-tutorial-a-healthcare-application-part-1-ae02027a050", "https://medium.com/@c3_62722/process-mining-with-python-tutorial-a-healthcare-application-part-3-cc9af986c122", "https://pm4py.fit.fraunhofer.de/", "https://www.futurelearn.com/courses/process-mining-healthcare"]}

Overview This repository contains a comprehensive dataset to assess cognitive states, workload, situational awareness, stress, and performance in human-in-the-loop process control rooms. The dataset includes objective and subjective measures from various data collection tools such as NASA-TLX, SART, eye tracking, EEG, Health Monitoring Watch, surveys, and think-aloud situational awareness assessments. It is based on an experimental study of a formaldehyde production plant based on participants' interactions in a controlled control room experimental setting. Purpose The study compared three different setups of human system interfaces in four human-in-the-loop (HITL) configurations, incorporating two alarm design formats (Prioritised vs non-prioritised) and three procedural guidance setups (e.g. one presenting paper procedures, one offering digitised screen-based procedures, and lastly an AI-based procedural guidance system). Key Features Subject Area: Chemical Engineering, Control and Safety Engineering, Human Factors and Ergonomics, Human-Computer Interaction, and Artificial Intelligence Data Format: Raw, Analyzed, Filtered Type of Data: CSV File (.csv), Matlab File (.mat), Excel (.xlsx), Table Data Collection: The dataset contains behavioural, cognitive, and performance data from 92 participants, including system data under each participant from three scenarios, each simulating a typical control room monitoring, alarm handling, planning, and intervention tasks and subtasks. The participants consented to participate on the test day, after which the researchers trained them. They performed tasks under three scenarios, each lasting 15 - 18 minutes. During these tests, the participant wore a watch for health monitoring, including an eye tracker. They were asked situational awareness questions based on the SPAM methodology at specific periods within 15 minutes, especially at the 6th, 8th, and 12th minutes. These questions assessed the three levels of situational awareness: perception, comprehension, and projection. This feedback collection process on situational awareness differed for one of the groups that used an AI-based decision support system. The question for this group was asked right after specific actions. Therefore, for the overall study, the following performance-shaping factors are considered: type of decision support system (alarm display design, procedure format, AI support, interface design), communication, situational awareness, cognitive workload, experience/training, task complexity, and stress. In both cases, communication was excluded as a factor considered in the first and second scenarios based on this absence. The data collected was normalized using the Min-Max normalization. Potential Applications The dataset provides an opportunity for various applications, including: Developing human performance models and process safety models Developing a digital twin simulating human-machine interaction in process control rooms Optimizing human-AI interaction in safety-critical industries Qualifying and quantifying the performance and effectiveness of AI-enhanced decision support systems incorporating Deep Reinforcement Learning (DRL) using a Specialized Reinforcement Learning Agent (SRLA) framework Validating proposed solutions for the industry Usage The dataset is instrumental for researchers, decision-makers, system engineers, human factor engineers, and teams developing guidelines and standards. It is also applicable for validating proposed solutions for the industry and for researchers in similar or close domains. Data Structure The concatenated Excel file for the dataset may include the following detailed data: Demographic and Educational Background Data: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Age: The age of each participant at the time of the experiment. Gender: The gender of each participant, typically categorized as male, female, or other. Educational Background: Details of participants' academic qualifications, including degree type (e.g., Masters, PhD), year of study, and field of study (e.g., Chemical Engineering, IT). Dominant Hand: Information on whether participants are right or left-handed, which could influence their interaction with the simulation interface. Familiarity with Industry and Control Room: Self-reported familiarity levels with the industry in general and control room environments specifically, on a scale from 1 to 5. SPAM Metrics: Participant Identifier: Unique codes for participants (e.g., P04, P06), maintaining anonymity while allowing for individual analysis. Group Assignment: Indicates the experimental group (e.g., G4, G3, G2, G1) to which participants belonged, reflecting different levels of decision support in the simulation. Scenario Engagement: Identifies the specific scenarios (e.g., S1, S2, S3) each participant encountered, representing diverse challenges within the control room simulation. SPAM Metrics: Participant ratings across three dimensions of the SPAM questionnaire - Perception, Understanding, and Projection, on a scale typically from 1 to 5. SPAM Index: Composite scores derived from the SPAM, indicating overall situation awareness levels experienced by participants. Calculated as the average of the score on perception, understanding and projection. NASA-TLX Responses: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Group Assignment: Indicates the experimental group (e.g., G1) to which participants were assigned, reflecting different levels of decision support in the simulation. TLX Ratings: Participants' responses utilizing the NASA Task Load Index (NASA TLX) questionnaire, providing insights into the cognitive, physical, and emotional workload experienced by operators in simulated control room scenarios. TLX Index: Composite scores derived from the NASA TLX, representing the overall workload experienced by the participant, calculated as an average of the ratings across the six dimensions. SART Data: Participant Identifier: Unique codes for participants (e.g., P04, P06), maintaining anonymity while allowing for individual analysis. Group Assignment: Indicates the experimental group (e.g., G1) to which participants belonged, reflecting different levels of decision support in the simulation. SART Metrics: Participants' responses to the Situation Awareness Rating Technique (SART) questionnaire, capturing metrics reflecting the participants' situation awareness. It is calculated using the equation U - (D - S). Situation Understanding (U) comprises Information Quantity, Information Quality, and Familiarity. Situation demand (D) includes the situation's Instability, Complexity, and Variability. At the same time, the Supply of attentional resources (S) comprises Arousal, Concentration, Division of Attention, and Spare Capacity. AI Decision Support System Feedback: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. AI System Ratings: Participants' feedback and ratings across different aspects of the AI decision support system, such as support, explainability, and trust, providing insights into the system's perceived strengths and areas for improvement. Workload Impact Data: Information on the workload impact and the balance between AI benefits and additional workload, offering valuable perspectives on the practicality and efficiency of integrating AI systems in control room operations. DRL (Deep Reinforcement Learning) Role: Emphasis on the importance of validating AI recommendations and the role of Deep Reinforcement Learning (DRL) in enhancing trust. Performance Metrics: Participant Identifier: A unique alphanumeric code assigned to each participant for anonymity and tracking purposes. Scenario Engagement: Details of the specific scenario (e.g., S1, S2, S3) each participant encountered, representing various challenges in the control room environment. Task-Specific Performance Measures: Data capturing the participants' experiences and performance across different scenarios in a control room simulation, including task-specific performance measures and outcomes related to decision-making processes in safety- critical environments. This detailed breakdown provides a comprehensive view of the specific data elements that could be included in the concatenated Excel file, allowing for thorough analysis and exploration of the participants' experiences, cognitive states, workload, and decision-making processes in control room environments. Citation Please cite this article and dataset if you use this dataset in your research or publication. Amazu, C. W., Mietkiewicz, J., Abbas, A. N., Briwa, H., Perez, A. A., Baldissone, G., ... & Leva, M. C. (2024). Experiment Data: Human-in-the-loop Decision Support in Process Control Rooms. Data in Brief, 110170.

