Widely used methods of evaluation electroencephalogram (EEG) signals coherence have well-known drawbacks associated with the averaging of the evaluation of coherence in non-overlapping time periods and/or the wide frequency ranges, and the influence of the evaluation of amplitude modulation and noise. The report considers a new approach to the evaluation of phase coherency of EEG signals, consisting of the calculation of the phase of signals wavelet spectra at the points of the ridges. These points, under certain conditions, have property of phase stationarity. In this case, it is not required to conduct mentioned averaging. Evaluations of inter-channel EEG phase coherency using cognitive and motor tests by healthy subjects and patients after traumatic brain injuries are considered. This method allows to distinguish the phase-coupled pairs of EEG leads from uncoupled ones and to determine the phase-coupled pairs of leads specified for a certain test of the EEG record.
{"title":"A New Approach to Evaluation of Electroencephalograms Inter-Channel Phase Synchronization","authors":"R. Tolmacheva, Y. Obukhov, L. Zhavoronkova","doi":"10.1109/CBMS.2018.00028","DOIUrl":"https://doi.org/10.1109/CBMS.2018.00028","url":null,"abstract":"Widely used methods of evaluation electroencephalogram (EEG) signals coherence have well-known drawbacks associated with the averaging of the evaluation of coherence in non-overlapping time periods and/or the wide frequency ranges, and the influence of the evaluation of amplitude modulation and noise. The report considers a new approach to the evaluation of phase coherency of EEG signals, consisting of the calculation of the phase of signals wavelet spectra at the points of the ridges. These points, under certain conditions, have property of phase stationarity. In this case, it is not required to conduct mentioned averaging. Evaluations of inter-channel EEG phase coherency using cognitive and motor tests by healthy subjects and patients after traumatic brain injuries are considered. This method allows to distinguish the phase-coupled pairs of EEG leads from uncoupled ones and to determine the phase-coupled pairs of leads specified for a certain test of the EEG record.","PeriodicalId":229453,"journal":{"name":"2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115712504","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}
Marc Schickler, R. Pryss, W. Schlee, T. Probst, B. Langguth, Johannes Schobel, M. Reichert
Many studies have revealed that therapeutic homework is beneficial for the efficacy of therapies. Interestingly, the latter have been less supported by IT systems so far and, hence, therapeutic opportunities have been neglected. For example, mobile devices can be used to notify patients about assigned homework and help them to accomplish it in a timely manner. In general, the use of mobile devices as well as their sensors seem to be promising for the support of remote therapeutic interventions. In the Albatros project, we have been developing a framework that enables domain experts to flexibly define the homework required in the context of a remote therapeutic intervention. More precisely, the various tasks of a homework can be specified as a mobile process, which is then run on the mobile device of the respective patient. To realize this vision, a configurator component using a model-driven approach was developed. In particular, the Albatros configurator shall relieve domain experts from complex technical issues when defining a homework. The study presented in this paper investigates whether domain experts are actually able to use the configurator component. In particular, the study revealed three insights. First, basic interventions can be easily defined with an acceptable number of errors. Second, for defining complex interventions (e.g., using a sensor when performing an exercise) several issues could be identified that will contribute to improve the Albatros configurator. Third, additional studies are needed to evaluate the overall mental effort of domain experts when using the configurator. Altogether, the Albatros framework may be a reasonable alley to empower domain experts in creating homework in the context of remote therapeutic interventions.
