Pub Date : 2021-06-01DOI: 10.1109/seh52539.2021.00007
{"title":"SEH 2021 Program Committee","authors":"","doi":"10.1109/seh52539.2021.00007","DOIUrl":"https://doi.org/10.1109/seh52539.2021.00007","url":null,"abstract":"","PeriodicalId":415051,"journal":{"name":"2021 IEEE/ACM 3rd International Workshop on Software Engineering for Healthcare (SEH)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121456387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-01DOI: 10.1109/SEH52539.2021.00010
András Kicsi, Klaudia Szabó Ledenyi, P. Pusztai, László Vidács
A great number of radiologic reports are created each year which incorporate the expertise of radiologists. This knowledge could be exploited via machine understanding. This could provide valuable statistics and visualization of the reports, and as training data, and it could also contribute to later automatic reporting applications. In our current work, we present our first steps toward the machine understanding of clinical reports of the spinal region, written in the Hungarian language. Our system provides an automatic classification and connection detection for various entities in the text. Our classification is achieved via bi-directional long short-term memory and conditional random fields producing 0.87–0.95 F1-score values, while the extraction of connection relies on linguistic analysis and predefined rules. The extracted information is displayed in an easily comprehensible, well-formed tree-structure.
{"title":"Automatic Classification and Entity Relation Detection in Hungarian Spinal MRI Reports","authors":"András Kicsi, Klaudia Szabó Ledenyi, P. Pusztai, László Vidács","doi":"10.1109/SEH52539.2021.00010","DOIUrl":"https://doi.org/10.1109/SEH52539.2021.00010","url":null,"abstract":"A great number of radiologic reports are created each year which incorporate the expertise of radiologists. This knowledge could be exploited via machine understanding. This could provide valuable statistics and visualization of the reports, and as training data, and it could also contribute to later automatic reporting applications. In our current work, we present our first steps toward the machine understanding of clinical reports of the spinal region, written in the Hungarian language. Our system provides an automatic classification and connection detection for various entities in the text. Our classification is achieved via bi-directional long short-term memory and conditional random fields producing 0.87–0.95 F1-score values, while the extraction of connection relies on linguistic analysis and predefined rules. The extracted information is displayed in an easily comprehensible, well-formed tree-structure.","PeriodicalId":415051,"journal":{"name":"2021 IEEE/ACM 3rd International Workshop on Software Engineering for Healthcare (SEH)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131185964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-01DOI: 10.1109/SEH52539.2021.00018
Bilal Ahmad, Sarah Beecham, Ita Richardson
This case study paper describes the development and evaluation of a prototype, Golden Jubilants, a smartphone app. Golden Jubilants was built within a software engineering in healthcare project, ReDEAP, that aimed to identify a set of recommendations for the design of smartphone apps for older adults aged over 50. Prototype development and evaluation is a recognized way to elicit meaningful feedback from any user group. This tangible artifact was interactive and provided fruitful engagement for us as researchers, and for the older adults who participated. This short paper presents the prototype evaluation process, and techniques used to ensure older adult involvement. To conclude, we discuss four key recommendations for consideration by software engineering in healthcare researchers who are using prototypes in their research – develop the research project through public and patient involvement, harness the potential of established evaluation and testing standards, develop a needed and tangible prototype, and involve an external group to evaluate findings.
