@inproceedings {42523,
	title = {Phenotyping of cervical cancer risk groups via generalized low-rank models using medical questionnaires},
	journal = {Norwegian AI Symposium: Nordic Artificial Intelligence Research and Development},
	year = {2022},
	pages = {94--110},
	publisher = {Springer},
	doi = {https://doi.org/10.1007/978-3-031-17030-0_8},
	author = {Becker, Florian and Nyg{\r a}rd, Mari and Nyg{\r a}rd, Jan and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {28185,
	title = {Generalized Low-Rank Models for Phenotyping Cervical Cancer Risk Groups using Medical Questionnaires},
	howpublished = {Stavanger, Norway},
	year = {2021},
	author = {Becker, Florian},
	editor = {Nyg{\r a}rd, Mari and Nyg{\r a}rd, Jan and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@article {25663,
	title = {Anticipating anonymity in screening program databases},
	journal = {International Journal of Medical Informatics},
	volume = {103},
	year = {2017},
	month = {Jan-07-2017},
	pages = {20 - 31},
	publisher = {Elsevier},
	issn = {13865056},
	doi = {10.1016/j.ijmedinf.2017.04.003},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S1386505617300801http://api.elsevier.com/content/article/PII:S1386505617300801?httpAccept=text/xmlhttp://api.elsevier.com/content/article/PII:S1386505617300801?httpAccept=text/plain},
	author = {Caballero, Rafael and Sen, Sagar and Nyg{\r a}rd, Jan}
}
@article {25644,
	title = {Automated Refactoring of OCL Constraints with Search},
	journal = {IEEE Transactions on Software Engineering (TSE)},
	year = {2017},
	publisher = {IEEE},
	abstract = {Object Constraint Language (OCL) constraints are typically used to provide precise semantics to models developed with the Unified Modeling Language (UML). When OCL constraints evolve regularly, it is essential that they are easy to understand and maintain. For instance, in cancer registries, to ensure the quality of cancer data, more than one thousand medical rules are defined and evolve regularly. Such rules can be specified with OCL. It is, therefore, important to ensure the understandability and maintainability of medical rules specified with OCL. To tackle such a challenge, we propose an automated search-based OCL constraint refactoring approach (SBORA) by defining and applying four semantics-preserving refactoring operators (i.e., Context Change, Swap, Split and Merge) and three OCL quality metrics (Complexity, Coupling, and Cohesion) to measure the understandability and maintainability of OCL constraints. We evaluate SBORA along with six commonly used multi-objective search algorithms (e.g., Indicator-Based Evolutionary Algorithm (IBEA)) by employing four case studies from different domains: healthcare (i.e., cancer registry system from Cancer Registry of Norway (CRN)), Oil\&amp;Gas (i.e., subsea production systems), warehouse (i.e., handling systems), and an open source case study named SEPA. Results show: 1) IBEA achieves the best performance among all the search algorithms and 2) the refactoring approach along with IBEA can manage to reduce on average 29.25\% Complexity and 39\% Coupling and improve 47.75\% Cohesion, as compared to the original OCL constraint set from CRN. Furthermore, we conducted a controlled experiment with 96 subjects and results show that the understandability and maintainability of the original constraint set can be improved significantly from the perspectives of the 96 participants of the controlled experiment.},
	doi = {10.1109/TSE.2017.2774829},
	url = {http://ieeexplore.ieee.org/document/8114267/},
	author = {Lu, Hong and Wang, Shuai and Yue, Tao and Ali, Shaukat and Nyg{\r a}rd, Jan}
}
@article {25460,
	title = {IOCL: An Interactive Tool for Specifying, Validating and Evaluating OCL Constraints},
	journal = {Science of Computer Programming (SCP)},
	volume = {149},
	year = {2017},
	month = {08/2017},
	pages = {3-8},
	publisher = {Elsevier},
	abstract = {The Object Constraint Language (OCL) is commonly used for specifying additional constraints on models, in addition, to the ones enforced by the semantics of the models. However, a lot of practitioners and even researchers are reluctant in using OCL to some extent due to the lack of sufficient familiarity with OCL. To facilitate practitioners and researchers in specifying OCL constraints, we designed and developed a web-based tool called interactive OCL (iOCL) for interactively specifying constraints on a given model. The core idea behind iOCL is to present and display only relevant details (e.g., operations) of OCL to users at a given step of constraint specification process, in addition to helping modelers with its syntax. We evaluated iOCL using a real-world case study from Cancer Registry of Norway and the results showed that iOCL can significantly reduce the time required to specify OCL constraints and decrease the possibility of making syntactic errors during the specification process. Thus, we conclude that iOCL can facilitate the process of OCL constraint specification.\&nbsp;},
	author = {Muhammad, Hammad and Yue, Tao and Wang, Shuai and Ali, Shaukat and Nyg{\r a}rd, Jan}
}
@techreport {25013,
	title = {A Pilot Experiment to Assess Interactive OCL Specification in a Real Setting},
	year = {2017},
	publisher = {Simula Research Laboratory},