{"title":"Usability Study on Mobile Processes Enabling Remote Therapeutic Interventions","authors":"Marc Schickler, R. Pryss, W. Schlee, T. Probst, B. Langguth, Johannes Schobel, M. Reichert","doi":"10.1109/CBMS.2018.00033","DOIUrl":"https://doi.org/10.1109/CBMS.2018.00033","url":null,"abstract":"Many studies have revealed that therapeutic homework is beneficial for the efficacy of therapies. Interestingly, the latter have been less supported by IT systems so far and, hence, therapeutic opportunities have been neglected. For example, mobile devices can be used to notify patients about assigned homework and help them to accomplish it in a timely manner. In general, the use of mobile devices as well as their sensors seem to be promising for the support of remote therapeutic interventions. In the Albatros project, we have been developing a framework that enables domain experts to flexibly define the homework required in the context of a remote therapeutic intervention. More precisely, the various tasks of a homework can be specified as a mobile process, which is then run on the mobile device of the respective patient. To realize this vision, a configurator component using a model-driven approach was developed. In particular, the Albatros configurator shall relieve domain experts from complex technical issues when defining a homework. The study presented in this paper investigates whether domain experts are actually able to use the configurator component. In particular, the study revealed three insights. First, basic interventions can be easily defined with an acceptable number of errors. Second, for defining complex interventions (e.g., using a sensor when performing an exercise) several issues could be identified that will contribute to improve the Albatros configurator. Third, additional studies are needed to evaluate the overall mental effort of domain experts when using the configurator. Altogether, the Albatros framework may be a reasonable alley to empower domain experts in creating homework in the context of remote therapeutic interventions.","PeriodicalId":229453,"journal":{"name":"2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126186985","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}
Federico Bolelli, F. Pollastri, Roberto Paredes Palacios, C. Grana
This paper proposes a novel strategy that employs Generative Adversarial Networks (GANs) to augment data in the image segmentation field, and a Convolutional-Deconvolutional Neural Network (CDNN) to automatically generate lesion segmentation mask from dermoscopic images. Training the CDNN with our GAN generated data effectively improves the state-of-the-art.
{"title":"Improving Skin Lesion Segmentation with Generative Adversarial Networks","authors":"Federico Bolelli, F. Pollastri, Roberto Paredes Palacios, C. Grana","doi":"10.1109/CBMS.2018.00086","DOIUrl":"https://doi.org/10.1109/CBMS.2018.00086","url":null,"abstract":"This paper proposes a novel strategy that employs Generative Adversarial Networks (GANs) to augment data in the image segmentation field, and a Convolutional-Deconvolutional Neural Network (CDNN) to automatically generate lesion segmentation mask from dermoscopic images. Training the CDNN with our GAN generated data effectively improves the state-of-the-art.","PeriodicalId":229453,"journal":{"name":"2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122353699","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}
Marcos Roberto Nesso Junior, M. Cazzolato, L. C. Scabora, Paulo H. Oliveira, Gabriel Spadon, J. D. Souza, Willian D. Oliveira, D. Y. T. Chino, J. F. Rodrigues, A. Traina, C. Traina
Medical exams, such as CT scans and mammograms, are obtained and stored every day in hospitals all over the world, including images, patient data, and medical reports. It is paramount to have tools and systems to improve computer-aided diagnoses based on such huge volumes of stored information. The Content-Based Image Retrieval (CBIR) is a powerful paradigm to help reaching such a goal, providing physicians with intelligent retrieval tools to present him/her with similar or complementary cases, in which visual characteristics improve textual data. Employing comparative inspection on previous cases, the physician can obtain a more comprehensive understanding of the case he/she is working on. Current hospital systems do not carry native CBIR functionalities yet, relying on add-on subsystems, which often do not adhere to the existing relational database infrastructures. In this work, we propose RAFIKI, a software prototype that extends the Relational Database Management System (RDBMS) PostgreSQL, providing native support for CBIR functionalities, modular extensibility, and seamless integration for data science tools, such as Python and R. We show the applicability of our system by evaluating three clinical scenarios, performing queries over a real-world image dataset of lung exams. Our results spot actual potential in promoting informed decision-making from the physician's perspective. Besides, the system exhibited a higher performance when compared to previous systems found in the literature. Moreover, RAFIKI contributes with a model to establish how to put together CBIR concepts and relational data, providing a powerful design for further development of theoretical and practical concepts and tools.
