Formal techniques in the safety analysis of software components of a new dialysis machine

The paper is concerned with the practical use of formal techniques to contribute to the risk analysis of a new neonatal dialysis machine. The described formal analysis focuses on the controller component of the software implementation. The controller drives the dialysis cycle and deals with error management. The logic was analysed using model checking techniques and the source code was analysed formally, checking type correctness conditions, use of pointers and shared memory. The analysis provided evidence of the verification of risk control measures relating to the software component. The productive dialogue between the developers of the device, who had no experience or knowledge of formal methods, and the analyst using the formal analysis tools, provided a basis for the development of rationale for the effectiveness of the evidence. (C) 2019 Elsevier B.V. All rights reserved.This work has been funded by: EPSRC research grants EP/G059063/1 and EP/J008133/1: CHI+MED (Computer -Human Interaction for Medical Devices); and NanoSTIMA (ref. NORTE-01-0145-FEDER-000016) financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). Leo Freitas would like to acknowledge EPSRC Trams2 project for financial support, Andrew Sims for providing access to the dialyser, which was used as our case study and Aleksandrs Baklanovs for doing some of the source analysis as part of an undergraduate project

Formal verification of interactive computing systems: Opportunities and challenges

Formal verification has the potential to provide a level of evidence based assurance not possible by more traditional development approaches. For this potential to be fulfilled, its integration into existing practices must be achieved. Starting from this premise, the position paper discusses the opportunities created and the challenges faced by the use of formal verification in the analysis of critical interactive computing systems. Three main challenges are discussed: the accessibility of the modelling stage; support for expressing relevant properties; the need to provide analysis results that are comprehensible to a broad range of expertise including software, safety and human factors.This work is financed by the ERDF - European Regional Development Fundthrough the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project POCI-01-0145-FEDER-016826

Integrating formal methods into medical software development : the ASM approach

Medical devices are safety-critical systems since their malfunctions can seriously compromise human safety. Correct operation of a medical device depends upon the controlling software, whose development should adhere to certification standards. However, these standards provide general descriptions of common software engineering activities without any indication regarding particular methods and techniques to assure safety and reliability. This paper discusses how to integrate the use of a formal approach into the current normative for the medical software development. The rigorous process is based on the Abstract State Machine (ASM) formal method, its refinement principle, and model analysis approaches the method supports. The hemodialysis machine case study is used to show how the ASM-based design process covers most of the engineering activities required by the related standards, and provides rigorous approaches for medical software validation and verification

Artificial intelligence for the artificial kidney: Pointers to the future of a personalized hemodialysis therapy

Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment, or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve patient’s quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of Big Data and will require real-time predictive models. These may come from the fields of Machine Learning and Computational Intelligence, both included in Artificial Intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of Artificial Intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in Artificial Intelligence and Machine Learning, a scientific meeting was organized in the Hospital of Bellvitge (Barcelona, Spain). As an outcome of that meeting, the aim of this review is to investigate Artificial Intelligence experiences on dialysis, with a focus on potential barriers, challenges and prospects for future applications of these technologies.Postprint (author's final draft

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Artificial intelligence for the artificial kidney: pointers to the future of a personalized hemodialysis therapy

Background: Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Summary and Key Messages: Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients

Safety analysis of software components of a dialysis machine using model checking

The paper describes the practical use of a model checking technique to contribute to the risk analysis of a new paediatric dialysis machine. The formal analysis focuses on one component of the system, namely the table-driven software controller which drives the dialysis cycle and deals with error management. The analysis provided evidence of the verification of risk control measures relating to the software component. The paper describes the productive dialogue between the developers of the device, who had no experience or knowledge of formal methods, and an analyst who had experience of using the formal analysis tools. There were two aspects to this dialogue. The first concerned the translation of safety requirements so that they preserved the meaning of the requirement. The second involved understanding the relationship between the software component under analysis and the broader concern of the system as a whole. The paper focuses on the process, highlighting how the team recognised the advantages over a more traditional testing approach.This work has been funded by: EPSRC research grant EP/G059063/1: CHI+MED (Computer-Human Interaction for Medical Devices). It has also been financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme, and by National Funds through the FCT - Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) within project POCI-01-0145-FEDER-006961