	abstract = {The Object Constraint Language (OCL) is a formal, declarative, and side-effect free language, standardized by the Object Management Group, for specifying constraints or queries on models specified in the Unified Modeling Language (UML). OCL was designed with the aim to bridge the gap between natural language and traditional formal languages requiring a strong mathematical background to understand and apply. OCL, along with UML, have been applied in practice for various purposes such as facilitating automated model-based testing. In most of such contexts of OCL, engineers with software engineering backgrounds specify OCL constraints. However, it is still a challenge for constraint authors (e.g., medical coders) who have no such background to apply OCL for other purposes (e.g., specifying medical rules). In this direction, in our previous work, we proposed a user-interactive specification framework, named iOCL, for facilitating OCL constraint specification and validation. The aim was to ease its adoption in practice in a wider application scope. In this paper, we present a pilot experiment that was conducted to assess the practical applicability of iOCL in Cancer Registry of Norway with real users of iOCL in terms of specifying medical cancer coding rules with iOCL. Results of the pilot experiment showed that, with iOCL, time to specify OCL constraints can be significantly reduced as compared to directly specifying OCL constraints without the tool support. In addition, participants of the experiment found that iOCL is easy to use.\&nbsp;\&nbsp;\&nbsp;\&nbsp;},
	author = {Ali, Shaukat and Muhammad, Hammad and Lu, Hong and Nyg{\r a}rd, Jan and Wang, Shuai and Yue, Tao}
}
@inproceedings {25464,
	title = {RCIA: Automated Change Impact Analysis to Facilitate a Practical Cancer Registry System},
	journal = {The International Conference on Software Maintenance and Evolution (ICSME)},
	year = {2017},
	pages = {603-612},
	publisher = {IEEE},
	abstract = {The Cancer Registry of Norway (CRN) employs a cancer registry system to collect cancer patient data (e.g., diagnosis and treatments) from various medical entities (e.g., clinic hospitals). The collected data are then checked for validity (i.e., validation) and assembled as cancer cases (i.e., aggregation) based on more than 1000 cancer coding rules in the system. However, it is frequent in practice that the collected cancer data changes due to various reasons (e.g., different treatments) and the cancer coding rules can also change/evolve due to new medical knowledge. Thus, such a cancer registry system requires an efficient means to automatically analyze these changes and provide consequent impacts to medical experts for further actions. This paper proposes an automated Rule-based Change Impact Analysis (CIA) approach named RCIA that includes: 1) a change classification to capture the potential changes that can occur at CRN; 2) in total 80 change impact analysis rules including 50 dependency rules and 30 impact rules; and 3) an efficient algorithm to analyze changes and produce consequent impacts. We evaluate RCIA via a case study with 12 real change sets from CRN and a conducted interview. The results showed that RCIA managed to produce 100\% actual change impacts and the medical expert at CRN is quite positive to apply RCIA to facilitate their cancer registry system. We also shared a set of lessons learned based on the collaboration with CRN.\&nbsp;},
	author = {Wang, Shuai and Schwitalla, Thomas and Yue, Tao and Ali, Shaukat and Nyg{\r a}rd, Jan},
	editor = {IEEE, }
}
@inproceedings {24479,
	title = {MBF4CR: A Model-Based Framework for Supporting An Automated Cancer Registry System},
	journal = {12th European Conference on Modelling Foundations and Applications (ECMFA 2016)},
	year = {2016},
	pages = {191-204},
	publisher = {Lecture Notes in Computer Science, Springer Verlag},
	abstract = {The Cancer Registry of Norway (CRN) collects medical information (e.g., laboratory results, clinical procedures and treatment) of cancer patients from different medical entities, for all cancer patients in Norway. The collected data are checked for validity and correctness (i.e., validation) and is the basis for the registration of cancer cases (i.e., aggregation) by employing more than a thousand of medical rules. However, the current practice of CRN lacks of a systematic way to capture the domain knowledge and maintain medical rules at a proper level of abstraction. To tackle these challenges, this paper proposes a model-based framework (named as MBF4CR) for capturing the domain knowledge, formalizing medi- cal rules, automating rule selection, and enabling data (cancer messages and cancer cases) validation and aggregation using Unified Modeling Language (UML) and Object Constraint Language (OCL). MBF4CR systematically cap- tures domain knowledge (e.g., cancer messages) as a UML class diagram and formally specifies medical rules as OCL constraints. By associating tags to OCL constraints, MBF4CR enables an automated rule selection process with tool support. We employed a case study from CRN that consists of 187 medical rules to evaluate MBF4CR from two aspects: Performance in terms of se- lecting and executing rules, and Correctness in terms of producing correct validation and aggregation results. Results show that MBF4CR can facilitate the current practice by complying with the medical domain knowledge with an acceptable performance, while reducing the maintenance effort.\&nbsp;},
	author = {Wang, Shuai and Lu, Hong and Yue, Tao and Ali, Shaukat and Nyg{\r a}rd, Jan}
}
@inproceedings {24936,
	title = {A Model-Based Approach with Tool Support to Facilitate the Cancer Registration Process in Cancer Registry of Norway},
	journal = {European Telemedicine Conference (ETC)},
	year = {2016},
	author = {Wang, Shuai and Lu, Hong and Yue, Tao and Ali, Shaukat and Nyg{\r a}rd, Jan}
}
