Rules offer an invaluable combination of predictive and descriptive capabilities. Our package for rule-based data analysis, RuleKit, has proven its effectiveness in classification, regression, and survival problems. Here we present its second version. New algorithms and optimized implementations of those previously included, significantly improved the computational performance of our suite, reducing the analysis time of some data sets by two orders of magnitude. The usability of RuleKit 2 is provided by two new components: Python package and browser application with a graphical user interface. The former complies with scikit-learn, the most popular data mining library for Python, allowing RuleKit 2 to be straightforwardly integrated into existing data analysis pipelines. RuleKit 2 is available at GitHub under GNU AGPL 3 license (https://github.com/adaa-polsl/RuleKit)

Can you imagine a future in which babies wear smart clothing to track their every move? It may sound like something from science fiction, but a romper suit being piloted in Helsinki, Copenhagen, and Pisa does exactly that. The'motor assessment of infants jumpsuit' (MAIJU) looks like typical baby clothing, but there is a crucial difference – it is full of sensors which assess child development. "MAIJU offers the first of its kind quantitative assessment of infant's motor abilities through the age from supine lying to fluent walking," explains Professor Sampsa Vanhatalo, project lead at the University of Helsinki. "Such quantitation has not been possible anywhere, not even in hospitals. Here, we are bringing the solution to homes, which provides the only ecologically relevant context for motor assessment."

Leaders from Europe and the US convened to explore exciting leaps forward of AI in oncology at the HIMSS21 & Health 2.0 European Health Conference, though the panel highlighted key barriers to greater acceptance and adoption of AI into mainstream care. The'New Frontiers of AI and Data Analytics in Oncology' session, moderated by Professor Karol Sikora, chief medical officer, Rutherford Health and former chief of the Cancer Programme, WHO, also saw leaders share innovative applications for AI used across the cancer pathway. The panel of experts also included Professor Barbara Alicja Jereczek-Fossa, associate professor of Radiation Oncology, University of Milan and head of Radiotherapy Division, European Institute of Oncology, and her colleague, Eng. Joining from the US was Dr Tufia Haddad, chair of Digital Health, Department of Oncology, Mayo Clinic and chair of Practice Innovation and Platform, Mayo Clinic Cancer Center. While AI is already widely used in oncology in image analysis and other areas, exciting new applications are being trialled at leading cancer centres across the globe.

Digital health experts from the UK will join the debate on how healthcare can evolve beyond Covid-19. As the world struggles to recover from the outfall of Covid-19, the role of digital health has come under the spotlight. Healthcare experts from the UK will be among the European leaders to discuss this new paradigm at the virtual HIMSS21 & Health 2.0 European Health Conference, on June 7-9 2021. The UK is known for its world-leading expertise in genomics. Professor Sharon Peacock, executive director and chair of the Covid-19 Genomics UK (COG-UK) consortium will contribute her vast knowledge and experience to the opening keynote session on creating a roadmap out of the coronavirus crisis.

Rule-based models are often used for data analysis as they combine interpretability with predictive power. We present RuleKit, a versatile tool for rule learning. Based on a sequential covering induction algorithm, it is suitable for classification, regression, and survival problems. The presence of a user-guided induction facilitates verifying hypotheses concerning data dependencies which are expected or of interest. The powerful and flexible experimental environment allows straightforward investigation of different induction schemes. The analysis can be performed in batch mode, through RapidMiner plug-in, or R package. A documented Java API is also provided for convenience. The software is publicly available at GitHub under GNU AGPL-3.0 license.

GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings Marek Sikora a,b,, Łukasz Wróbel a,b,, Adam Gudyś a, a Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland b Institute of Innovative Technologies, EMAG, Leopolda 31, 40-189 Katowice, PolandAbstract This article presents GuideR, a user-guided rule induction algorithm, which overcomes the largest limitation of the existing methods---the lack of the possibility to introduce user's preferences or domain knowledge to the rule learning process. Automatic selection of attributes and attribute ranges often leads to the situation in which resulting rules do not contain interesting information. We propose an induction algorithm which takes into account user's requirements. Our method uses the sequential covering approach and is suitable for classification, regression, and survival analysis problems. The effectiveness of the algorithm in all these tasks has been verified experimentally, confirming guided rule induction to be a powerful data analysis tool. Introduction Sequential covering rule induction algorithms can be used for both, predictive and descriptive purposes [1, 2, 3, 4]. In spite of the development of increasingly sophisticated versions of those algorithms [5, 6], the main principle remains unchanged and involves two phases: rule growing and rule pruning. In the latter, some of these conditions are removed. In comparison to other machine learning methods, rule sets obtained by sequential covering algorithm, also known as separate-and-conquer strategy (SnC), are characterized by good predictive as well as descriptive capabilities. Taking into consideration only the former, superior results can often be obtained using other methods, e.g. However, data models obtained this way are much less comprehensible than rule sets. In the case of rule learning for descriptive purposes, the algorithms of association rule induction [12, 13, 14] or subgroup discovery [15, 6], are applied. The former leads to a very large number of rules which must then be limited by filtering according to rule interestingness measures [16, 17, 18]. Nevertheless, rule sets obtained by subgroup discovery are characterized by worse predictive abilities than those generated by the standard sequential covering approach. Therefore, if creating a prediction system with comprehensible data model is the main objective, the application of sequential covering rule induction algorithms provides the most sensible solution.