Design Science Research Research Articles

Editorial: Continuing on an Inclusive Path to Scholarly Excellence with Renewed Vigor

Editorial: Continuing on an Inclusive Path to Scholarly Excellence with Renewed Vigor

A Decentralized Marketplace for Patient-Generated Health Data: Design Science Approach.

Wearable devices have limited ability to store and process such data. Currently, individual users or data aggregators are unable to monetize or contribute such data to wider analytics use cases. When combined with clinical health data, such data can improve the predictive power of data-driven analytics and can proffer many benefits to improve the quality of care. We propose and provide a marketplace mechanism to make these data available while benefiting data providers. We aimed to propose the concept of a decentralized marketplace for patient-generated health data that can improve provenance, data accuracy, security, and privacy. Using a proof-of-concept prototype with an interplanetary file system (IPFS) and Ethereum smart contracts, we aimed to demonstrate decentralized marketplace functionality with the blockchain. We also aimed to illustrate and demonstrate the benefits of such a marketplace. We used a design science research methodology to define and prototype our decentralized marketplace and used the Ethereum blockchain, solidity smart-contract programming language, the web3.js library, and node.js with the MetaMask application to prototype our system. We designed and implemented a prototype of a decentralized health care marketplace catering to health data. We used an IPFS to store data, provide an encryption scheme for the data, and provide smart contracts to communicate with users on the Ethereum blockchain. We met the design goals we set out to accomplish in this study. A decentralized marketplace for trading patient-generated health data can be created using smart-contract technology and IPFS-based data storage. Such a marketplace can improve quality, availability, and provenance and satisfy data privacy, access, auditability, and security needs for such data when compared with centralized systems.

Public Health Framework for Smart Cities within the Comprehensive Approach to Sustainability in Europe: Case Study of Diabetes

European cities should address the climate change challenges, improving quality of life and reducing costs. They need potential smart and digital approaches. Public health (PH) has recognized climate change as a major challenge. The development of urban policies should be guided by evidence-based PH practices. The environmental health determinants and the climate crisis now represent a clear PH threat. The core of the Smart City is sustainability, and its basic condition is active PH. The inclusion of public health into the pillars of the Smart City concept to contribute toward mitigating PH crises, such as the COVID-19 pandemic, is a framework for action. Design Science Research Methodology (DSRM) is used to elicit a Smart Public Health City (SPHEC) framework. A set of PH and smart city experts participated in the DSRM process, using diabetes as a case study. The European Green Deal served as a blueprint for this transformational change toward a healthier and more sustainable city. The SPHEC framework was defined by elucidating clearly the several dimensions of the PH functions within a digital city, via the identification of a set of digital PH services that are required to support the SPHEC framework. This allows for an assessment of the actual benefits that are obtained with the digital health services, and provides evidence for guiding decision-making. The role of digital PH services emerges from the analysis of the SPHEC framework, through the development of proper digital health services within the smart city, strengthening capacity and resilience in future climate emergencies, and motivating policy makers to take this challenge more seriously.

Vapor‐Phase Precise‐Synthesis of 2D Inorganic Materials for Optoelectronics

Comprehensive Summary2D materials have attracted intensive attention due to their unique electrical and optical properties associated with their strictly defined low dimensionalities. They provide a wide range of basic building blocks for future electronics and optoelectronics. The chemical vapor deposition (CVD) has been proposed to be efficient to realize the controllable thickness, scalable size, which are necessary for both industrial applications and fundamental researches. Herein, we share our research works to realize the controllable growth of 2D materials. We found that stable growth microenvironment can regulate the growth of 2D materials. Thus, we developed near‐steady source supply, space‐confined, and additive‐assisted passivated growth methods to solve the problem of unstable growth environment caused by uneven source and mass transfer. Then, we developed several strategies to precisely control the parity, separation, and transport of the carriers in 2D materials including fabricating defect‐free interface via van der Waals dielectrics, modulating the parity of carriers via ferroelectric‐field, and the separation of carriers via band engineering. Toward future development, we highlight the opportunities and challenges in this field. What is the most favorite and original chemistry developed in your research group?Additive‐assisted passivated chemical vapor deposition for growing 2D materials such as non‐layered 2D materials and 2D inorganic molecular crystals.How do you get into this specific field? Could you please share some experiences with our readers?I got my introduction to chemical vapor deposition for growing nanomaterials from my undergraduate training. Then I focused on transistors and photodetectors during my postgraduate research in Japan. When I came back, I found that 2D materials are promising for the next generation optoelectronics. Thus, I started to get into this field of synthesis of 2D materials for optoelectronics.What is the most important personality for scientific research?Interest, is the driving force to find novel phenomenon, and persist inscientific research.How do you supervise your students?I prefer to discuss different experimental directions according to each student's interests. In terms of topic selection, I will give them more space for thinking and ask them to discuss with me with their problems. Then, we will choose one or more directions that deserve to be focused. I will follow up the details of their experiments every week. In the meantime, I also coach them how to design research projects for their research, and perform multidisciplinary research in collaboration with others.What are your hobbies?Running.If you have anything else to tell our readers, please feel free to do so.As a scientist, please keep interests and curiosity in your scientific research, as well as persistent.