{"title":"The case of Golden Jubilants: using a Prototype to support Healthcare Technology Research","authors":"Bilal Ahmad, Sarah Beecham, Ita Richardson","doi":"10.1109/SEH52539.2021.00018","DOIUrl":"https://doi.org/10.1109/SEH52539.2021.00018","url":null,"abstract":"This case study paper describes the development and evaluation of a prototype, Golden Jubilants, a smartphone app. Golden Jubilants was built within a software engineering in healthcare project, ReDEAP, that aimed to identify a set of recommendations for the design of smartphone apps for older adults aged over 50. Prototype development and evaluation is a recognized way to elicit meaningful feedback from any user group. This tangible artifact was interactive and provided fruitful engagement for us as researchers, and for the older adults who participated. This short paper presents the prototype evaluation process, and techniques used to ensure older adult involvement. To conclude, we discuss four key recommendations for consideration by software engineering in healthcare researchers who are using prototypes in their research – develop the research project through public and patient involvement, harness the potential of established evaluation and testing standards, develop a needed and tangible prototype, and involve an external group to evaluate findings.","PeriodicalId":415051,"journal":{"name":"2021 IEEE/ACM 3rd International Workshop on Software Engineering for Healthcare (SEH)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133758505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-01DOI: 10.1109/SEH52539.2021.00012
Jean-Philippe Stoldt, J. Weber
Data quality is a critical requirement for data-driven clinical decision making in modern healthcare. It is a key prerequisite to many clinical analytics applications, yet much of the research to date focuses on assessing data quality of electronic health records for secondary use. This paper proposes a trust model and provenance-based assessment method for considering data quality during clinical decision making at the point of care. The method uses fuzzy logic to infer data quality trust from a data user’s trust preferences with respect to data producers, data production methods, verification of data items, and certification of data production methods. Implementation with an existing “SMART on FHIR” app in primary care demonstrates the feasibility of model and method. An extension to FHIR resources for data quality trust allows for platform interoperability across system contexts. We consider dual process theories in designing a user interface that supports data quality trust for clinical decisions in heuristic and systematic cognitive processing modes. Model and method are adaptable to other application domains that rely on data quality for decision making.
{"title":"Provenance-based Trust Model for Assessing Data Quality during Clinical Decision Making","authors":"Jean-Philippe Stoldt, J. Weber","doi":"10.1109/SEH52539.2021.00012","DOIUrl":"https://doi.org/10.1109/SEH52539.2021.00012","url":null,"abstract":"Data quality is a critical requirement for data-driven clinical decision making in modern healthcare. It is a key prerequisite to many clinical analytics applications, yet much of the research to date focuses on assessing data quality of electronic health records for secondary use. This paper proposes a trust model and provenance-based assessment method for considering data quality during clinical decision making at the point of care. The method uses fuzzy logic to infer data quality trust from a data user’s trust preferences with respect to data producers, data production methods, verification of data items, and certification of data production methods. Implementation with an existing “SMART on FHIR” app in primary care demonstrates the feasibility of model and method. An extension to FHIR resources for data quality trust allows for platform interoperability across system contexts. We consider dual process theories in designing a user interface that supports data quality trust for clinical decisions in heuristic and systematic cognitive processing modes. Model and method are adaptable to other application domains that rely on data quality for decision making.","PeriodicalId":415051,"journal":{"name":"2021 IEEE/ACM 3rd International Workshop on Software Engineering for Healthcare (SEH)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133046282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-01DOI: 10.1109/SEH52539.2021.00019
Thibaud L'Yvonnet, Elisabetta De Maria, S. Moisan, J. Rigault
Human activity recognition plays an important role especially in medical applications. This paper proposes a formal approach to model such activities, taking into account possible variations in human behavior. This approach is based on discrete-time Markov chains enriched with event occurrence probabilities. We use the PRISM and Storm frameworks and their model checking facilities to express and check interesting temporal logic properties concerning the dynamic evolution of activities. We illustrate our approach on two serious games used by clinicians to monitor Alzheimer patients. This paper focuses on the suitability of such a formal approach to model patients’ behavior, to check behavioral properties of medical interest, and on the respective advantages of the PRISM and Storm frameworks. Our goal is to provide a new tool for doctors to evaluate patients.