Abstract Background Mitotic rate is routinely assessed in breast cancer cases and based on the assessment of 10 high power fields (HPF), a non-standard sample area, as per the College of American Pathologists cancer checklist. The effect of sample area variation has not been assessed. Methods A computer model making use of the binomial distribution was developed to calculate the misclassification rate in 1,000,000 simulated breast specimens using the extremes of field diameter (FD) and mitotic density cutoffs (3 and 8 mitoses/mm2), and for a sample area of 5 mm2. Mitotic counts were assumed to be a random sampling problem using a mitotic rate distribution derived from an experimental study (range 0–16.4 mitoses/mm2). The cellular density was 2500 cell/mm2. Results For the smallest microscopes (FD = 0.40 mm, area 1.26 mm2) 16% of cases were misclassified, compared to 9% of the largest (FD 0.69 mm, area 3.74 mm2), versus 8% for 5 mm2. An excess of 27% of score 2 cases were misclassified as 1 or 3 for the lower FD. Conclusion Mitotic scores based on ten HPFs of a small field area microscope are less reliable measures of the mitotic density than in a bigger field area microscope; therefore, the sample area should be standardized. When mitotic counts are close to the cut-offs the score is less reproducible. These cases could benefit from using larger sample areas. A measure of mitotic density variation due to sampling may assist in the interpretation of the mitotic score.