Optimization of purchasing business process in Moroccan public universities based on COBIT and artificial intelligence techniques

PurposeThe main goal of this research work is the optimization of the purchasing business process in the Moroccan public sector in terms of transparency and budgetary optimization. The authors have worked on the public university as an implementation field.Design/methodology/approachThe design of the research work followed the design science research (DSR) methodology for information systems. DSR is a research paradigm wherein a designer answers questions relevant to human problems through the creation of innovative artifacts, thereby contributing new knowledge to the body of scientific evidence. The authors have adopted a techno-functional approach. The technical part consists of the development of an intelligent recommendation system that supports the choice of optimal information technology (IT) equipment for decision-makers. This intelligent recommendation system relies on a set of functional and business concepts, namely the Moroccan normative laws and Control Objectives for Information and Related Technology's (COBIT) guidelines in information system governance.FindingsThe modeling of business processes in public universities is established using business process model and notation (BPMN) in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature. Implementation of artificial intelligence techniques can bring great value in terms of transparency and fluidity in purchasing business process execution.Research limitations/implicationsBusiness limitations: First, the proposed system was modeled to handle one type products, which are computer-related equipment. Hence, the authors intend to extend the model to other types of products in future works. Conversely, the system proposes optimal purchasing order and assumes that decision makers will rely on this optimal purchasing order to choose between offers. In fact, as a perspective, the authors plan to work on a complete automation of the workflow to also include vendor selection and offer validation. Technical limitations: Natural language processing (NLP) is a widely used sentiment analysis (SA) technique that enabled the authors to validate the proposed system. Even working on samples of datasets, the authors noticed NLP dependency on huge computing power. The authors intend to experiment with learning and knowledge-based SA and assess the' computing power consumption and accuracy of the analysis compared to NLP. Another technical limitation is related to the web scraping technique; in fact, the users' reviews are crucial for the authors' system. To guarantee timeliness and reliable reviews, the system has to look automatically in websites, which confront the authors with the limitations of the web scraping like the permanent changing of website structure and scraping restrictions.Practical implicationsThe modeling of business processes in public universities is established using BPMN in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature.Originality/valueThe adopted techno-functional approach enabled the authors to bring information system governance from a highly abstract level to a practical implementation where the theoretical best practices and guidelines are transformed to a tangible application.

Predictis: an IoT and machine learning-based system to predict risk level of cardio-vascular diseases

BackgroundDespite technological advancement in the field of healthcare, the worldwide burden of illness caused by cardio-vascular diseases (CVDs) is rising, owing mostly to a sharp increase in developing nations that are undergoing fast health transitions. People have been experimenting with techniques to extend their lives since ancient times. Despite this, technology is still a long way from attaining the aim of lowering mortality rates.MethodsFrom methodological perspective, a design Science Research (DSR) approach is adopted in this research. As such, to investigate the current healthcare and interaction systems created for predicting cardiac disease for patients, we first analyzed the body of existing literature. After that, a conceptual framework of the system was designed using the gathered requirements. Based on the conceptual framework, the development of different components of the system was completed. Finally, the evaluation study procedure was developed taking into account the effectiveness, usability and efficiency of the developed system.ResultsTo attain the objectives, we proposed a system consisting of a wearable device and mobile application, which allows the users to know their risk levels of having CVDs in the future. The Internet of Things (IoT) and Machine Learning (ML) techniques were adopted to develop the system that can classify its users into three risk levels (high, moderate and low risk of having CVD) with an F1 score of 80.4% and two risk levels (high and low risk of having CVD) with an F1 score of 91%. The stacking classifier incorporating best-performing ML algorithms was used for predicting the risk levels of the end-users utilizing the UCI Repository dataset.ConclusionThe resultant system allows the users to check and monitor their possibility of having CVD in near future using real-time data. Also, the system was evaluated from the Human-Computer Interaction (HCI) point of view. Thus, the created system offers a promising resolution to the current biomedical sector.Trial RegistrationNot Applicable.