{"title":"Probabilistic Model Checking for Activity Recognition in Medical Serious Games","authors":"Thibaud L'Yvonnet, Elisabetta De Maria, S. Moisan, J. Rigault","doi":"10.1109/SEH52539.2021.00019","DOIUrl":"https://doi.org/10.1109/SEH52539.2021.00019","url":null,"abstract":"Human activity recognition plays an important role especially in medical applications. This paper proposes a formal approach to model such activities, taking into account possible variations in human behavior. This approach is based on discrete-time Markov chains enriched with event occurrence probabilities. We use the PRISM and Storm frameworks and their model checking facilities to express and check interesting temporal logic properties concerning the dynamic evolution of activities. We illustrate our approach on two serious games used by clinicians to monitor Alzheimer patients. This paper focuses on the suitability of such a formal approach to model patients’ behavior, to check behavioral properties of medical interest, and on the respective advantages of the PRISM and Storm frameworks. Our goal is to provide a new tool for doctors to evaluate patients.","PeriodicalId":415051,"journal":{"name":"2021 IEEE/ACM 3rd International Workshop on Software Engineering for Healthcare (SEH)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128906773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-01DOI: 10.1109/seh52539.2021.00002
{"title":"Title Page iii","authors":"","doi":"10.1109/seh52539.2021.00002","DOIUrl":"https://doi.org/10.1109/seh52539.2021.00002","url":null,"abstract":"","PeriodicalId":415051,"journal":{"name":"2021 IEEE/ACM 3rd International Workshop on Software Engineering for Healthcare (SEH)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127048540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-23DOI: 10.1109/SEH52539.2021.00017
Gast'on M'arquez, C. Taramasco
Clinical software has become a significant contribution to support clinical management and intra-hospital processes. In this regard, the success or failure of clinical software is mostly yielded on a suitable requirements elicitation process. Although several techniques and approaches address this process, the complexity of clinical services and the variety of clinicians involved in those services make it challenging to elicit requirements. To address this concern, in our previous work, we have proposed the D&I Framework. This collaborative technique translates clinical priorities into guidelines for eliciting software requirements in the healthcare context using implementation and dissemination strategies. This article evaluates the functionalities and tasks implemented in a clinical bed management system whose requirements were elicited using the D&I Framework. We focused on evaluating clinicians’ usability expectation levels using a specific questionnaire executed in 2018 and 2020. The results show that, in comparison with the first release (2018) and the last one (2020), clinicians perceive an improvement in the functionalities and tasks implemented in the system. This study introduces the effects of implementation and dissemination strategies to elicit pragmatic clinical requirements.
{"title":"Evaluating Dissemination and Implementation Strategies to Develop Clinical Software","authors":"Gast'on M'arquez, C. Taramasco","doi":"10.1109/SEH52539.2021.00017","DOIUrl":"https://doi.org/10.1109/SEH52539.2021.00017","url":null,"abstract":"Clinical software has become a significant contribution to support clinical management and intra-hospital processes. In this regard, the success or failure of clinical software is mostly yielded on a suitable requirements elicitation process. Although several techniques and approaches address this process, the complexity of clinical services and the variety of clinicians involved in those services make it challenging to elicit requirements. To address this concern, in our previous work, we have proposed the D&I Framework. This collaborative technique translates clinical priorities into guidelines for eliciting software requirements in the healthcare context using implementation and dissemination strategies. This article evaluates the functionalities and tasks implemented in a clinical bed management system whose requirements were elicited using the D&I Framework. We focused on evaluating clinicians’ usability expectation levels using a specific questionnaire executed in 2018 and 2020. The results show that, in comparison with the first release (2018) and the last one (2020), clinicians perceive an improvement in the functionalities and tasks implemented in the system. This study introduces the effects of implementation and dissemination strategies to elicit pragmatic clinical requirements.","PeriodicalId":415051,"journal":{"name":"2021 IEEE/ACM 3rd International Workshop on Software Engineering for Healthcare (SEH)","volume":"1026 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131450618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-17DOI: 10.1109/SEH52539.2021.00013
Vlad Stirbu, Tuomas Granlund, Jere Hel'en, T. Mikkonen
Software of Unknown Provenance, SOUP, refers to a software component that is already developed and widely available from a 3rd party, and that has not been developed, to be integrated into a medical device. From regulatory perspective, SOUP software requires special considerations, as the developers’ obligations related to design and implementation are not applied to it. In this paper, we consider the implications of extending the concept of SOUP to machine learning (ML) models. As the contribution, we propose practical means to manage the added complexity of 3rd party ML models in regulated development.
{"title":"Extending SOUP to ML Models When Designing Certified Medical Systems","authors":"Vlad Stirbu, Tuomas Granlund, Jere Hel'en, T. Mikkonen","doi":"10.1109/SEH52539.2021.00013","DOIUrl":"https://doi.org/10.1109/SEH52539.2021.00013","url":null,"abstract":"Software of Unknown Provenance, SOUP, refers to a software component that is already developed and widely available from a 3rd party, and that has not been developed, to be integrated into a medical device. From regulatory perspective, SOUP software requires special considerations, as the developers’ obligations related to design and implementation are not applied to it. In this paper, we consider the implications of extending the concept of SOUP to machine learning (ML) models. As the contribution, we propose practical means to manage the added complexity of 3rd party ML models in regulated development.","PeriodicalId":415051,"journal":{"name":"2021 IEEE/ACM 3rd International Workshop on Software Engineering for Healthcare (SEH)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125837977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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