Additional file 17. GWAS enrichment results and results from pathway analysis of those GWAS genes, for lesional and non-lesional DEGs and URs, in AD, UC, and CD.

End-stage renal disease (ESRD) is a major and growing healthcare problem associated with substantial costs. By the end of 2010 the global patient population requiring chronic renal replacement therapy (RRT) exceeds 2 million, of which approximately 90% relies on hemodialysis (HD). The number of patients dependent on RRT are expected to annually increase with 8%. Based on this figure, it is expected that in 2030, 7.3 million ESRD patients need HD treatment. To facilitate adequate HD therapy a reliable vascular access (VA) is mandatory and can be provided by either creation of an autologous arteriovenous fistula (AVF), a prosthetic arteriovenous graft (AVG) or a central venous catheter (CVC). Guidelines by the National Kidney Foundation (NKF K/DOQI Guidelines), the Vascular Access Society (Good Nephrological Practice Guidelines) and the European Dialysis and Transplant Association (European Best Practice Guidelines on vascular access) advocate the implementation of an all-autologous fistula policy to maximize the use of AVF over AVG and CVC because AVF have the best long-term patency, fewer complications and require less interventions once fully maturated. Although the implementation of preoperative ultrasonography examination for vessel assessment has reduced the number of early AVF failure by improving the selection of the most suitable vessels and site for AVF creation, short- and long-term AVF dysfunction remains the major cause of morbidity and hospitalisation in HD patients, and is therefore the major limitation for HD treatment. This dysfunction is usually associated with non-maturation of the newly created AVF or the formation of neo-intimal hyperplasia (NIH) which potentially results in decreased access flow and eventual fistula thrombosis in up to 50% of AVFs. On the other hand, the low resistance vascular traject via the AVF may lead to impeded perfusion of the extremity distally of the AV anastomosis resulting in hand ischemia (HAIDI = Hemodialysis Access Induced Distal Ischemia), while an abundant AVF flow may lead to the development of left ventricular hypertrophy, both with potentially severe consequences. These high-flow complications occur in approximately 20% of fistulae. Numerous studies have investigated alternative preoperative mapping tools and criteria for reduction of AVF related complications. However, current clinical use of these individual tools and parameters does not take into account their potential interplay at a systemic level. Therefore one might consider that multiple prognostic parameters within a single patient are likely more valuable to improve outcome and therefore it is obvious to tailor the type of AVF to the individual patient. A possible solution to deal with multiple independent prognostic factors is implementation of a predictive patient-specific computational tool that relates geometrical, mechanical and hemodynamical parameters by means of physical laws. As a result, the computational tool takes the complex interplay between different prognostic parameters into consideration and accounts for individual differences in anatomy, physiology, demography and hemodynamics. Such an innovative computational tool opens new opportunities. By predicting postoperative flow abovementioned deleterious events can possibly be prevented. High-flow (>1500ml/min) and low-flow (<600ml/min) vascular access can then be predicted and consequently be rejected and a more suitable AVF-configuration chosen. Consequently, simulation of outcome after AVF creation is at hand. Recently, the feasibility of VA computational simulation has been investigated and proven in the ARCH FP7 ICT-224390 project (ARCH; patient-specific image-based computational modeling for improvement of short- and long- term outcome of vascular access in patients on hemodialysis therapy). Within this technological and clinical study, longitudinal collection of cardiovascular data was performed with the intention to develop, calibrate and validate patient-specific modelling tools for surgical planning and assistance in the management of complications arising from AVF creation. Given the difficult and heterogeneous patient population, the study protocol was designed in such way that pre- and postoperative imaging could be performed strictly, aiming at complete datasets of structural, functional and demographical data. Although the computational simulation model has been validated in a small patient group, larger randomized observational patient studies, aiming at evaluating the potential beneficial effect of the use of computational tools in reducing AVF-related clinical problems, are needed. Patients suffering from end-stage renal disease (ESRD) are dependent on renal replacement therapy (dialysis). The majority of dialysis is facilitated by hemodialysis. For hemodialysis a vascular access is necessary, preferable an arteriovenous fistula (AVF) in which a vein is directly anastomosed to an artery. In order to use the AVF for hemodialysis three criteria have to be met; the minimal flow over the AVF is 600 mL/min, the diameter is at least 6 mm, and the AVF is located less than 6 mm under the skin. Unfortunately, approximately half of the patients (50%) are confronted with an AVF that does not meet these criteria; the so called non-maturation or primary failure. In case of non-maturation the AVF is not only unusable for dialysis, but also requires reinterventions on short- and long-term. Firstly to mature the AVF, and secondly, when the AVF is matured, to keep the vascular access. Using a computational simulation postoperative flow can be predicted. Based on patient-specific duplex measurements, the model can calculate the flow that can be expected following vascular access surgery for all AVF configurations; fore- or upper arm. These calculations lead to an advice which configuration is indicated; a flow that exceeds 600 mL/min, leading to maturation. Potentially the aforementioned 50% of non-maturation can be reduced. The patient then has an adequate vascular access and reinterventions are adverted, resulting in a decrease of costs, hospital demand, and an increase of the patients' quality of life. When the expected reduction of non-maturation is confirmed, the computational tool can be offered to other hospitals.