Design, Development, and Evaluation of an Automated Solution for Electronic Information Exchange Between Acute and Long-term Postacute Care Facilities: Design Science Research.

Information exchange is essential for transitioning high-quality care between care settings. Inadequate or delayed information exchange can result in medication errors, missed test results, considerable delays in care, and even readmissions. Unfortunately, long-term and postacute care facilities often lag behind other health care facilities in adopting health information technologies, increasing difficulty in facilitating care transitions through electronic information exchange. The research gap is most evident when considering the implications of the inability to electronically transfer patients' health records between these facilities. This study aimed to design and evaluate an open standards-based interoperability solution that facilitates seamless bidirectional information exchange between acute care and long-term and postacute care facilities using 2 vendor electronic health record (EHR) systems. Using the design science research methodology, we designed an interoperability solution that improves the bidirectional information exchange between acute care and long-term care (LTC) facilities using different EHR systems. Different approaches were applied in the study with a focus on the relevance cycle, including eliciting detailed requirements from stakeholders in the health system who understand the complex data formats, constraints, and workflows associated with transferring patient records between 2 different EHR systems. We performed literature reviews and sought experts in the health care industry from different organizations with a focus on the rigor cycle to identify the components relevant to the interoperability solution. The design cycle focused on iterating between the core activities of implementing and evaluating the proposed artifact. The artifact was evaluated at a health care organization with a combined footprint of acute and postacute care operations using 2 different EHR systems. The resulting interoperability solution offered integrations with source systems and was proven to facilitate bidirectional information exchange for patients transferring between an acute care facility using an Epic EHR system and an LTC facility using a PointClickCare EHR system. This solution serves as a proof of concept for bidirectional data exchange between Epic and PointClickCare for medications, yet the solution is designed to expand to additional data elements such as allergies, problem lists, and diagnoses. Historically, the interoperability topic has centered on hospital-to-hospital data exchange, making it more challenging to evaluate the efficacy of data exchange between other care settings. In acute and LTC settings, there are differences in patients' needs and delivery of care workflows that are distinctly unique. In addition, the health care system's components that offer long-term and acute care in the United States have evolved independently and separately. This study demonstrates that the interoperability solution improves the information exchange between acute and LTC facilities by simplifying data transfer, eliminating manual processes, and reducing data discrepancies using a design science research methodology.

Paper Planes for Teaching Construction Production Systems Based on Lean Tools: Continuous Improvement Cells and 5S

Teaching production systems and Lean tools is a challenge for educators in the construction area, which is highlighted by the difficulty of emulating in the classroom the scenarios that industry professionals will face. Hence, implementing pedagogical games has a high potential to improve construction education processes. However, gaps are observed in the proposal of pedagogical games applicable to teaching construction systems based on Lean tools. Considering this gap, this paper proposes a game of paper planes to support the teaching of production systems in construction based on the Lean tools Continuous Improvement Cells and 5S. The research method consisted of applying the Design Science Research (DSR) method to develop, evaluate, and improve the game proposal. Thus, the game was assumed as an artifact subject to a development and improvement process to solve an identified problem. The proposed game consists of three main rounds: (1) production system design, (2) Continuous Improvement Cells implementation, and (3) 5S implementation. The main theoretical and practical contribution is the proposal of a pedagogical game to support the teaching of construction production systems based on Lean tools, evaluating students’ knowledge by applying surveys before and after the game and assessing performance indicators. The application of the game to a group of undergraduate and graduate students showed a broad positive impact on the assimilation of the principles of construction production systems based on Lean tools.

