Pub Date : 2023-01-01DOI: 10.5220/0011776100003414
Angelina Schmidt, Dimitri Kraft, Fabienne Lambusch, M. Fellmann
{"title":"User-Perception of a Webcam-Based Intervention System for Healthy Habits at Computer Workstations","authors":"Angelina Schmidt, Dimitri Kraft, Fabienne Lambusch, M. Fellmann","doi":"10.5220/0011776100003414","DOIUrl":"https://doi.org/10.5220/0011776100003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"24 1","pages":"567-580"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84444976","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 : 2023-01-01DOI: 10.5220/0011667700003414
S. Appelbaum, D. Krüerke, Stephan Baumgartner, M. Schenker, T. Ostermann
: Cancer is still a fatal disease in many cases, despite intensive research into prevention, treatment and follow-up. In this context, an important parameter is the stage of the cancer. The TNM/UICC classification is an important method to describe a cancer. It dates back to the surgeon Pierre Denoix and is an important prognostic factor for patient survival. Unfortunately, despite its importance, the TNM/UICC classification is often poorly documented in cancer registries. The aim of this work is to investigate the possibility of predicting UICC stages using statistical learning methods based on cancer registry data. Data from the Cancer Registry Clinic Arlesheim (CRCA) were used for this analysis. It contains a total of 5,305 records of which 1,539 cases were eligible for data analysis. For prediction classification and regression trees, random forests, gradient tree boosting and logistic regression are used as statistical methods for the problem at hand. As performance measures Mean misclassification error (mmce), area under the receiver operating curve (AUC) and Cohen’s kappa are applied. Misclassification rates were in the range of 28.0% to 30.4%. AUCs ranged between 0.73 and 0.80 and Cohen kappa showed values between 0.39 and 0.44 which only show a moderate predictive performance. However, with only 1,539 records, the data set considered here was significantly lower than those of larger cancer registries, so that the results found here should be interpreted with caution.
{"title":"Development, Implementation and Validation of a Stochastic Prediction Model of UICC Stages for Missing Values in Large Data Sets in a Hospital Cancer Registry","authors":"S. Appelbaum, D. Krüerke, Stephan Baumgartner, M. Schenker, T. Ostermann","doi":"10.5220/0011667700003414","DOIUrl":"https://doi.org/10.5220/0011667700003414","url":null,"abstract":": Cancer is still a fatal disease in many cases, despite intensive research into prevention, treatment and follow-up. In this context, an important parameter is the stage of the cancer. The TNM/UICC classification is an important method to describe a cancer. It dates back to the surgeon Pierre Denoix and is an important prognostic factor for patient survival. Unfortunately, despite its importance, the TNM/UICC classification is often poorly documented in cancer registries. The aim of this work is to investigate the possibility of predicting UICC stages using statistical learning methods based on cancer registry data. Data from the Cancer Registry Clinic Arlesheim (CRCA) were used for this analysis. It contains a total of 5,305 records of which 1,539 cases were eligible for data analysis. For prediction classification and regression trees, random forests, gradient tree boosting and logistic regression are used as statistical methods for the problem at hand. As performance measures Mean misclassification error (mmce), area under the receiver operating curve (AUC) and Cohen’s kappa are applied. Misclassification rates were in the range of 28.0% to 30.4%. AUCs ranged between 0.73 and 0.80 and Cohen kappa showed values between 0.39 and 0.44 which only show a moderate predictive performance. However, with only 1,539 records, the data set considered here was significantly lower than those of larger cancer registries, so that the results found here should be interpreted with caution.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"145 1","pages":"117-123"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85287775","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 : 2023-01-01DOI: 10.5220/0011603800003414
Paulina Adamczyk, Sylwia Marek, Ryszard Pr˛ecikowski, Maciej Kuś, Michal K. Grzeszczyk, M. Malawski, Aneta Lisowska
: To monitor patients’ well-being and evaluate the efficacy of digital health intervention, patients are required to regularly respond to standardised surveys. Responding to a large number of questionnaires is effortful and may discourage mHealth app users from engaging with the intervention. Gamification might reduce the burden of self-reporting. However, researchers have adopted various approaches to the personalisation of gamification design: ranking of game elements by the user, Hexad Gamification User Types classification (G) and selection of preferred design mockups (MU) . In this paper we report on a small population study involving 54 healthy participants aged 17 to 60, and investigate if these alternative approaches lead to the same design choices. We find that different evaluation approaches lead to different choices of gamification elements. We suggest to use game element ranking in combination with mockup selection. Hexad player classification might be less useful in the context of mHealth applications design.