{"title":"RAFIKI: Retrieval-Based Application for Imaging and Knowledge Investigation","authors":"Marcos Roberto Nesso Junior, M. Cazzolato, L. C. Scabora, Paulo H. Oliveira, Gabriel Spadon, J. D. Souza, Willian D. Oliveira, D. Y. T. Chino, J. F. Rodrigues, A. Traina, C. Traina","doi":"10.1109/CBMS.2018.00020","DOIUrl":"https://doi.org/10.1109/CBMS.2018.00020","url":null,"abstract":"Medical exams, such as CT scans and mammograms, are obtained and stored every day in hospitals all over the world, including images, patient data, and medical reports. It is paramount to have tools and systems to improve computer-aided diagnoses based on such huge volumes of stored information. The Content-Based Image Retrieval (CBIR) is a powerful paradigm to help reaching such a goal, providing physicians with intelligent retrieval tools to present him/her with similar or complementary cases, in which visual characteristics improve textual data. Employing comparative inspection on previous cases, the physician can obtain a more comprehensive understanding of the case he/she is working on. Current hospital systems do not carry native CBIR functionalities yet, relying on add-on subsystems, which often do not adhere to the existing relational database infrastructures. In this work, we propose RAFIKI, a software prototype that extends the Relational Database Management System (RDBMS) PostgreSQL, providing native support for CBIR functionalities, modular extensibility, and seamless integration for data science tools, such as Python and R. We show the applicability of our system by evaluating three clinical scenarios, performing queries over a real-world image dataset of lung exams. Our results spot actual potential in promoting informed decision-making from the physician's perspective. Besides, the system exhibited a higher performance when compared to previous systems found in the literature. Moreover, RAFIKI contributes with a model to establish how to put together CBIR concepts and relational data, providing a powerful design for further development of theoretical and practical concepts and tools.","PeriodicalId":229453,"journal":{"name":"2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131208047","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}
Maria Pedroto, A. Jorge, João Mendes-Moreira, T. Coelho
This work describes a problem oriented approach to analyze and predict the Age of Onset of Patients diagnosed with Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP). We constructed, from a set of clinical and familial records, three sets of features which represent different characteristics of a patient, before becoming symptomatic. Using those features, we tested a set of machine learning regression methods, namely Decision Tree (Regression Tree), Elastic Net, Lasso, Linear Regression, Random Forest Regressor, Ridge Regression and Support Vector Machine Regressor (SVM). Later, we defined a baseline model that represents the current medical practice to serve as a guideline for us to measure the accuracy of our approach. Our results show a significant improvement of machine learning methods when compared with the current baseline.
{"title":"Predicting Age of Onset in TTR-FAP Patients with Genealogical Features","authors":"Maria Pedroto, A. Jorge, João Mendes-Moreira, T. Coelho","doi":"10.1109/CBMS.2018.00042","DOIUrl":"https://doi.org/10.1109/CBMS.2018.00042","url":null,"abstract":"This work describes a problem oriented approach to analyze and predict the Age of Onset of Patients diagnosed with Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP). We constructed, from a set of clinical and familial records, three sets of features which represent different characteristics of a patient, before becoming symptomatic. Using those features, we tested a set of machine learning regression methods, namely Decision Tree (Regression Tree), Elastic Net, Lasso, Linear Regression, Random Forest Regressor, Ridge Regression and Support Vector Machine Regressor (SVM). Later, we defined a baseline model that represents the current medical practice to serve as a guideline for us to measure the accuracy of our approach. Our results show a significant improvement of machine learning methods when compared with the current baseline.","PeriodicalId":229453,"journal":{"name":"2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132294912","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}
R. S. Bressan, D. Alves, Lucas M. Valério, P. Bugatti, P. T. Saito
Nowadays, deep features, obtained from a variety of deep learning architectures, play an important role in several real problems. It is know that transfer learning strategies could be employed to take advantage of such deep features trained under a general context (e.g. ImageNet). However, to the best of our knowledge, the majority of works focus on similar contexts to accomplish such transfer strategies. Thus, in this work we analyze the role of deep features in content-based medical image retrieval, and demonstrate that it is possible to make use of transfer learning from a general context to a specific medical context, like the content-based mammographic image retrieval. To do so, we evaluated several hand-crafted features against deep features acquired from state-of-the-art deep architectures through transfer learning. Extensive experiments on challenging public mammographic image datasets testify that the generalized deep features are able to improve in a great extend the precision of similarity queries both in the traditional process and applying query refinement strategies.