A design concept of big data analytics model for managers in hospitality industries.

The hospitality and tourism sector has long played a significant role in Australia's economy, especially in regional areas. Due to the onslaught of COVID-19, numerous businesses have experienced lockdowns, restrictions, and closures due to the fact that people's activity in restaurants, shopping centers, and recreational destinations was restricted, and many other places went into hibernation. After about 2years since the outbreak, businesses in this sector are gradually starting to reopen and revitalize themselves, but in order to have better decision support about the future of this sector, thus being able to plan, businesses are suffering from an effective analytics solution due to the lack of broken data trends. Starting from fresh day-to-day real-time big data, the study aims to develop a new data analytics model, adopting the design science research methodology, which can provide invaluable options and techniques to make prediction easier from immediate past datasets. This study introduces an innovative design artifact as a big data solution for hospitality managers to utilize analytics for predictive strategic decision-making in post-COVID situation. The artifact can also be generalized for other sectors with tailoring aspects which are subject to further studies. The proposed artifact is then compared with other design artifacts related to big data solutions where it outperforms them in terms of comprehensiveness. The proposed artifact also shows promises for primarily available UGC in managers' decision support aids.

A method for selecting processes for automation with AHP and TOPSIS

Organizations are more frequently turning towards robotic process automation (RPA) as a solution for employees to focus on higher complexity and more valuable tasks while delegating routine, monotonous and rule-based tasks to their digital colleagues. These software robots can handle various rule-based, digital, repetitive tasks. However, currently available process identification methods must be qualified to select suitable automation processes accurately. Wrong process selection and failed attempts are often the origin of process automation's bad reputation within organizations and often result in the avoidance of this technology. As a result, in this research, a method for selecting processes for automation combining two multi-criteria decision-making techniques, 'Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), will be proposed, demonstrated, and evaluated. This study follows the Design Science Research Methodology (DSRM) and applies the proposed method for selecting processes for automation to a real-life scenario. The result will be a method to support the proper selection of business processes for automation, increasing the success of implementing RPA tools in an organization.

Policy components – a conceptual model for modularizing and tailoring of information security policies

PurposeThis paper aims to propose a conceptual model of policy components for software that supports modularizing and tailoring of information security policies (ISPs).Design/methodology/approachThis study used a design science research approach, drawing on design knowledge from the field of situational method engineering. The conceptual model was developed as a unified modeling language class diagram using existing ISPs from public agencies in Sweden.FindingsThis study’s demonstration as proof of concept indicates that the conceptual model can be used to create free-standing modules that provide guidance about information security in relation to a specific work task and that these modules can be used across multiple tailored ISPs. Thus, the model can be considered as a step toward developing software to tailor ISPs.Research limitations/implicationsThe proposed conceptual model bears several short- and long-term implications for research. In the short term, the model can act as a foundation for developing software to design tailored ISPs. In the long term, having software that enables tailorable ISPs will allow researchers to do new types of studies, such as evaluating the software's effectiveness in the ISP development process.Practical implicationsPractitioners can use the model to develop software that assist information security managers in designing tailored ISPs. Such a tool can offer the opportunity for information security managers to design more purposeful ISPs.Originality/valueThe proposed model offers a detailed and well-elaborated starting point for developing software that supports modularizing and tailoring of ISPs.

Conception of Learning Objects with Feedback for Self-Regulation of Learning Mathematical Concepts Necessary for Differential and Integral Calculus

Background: Due to the high failure rates in engineering courses, it is of fundamental importance to seek alternatives that minimise the difficulties that students entering Brazilian universities have in the disciplines of the area of mathematics. An alternative is to use learning objects (LO) that allow students to be aware of their errors and encourage them to formulate hypotheses and solve problem situations, aspects that are fundamental for the self-regulation of learning. Objectives: To present the conception and evaluation of an LO with feedback that allows the student to self-regulate their learning with regard to the concepts of mathematics necessary for differential and integral calculus. Design: The research is based on the design science research - DSR methodology. In the first stage, detailed in this article, we sought to present the relevance of the problem and the artefact’s design proposal and the first phase of assessing this design. Setting and Participants: The LO was submitted to an online assessment by seven mathematics teachers and two mathematics education researchers. Data collection and analysis: It was carried out through the transcription of the videos recorded during the assessment via Google Meet. Results: The experts pointed out the potentialities of the LO to diagnose and remedy/intervene before possible errors made by higher education newcomers. Conclusions: After the assessment, the artefact was restructured and improved following the specialists’ notes. The following steps of the research will be the implementation of the complete version of the Los, with questions that explore the strata of numerical, algebraic, and functional knowledge. We expect that the educational product of this research will help students with their mathematical difficulties in DIC.

