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Pattern Recognition in the Usage Sequences of Medical Apps

Abstract : Radiologists use medical imaging solutions on a daily basis for diagnosis. Improving user experience is a major line of the continuous effort to enhance the global quality and usability of software products. Monitoring applications enable to record the evolution of various software and system parameters during their use and in particular the successive actions performed by the users in the software interface. These interactions may be represented as sequences of actions. Based on this data, this work deals with two industrial topics: software crashes and software usability. Both topics imply on one hand understanding the patterns of use, and on the other developing prediction tools either to anticipate crashes or to dynamically adapt software interface according to users' needs. First, we aim at identifying crash root causes. It is essential in order to fix the original defects. For this purpose, we propose to use a binomial test to determine which type of patterns is the most appropriate to represent crash signatures. The improvement of software usability through customization and adaptation of systems to each user's specific needs requires a very good knowledge of how users use the software. In order to highlight the trends of use, we propose to group similar sessions into clusters. We compare 3 session representations as inputs of different clustering algorithms. The second contribution of our thesis concerns the dynamical monitoring of software use. We propose two methods -- based on different representations of input actions -- to address two distinct industrial issues: next action prediction and software crash risk detection. Both methodologies take advantage of the recurrent structure of LSTM neural networks to capture dependencies among our sequential data as well as their capacity to potentially handle different types of input representations for the same data.
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Submitted on : Monday, May 20, 2019 - 4:08:06 PM
Last modification on : Saturday, May 1, 2021 - 3:49:20 AM


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  • HAL Id : tel-02134576, version 1


Chloé Adam. Pattern Recognition in the Usage Sequences of Medical Apps. Neural and Evolutionary Computing [cs.NE]. Université Paris Saclay (COmUE), 2019. English. ⟨NNT : 2019SACLC027⟩. ⟨tel-02134576⟩



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