{"title":"DOCToR: The Role of Deep Features in Content-Based Mammographic Image Retrieval","authors":"R. S. Bressan, D. Alves, Lucas M. Valério, P. Bugatti, P. T. Saito","doi":"10.1109/CBMS.2018.00035","DOIUrl":"https://doi.org/10.1109/CBMS.2018.00035","url":null,"abstract":"Nowadays, deep features, obtained from a variety of deep learning architectures, play an important role in several real problems. It is know that transfer learning strategies could be employed to take advantage of such deep features trained under a general context (e.g. ImageNet). However, to the best of our knowledge, the majority of works focus on similar contexts to accomplish such transfer strategies. Thus, in this work we analyze the role of deep features in content-based medical image retrieval, and demonstrate that it is possible to make use of transfer learning from a general context to a specific medical context, like the content-based mammographic image retrieval. To do so, we evaluated several hand-crafted features against deep features acquired from state-of-the-art deep architectures through transfer learning. Extensive experiments on challenging public mammographic image datasets testify that the generalized deep features are able to improve in a great extend the precision of similarity queries both in the traditional process and applying query refinement strategies.","PeriodicalId":229453,"journal":{"name":"2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114305370","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}
{"title":"CBMS 2018 Welcome Message and Preface","authors":"B. Kane","doi":"10.1109/cbms.2018.00005","DOIUrl":"https://doi.org/10.1109/cbms.2018.00005","url":null,"abstract":"","PeriodicalId":229453,"journal":{"name":"2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114529142","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}
João Rafael Almeida, T. Godinho, Luís Bastião, C. Costa, J. Oliveira
Medical imaging laboratories are supported by information and communication systems commonly denominated as PACS, that encompasses technology for acquisition, archive, distribution and visualization of digital images in network. Concerning the data and workflow management, traditional solutions used in production provide a limited set of services usually configured at system installation. As result, healthcare institutions are not able to fully explore their infrastructure or adapt it to new operational requirements, either for clinical or research procedures. This article proposes a framework for services orchestration and workflow management in distributed medical imaging environments. It was designed for end-user usage and is accessible through a Web portal that allows to document, repeat and allocate procedures and tasks to correct resources, either from information systems or human interventions. It provides an abstraction layer for integration with distinct data sources through standard services, allows the creation of new services through orchestration of existent ones and the scheduling of tasks. Moreover, it includes a logging and alert mechanism integrated with email service. The solution was validated through the specification of two use cases that were deployed in production environment.
{"title":"Services Orchestration and Workflow Management in Distributed Medical Imaging Environments","authors":"João Rafael Almeida, T. Godinho, Luís Bastião, C. Costa, J. Oliveira","doi":"10.1109/CBMS.2018.00037","DOIUrl":"https://doi.org/10.1109/CBMS.2018.00037","url":null,"abstract":"Medical imaging laboratories are supported by information and communication systems commonly denominated as PACS, that encompasses technology for acquisition, archive, distribution and visualization of digital images in network. Concerning the data and workflow management, traditional solutions used in production provide a limited set of services usually configured at system installation. As result, healthcare institutions are not able to fully explore their infrastructure or adapt it to new operational requirements, either for clinical or research procedures. This article proposes a framework for services orchestration and workflow management in distributed medical imaging environments. It was designed for end-user usage and is accessible through a Web portal that allows to document, repeat and allocate procedures and tasks to correct resources, either from information systems or human interventions. It provides an abstraction layer for integration with distinct data sources through standard services, allows the creation of new services through orchestration of existent ones and the scheduling of tasks. Moreover, it includes a logging and alert mechanism integrated with email service. The solution was validated through the specification of two use cases that were deployed in production environment.","PeriodicalId":229453,"journal":{"name":"2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124629675","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}
Improving the accuracy and consistency of interpretation results for foetal monitoring has been an active research direction in both obstetrics and gynaecology. In this paper, we have developed computer-aided analysis systems for use both in hospitals and at home that incorporate automatic scoring functions to evaluate the foetal conditions in the cavity of the uterus. These systems can analyse any segment of data in a foetal monitoring record. Our novel systems can accurately identify the CTG patterns, such as FHR baseline, foetal movements, uterine contractions, accelerations and type of decelerations, thus making the interpretation results more accurate. There are two modes of scoring: automatic and manual, and the system consists of a number of popular scoring methods, including the Kreb's, Fischer, and improved Fischer scoring methods and the ACOG three-tier classification methods. According to clinical tests in hospitals, the systems have comparable accuracy to obstetricians' interpretations. Computerised interpretation thus provides a supplement to traditional analysis that could help obstetricians function more effectively.