Evaluating a Framework for Implementing IT Governance in Uganda's Higher Institutions of Learning

The reliance on IT in day-to-day organization activities raises concern about how to deal with its increasing complexity. Managing IT necessitates implementing IT governance to realize the benefits of IT use. However, there is a lack of suitable frameworks to implement IT governance. For higher institutions of learning (HILs) in Uganda, the case is not different; hence, there is need to provide a framework to implement IT governance in Uganda's HILs. This paper therefore applies design science research principles to evaluate a framework for implementing IT governance in HILs in the context of Uganda. It was mainly achieved using a previous study in this environment as a basis. Framework evaluation was conducted using case study and expert opinion methods. Contrarily, the evaluation criteria was based on the framework understandability, ease of use, usefulness, and completeness. Results from the evaluation showed the framework satisfactorily implements IT governance in Uganda's HILs.

Kickstarting blockchain: designing blockchain-based tokens for equity crowdfunding

Blockchain-based tokens seek to overcome the friction and opaqueness of the legacy financial infrastructure in the company funding process, particularly in the early-stage and equity crowdfunding domain. While Initial Coin Offerings and Security Token Offerings proposed a solution for crowdfunding, early-stage companies still face challenges in using blockchain as an alternative equity funding infrastructure. In this context, the idea of blockchain-based equity tokens remains hypothetical. In addition, the literature lacks design theory for the development and implementation of blockchain-based equity tokens. This research bridges this gap by designing, developing, and evaluating an equity token prototype for crowdfunding, following the design science research approach. We propose a refined crowdfunding model and derive seven design principles that contribute to the design theory of equity tokens. The research results show that blockchain-based equity tokens improve efficiency, transparency, and interoperability while meeting regulatory requirements and facilitating secondary market trading.

A spatial data integration and visualization approach for occupational health and safety risks management: Application to Algerian electricity and gas company

AbstractIn the safety 4.0 era, occupational health and safety (OHS) related data has become one of the most valuable assets for organizations. Risk Management in the OHS field is a data‐driven process. However, geospatial data has not been fully considered and few works have used visualization for decision‐making purposes. Our research was conducted using a design science research methodology. It is a methodology for creating and evaluating IT artifacts to solve identified organizational problems. We used Wang's safety intelligence framework and the recommendations of the ISO 45001 model from extant literature to inform the Design. While creating our artifact, we chose to make an inventory of occupational risk prevention practices in Algeria. A qualitative study using interviews with experts in the field consolidated the requirements for such a system. The key findings from the analysis of collected data state that the development of safety data‐driven decision‐making requires top management support and involvement, which is in line with previous studies. A second concern is the availability of spatial data. The designed artifact is an approach called spatial data integration and visualization approach for occupational health and safety management systems (SDIVA‐OHSMS). SDIVA‐OHSMS provides a practical approach for organizations in charge of OHS services to implement OHS data‐driven decision‐making solutions highlighting spatial and semantic dimensions. The evaluation was performed through a representative case study within the Algerian electricity and gas company (Sonelgaz), employing expert opinion and software implementation. As a result, experts concluded that such systems could provide a more comprehensive and accurate view of occupational health issues and guide further prevention policies.

Engineering Student Profile Related to the Market of Operation: Competence Perspective

This article proposes a method/framework to model and quantify engineer competencies from the point of view of students, teachers, and the market. Design/Methodology/Approach: As this work has a practical nature to solve a real problem related to engineering course design, the applied methodology was design science research (DSR). Limitations of the Investigation: The study carries out in a single course, and the instrument allows it to be applied only in courses that use the competence of the conceive design implement operate (CDIO) Syllabus. The number of respondents tends to be low because the student must attend the components of the course (students of the last year or former students). Practical Implications: This study allows us to analyze the differences between the competence developed during the undergraduate course and the labor market demand. Thus, course design improvements can be implemented more effectively. Originality/Value: There are no works in the literature that propose the use of the graph technique in conjunction with the analytic hierarchy process (AHP) to map engineer competence and model performance indicators for the competence developed by students in an engineering course.