Additional file 3. GNU octave computer code â get_sample_mitotic_score.mâ .

Additional file 8. Prioritization of all identified URs. The z score indicates the activation state of an upstream regulator. The farther the activation z score is from zero, the more likely it is that the direction of differential expression of the target genes is consistent with the regulator being in either an “activated” or an “inhibited” state. The URs (columns) are presented in ranked order, from left to rights (decreasing), based on the number of cell types that each UR is predicted to regulate at different time points (|z-score| ≥ 2 and P-value < 0.05). The data is illustrated in Fig. 7D.

Abstract The metaverse is a virtual world that is being developed to allow people to interact with each other and with digital objects in a more immersive way. It involves the convergence of three major technological trends: telepresence, the digital twin, and blockchain. Telepresence is the ability of people to “be together” in a virtual way while not being close to each other. The digital twin is a virtual, digital equivalent of a patient, a medical device or even a hospital. Blockchain can be used by patients to keep their personal medical records secure. In medicine and healthcare, the metaverse could be used in several ways: (1) virtual medical consultations; (2) medical education and training; (3) patient education; (4) medical research; (5) drug development; (6) therapy and support; (7) laboratory medicine. The metaverse has the potential to enable more personalized, efficient, and accessible healthcare, improving patient outcomes and reducing healthcare costs. However, the implementation of the metaverse in medicine and healthcare will require careful consideration of ethical and privacy concerns, as well as social, technical and regulatory challenges. Overall, the future of the metaverse in healthcare looks bright, but new metaverse-specific laws should be created to help overcome any potential downsides.

AbstractAlthough there is much discussion exploring the potential causes of plagiarism, there is limited research available which provides evidence as to the academic interventions which may help reduce this. This paper discusses a bespoke English for Academic Purposes (EAP) programme introduced at the university level, aimed at improving the academic writing standards of students, reducing plagiarism, and detecting cases of contract cheating. Results from 12 semesters of academic misconduct data (n = 12,937) demonstrate a 37.01% reduction in instances of detected plagiarism following the intervention, but due to limited data, cannot demonstrate a direct impact on reducing detected rates of contract cheating. The results also show a lower than expected proportion of plagiarised assignments (3.46%) among submissions.