{"title":"Computerised Interpretation Systems for Cardiotocography for Both Home and Hospital Uses","authors":"Yu Lu, Yongjie Gao, Yuyang Xie, Shunan He","doi":"10.1109/CBMS.2018.00080","DOIUrl":"https://doi.org/10.1109/CBMS.2018.00080","url":null,"abstract":"Improving the accuracy and consistency of interpretation results for foetal monitoring has been an active research direction in both obstetrics and gynaecology. In this paper, we have developed computer-aided analysis systems for use both in hospitals and at home that incorporate automatic scoring functions to evaluate the foetal conditions in the cavity of the uterus. These systems can analyse any segment of data in a foetal monitoring record. Our novel systems can accurately identify the CTG patterns, such as FHR baseline, foetal movements, uterine contractions, accelerations and type of decelerations, thus making the interpretation results more accurate. There are two modes of scoring: automatic and manual, and the system consists of a number of popular scoring methods, including the Kreb's, Fischer, and improved Fischer scoring methods and the ACOG three-tier classification methods. According to clinical tests in hospitals, the systems have comparable accuracy to obstetricians' interpretations. Computerised interpretation thus provides a supplement to traditional analysis that could help obstetricians function more effectively.","PeriodicalId":229453,"journal":{"name":"2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124748008","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}
Lakshmi Prasath Muniandi, W. Schlee, R. Pryss, M. Reichert, Johannes Schobel, Robin Kraft, M. Spiliopoulou
Mobile applications can help patients with a chronical disease to record their Ecological Momentary Assessments (EMA) and to get a more precise impression of how their disease manifests itself during day and night and over longer time periods. Such crowdsensing applications contribute to patient empowerment, in which patients monitor their disease and, sometimes, learn to cope better with it. An open question is whether physicians can also be helped in assisting their patients, by understanding similarities and differences in the patients' evolution. We study the EMA of patients with the chronical disease tinnitus, as recorded with the mobile crowdsensing application Track Your Tinnitus. We propose a method that captures similarities in patient evolution, taking account of the differences in the frequency of each patient's EMA recordings. We incorporate this method into a complete workflow that encompasses following components: an algorithm that captures similarities among patients on the basis of their registration data, a method that juxtaposes static patient similarity to EMA-based patient similarity, and a method that identifies those subspaces of the static feature space and those of the EMA-based feature space, which are mainly contributing to patient similarity. We report on our results for the time period recordings from 2014 till 2017 of 450 tinnitus patients from TrackYourTinnitus mobile application.
移动应用程序可以帮助患有慢性疾病的患者记录他们的生态瞬间评估(EMA)，并更准确地了解他们的疾病在白天和黑夜以及更长时间内的表现。这种群众感知应用有助于赋予患者权力，患者可以监测自己的疾病，有时还能学会更好地应对疾病。一个悬而未决的问题是，通过了解患者进化过程中的异同点，是否也能帮助医生帮助他们的患者。我们研究慢性疾病耳鸣患者的EMA，通过移动众感应用程序Track Your tinnitus记录。我们提出了一种方法来捕捉患者进化的相似性，同时考虑到每个患者EMA记录频率的差异。我们将该方法整合到一个完整的工作流中，该工作流包含以下组件:一个基于注册数据捕获患者之间相似性的算法，一个将静态患者相似性与基于ema的患者相似性并列的方法，以及一个识别静态特征空间和基于ema的特征空间的子空间的方法，这些子空间主要有助于患者相似性。我们报告了来自TrackYourTinnitus移动应用程序的450名耳鸣患者2014年至2017年的时间段记录结果。
{"title":"Finding Tinnitus Patients with Similar Evolution of Their Ecological Momentary Assessments","authors":"Lakshmi Prasath Muniandi, W. Schlee, R. Pryss, M. Reichert, Johannes Schobel, Robin Kraft, M. Spiliopoulou","doi":"10.1109/CBMS.2018.00027","DOIUrl":"https://doi.org/10.1109/CBMS.2018.00027","url":null,"abstract":"Mobile applications can help patients with a chronical disease to record their Ecological Momentary Assessments (EMA) and to get a more precise impression of how their disease manifests itself during day and night and over longer time periods. Such crowdsensing applications contribute to patient empowerment, in which patients monitor their disease and, sometimes, learn to cope better with it. An open question is whether physicians can also be helped in assisting their patients, by understanding similarities and differences in the patients' evolution. We study the EMA of patients with the chronical disease tinnitus, as recorded with the mobile crowdsensing application Track Your Tinnitus. We propose a method that captures similarities in patient evolution, taking account of the differences in the frequency of each patient's EMA recordings. We incorporate this method into a complete workflow that encompasses following components: an algorithm that captures similarities among patients on the basis of their registration data, a method that juxtaposes static patient similarity to EMA-based patient similarity, and a method that identifies those subspaces of the static feature space and those of the EMA-based feature space, which are mainly contributing to patient similarity. We report on our results for the time period recordings from 2014 till 2017 of 450 tinnitus patients from TrackYourTinnitus mobile application.","PeriodicalId":229453,"journal":{"name":"2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129761919","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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