The first two months in the war in Ukraine through topic modeling and sentiment analysis

AbstractIn recent years, social media remained not only an environment for expression, but it also became an active battlefield where its users emulate their thoughts, feelings, beliefs, and experiences about the ongoing conflicts and wars, and where sometimes social manipulation techniques become difficult to identify, tackle, and counter. Currently, proper social media analytics solutions are still incipient regarding the ongoing war in Ukraine, but they are much needed. Consequently, this research aims to extract and analyze the topics discussed and the sentiments experienced by Ukrainian Telegram users on data collected in the first 2 months of war in Ukraine through a design science research in a data science methodological approach carried out in a multidisciplinary stance contributing to the ongoing strategic, socio‐ethical, and technical discourses and efforts.

CASCADE: An Asset-driven Approach to Build Security Assurance Cases for Automotive Systems

Security Assurance Cases (SAC) are structured arguments and evidence bodies used to reason about the security of a certain system. SACs are gaining focus in the automotive industry, as the needs for security assurance are growing in this domain. However, the state-of-the-arts lack a mature approach able to suit the needs of the automotive industry. In this article, we present CASCADE, an asset-driven approach for creating SAC, which is inspired by the upcoming security standard ISO/SAE-21434 as well as the internal needs of automotive Original Equipment Manufacturers (OEMs). CASCADE also differentiates itself from the state-of-the-art by incorporating a way to reason about the quality of the constructed security assurance case. We created the approach by conducting an iterative design science research study. We illustrate the results using the example case of the road vehicle’s headlamp provided in the ISO standard. We also illustrate how our approach aligns well with the structure and content of the ISO/SAE-21434 standard, hence demonstrating the practical applicability of CASCADE in an industrial context.

Digital transformation of Higher Education Institutions

Introduction: Digital Transformation (DT) was accelerated by the COVID-19 pandemic, since it has intensified the use of digital platforms as a way to avoid crowding of people in physical environments. DT is present in different markets as more population groups have access to the internet. It has been changing consumer preferences and, consequently, consumer decision-making. One of the markets is that of Higher Education Institutions (HEIs), which adopt strategic changes to continue reaching the audiences that emerge from online platforms. Objective: We applied the Resource-Based View to identify the strategic resources that Brazilian HEIs have used to promote their digital transformation to the e-learning format. Methodology: Therefore, we proposed a framework using the Design Science Research method. To validate the framework, three stages of interviews were carried out with individuals directly involved with DT, HEI professors, HEI managers and EdTech’s professionals. Results/Conclusion: Our results validated a framework with eight attributes: Effectiveness of Courses, Quality Educational Programs, Training of Professors for e-learning, Appropriate Management System, Active Sales Process, Engagement of Professionals, Technology, Agile Development, and Entrepreneurial Culture.

Near-Infrared Spectroscopy for Bladder Monitoring: A Machine Learning Approach

Patients living with neurogenic bladder dysfunction can lose the sensation of their bladder filling. To avoid over-distension of the urinary bladder and prevent long-term damage to the urinary tract, the gold standard treatment is clean intermittent catheterization at predefined time intervals. However, the emptying schedule does not consider actual bladder volume, meaning that catheterization is performed more often than necessary, which can lead to complications such as urinary tract infections. Time-consuming catheterization also interferes with patients' daily routines and, in the case of an empty bladder, uses human and material resources unnecessarily. To enable individually tailored and volume-responsive bladder management, we design a model for the continuous monitoring of bladder volume. During our design science research process, we evaluate the model's applicability and usefulness through interviews with affected patients, prototyping, and application to a real-world in vivo dataset. The developed prototype predicts bladder volume based on relevant sensor data (i.e., near-infrared spectroscopy and acceleration) and the time elapsed since the previous micturition. Our comparison of several supervised state-of-the-art machine and deep learning models reveals that a long short-term memory network architecture achieves a mean absolute error of 116.7 ml that can improve bladder management for patients.