{"title":"Designing Personalised Gamification of mHealth Survey Applications","authors":"Paulina Adamczyk, Sylwia Marek, Ryszard Pr˛ecikowski, Maciej Kuś, Michal K. Grzeszczyk, M. Malawski, Aneta Lisowska","doi":"10.5220/0011603800003414","DOIUrl":"https://doi.org/10.5220/0011603800003414","url":null,"abstract":": To monitor patients’ well-being and evaluate the efficacy of digital health intervention, patients are required to regularly respond to standardised surveys. Responding to a large number of questionnaires is effortful and may discourage mHealth app users from engaging with the intervention. Gamification might reduce the burden of self-reporting. However, researchers have adopted various approaches to the personalisation of gamification design: ranking of game elements by the user, Hexad Gamification User Types classification (G) and selection of preferred design mockups (MU) . In this paper we report on a small population study involving 54 healthy participants aged 17 to 60, and investigate if these alternative approaches lead to the same design choices. We find that different evaluation approaches lead to different choices of gamification elements. We suggest to use game element ranking in combination with mockup selection. Hexad player classification might be less useful in the context of mHealth applications design.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"17 1","pages":"224-231"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82377059","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 : 2023-01-01DOI: 10.5220/0011776800003414
Ana Martins, I. Nunes, L. Lapão, A. Londral
{"title":"Designing a Digital Personal Coach to Promote a Healthy Diet and Physical Activity Among Patients After Cardiothoracic Surgery","authors":"Ana Martins, I. Nunes, L. Lapão, A. Londral","doi":"10.5220/0011776800003414","DOIUrl":"https://doi.org/10.5220/0011776800003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"109 1","pages":"595-602"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90314177","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 : 2023-01-01DOI: 10.5220/0011626400003414
Christian E. Pulmano, Proceso Fernandez
: The COVID-19 pandemic has emphasized the importance of timely and accurate prediction of disease outbreaks. Mathematical disease models can help simulate the trajectory of diseases and guide policymakers in identifying priorities and gaps in current policies. This study evaluates the performance, on various metrics, of three different parameter estimation algorithms in compartmental models, i.e., Nelder-Mead, Simulated Annealing, and L-BFGS-B, together with the ARIMA time-series modeling, in modeling COVID-19 cases. Using the daily number of confirmed cases of COVID-19 in the Philippines as the dataset, the models were trained on 90 different periods, with each period having 30 days of case data. After training, the models were used to predict the cases up to 30 days later. The Negative Log Likelihood (NLL), time spent, iterations per second, and memory allocation were all measured. The results show that ARIMA performed better in terms of accuracy, time, and space efficiency than each of the other algorithms. This suggests that ARIMA should be preferred for predicting the number of cases. However, policymaking sometimes requires scenario-based modeling, which ARIMA is unable to provide. For such requirements, any of the three compartmental models may be preferred, as each performed generally very well, too.
{"title":"Benchmarking Disease Modeling Techniques on the Philippines' COVID-19 Dataset","authors":"Christian E. Pulmano, Proceso Fernandez","doi":"10.5220/0011626400003414","DOIUrl":"https://doi.org/10.5220/0011626400003414","url":null,"abstract":": The COVID-19 pandemic has emphasized the importance of timely and accurate prediction of disease outbreaks. Mathematical disease models can help simulate the trajectory of diseases and guide policymakers in identifying priorities and gaps in current policies. This study evaluates the performance, on various metrics, of three different parameter estimation algorithms in compartmental models, i.e., Nelder-Mead, Simulated Annealing, and L-BFGS-B, together with the ARIMA time-series modeling, in modeling COVID-19 cases. Using the daily number of confirmed cases of COVID-19 in the Philippines as the dataset, the models were trained on 90 different periods, with each period having 30 days of case data. After training, the models were used to predict the cases up to 30 days later. The Negative Log Likelihood (NLL), time spent, iterations per second, and memory allocation were all measured. The results show that ARIMA performed better in terms of accuracy, time, and space efficiency than each of the other algorithms. This suggests that ARIMA should be preferred for predicting the number of cases. However, policymaking sometimes requires scenario-based modeling, which ARIMA is unable to provide. For such requirements, any of the three compartmental models may be preferred, as each performed generally very well, too.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"7 1","pages":"264-270"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80873797","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 : 2023-01-01DOI: 10.5220/0011689600003414
Pedro Almir Oliveira, R. Andrade, Pedro de A. Santos Neto
: In the modern world, it is no overstatement to say that “ our devices know us better than we know ourselves ”. In this sense, the vast amount of data generated by wearables, mobile devices, and environmental sensors has enabled the development of increasingly personalized and intelligent services. Among them, there is a growing interest in the delivery of medical practice using mobile devices ( i.e. , mobile health or mHealth). mHealth makes it possible to optimize healthcare systems based on continuous and transparent health monitoring, aiming to detect the emergence of diseases. However, mHealth monitoring in the real world ( i.e. , uncontrolled environment or, as labeled in this paper, “in the wild”) has many challenges. Therefore, this practical report discusses ten lessons learned from the Quality of Life (QoL) monitoring of twenty-one volunteers over three months. The main objective of this QoL monitoring was to collect data capable of training Machine Learning algorithms to infer users’ Quality of Life using the WHOQOL-BREF as a reference. During this period, our research team systematically recorded the problems faced and the strategies to overcome them. Such lessons can support researchers and practitioners in planning future studies to avoid or mitigate similar issues. In addition, we present strategies for dealing with each challenge using the 5W1H model.
{"title":"Lessons Learned from mHealth Monitoring in the Wild","authors":"Pedro Almir Oliveira, R. Andrade, Pedro de A. Santos Neto","doi":"10.5220/0011689600003414","DOIUrl":"https://doi.org/10.5220/0011689600003414","url":null,"abstract":": In the modern world, it is no overstatement to say that “ our devices know us better than we know ourselves ”. In this sense, the vast amount of data generated by wearables, mobile devices, and environmental sensors has enabled the development of increasingly personalized and intelligent services. Among them, there is a growing interest in the delivery of medical practice using mobile devices ( i.e. , mobile health or mHealth). mHealth makes it possible to optimize healthcare systems based on continuous and transparent health monitoring, aiming to detect the emergence of diseases. However, mHealth monitoring in the real world ( i.e. , uncontrolled environment or, as labeled in this paper, “in the wild”) has many challenges. Therefore, this practical report discusses ten lessons learned from the Quality of Life (QoL) monitoring of twenty-one volunteers over three months. The main objective of this QoL monitoring was to collect data capable of training Machine Learning algorithms to infer users’ Quality of Life using the WHOQOL-BREF as a reference. During this period, our research team systematically recorded the problems faced and the strategies to overcome them. Such lessons can support researchers and practitioners in planning future studies to avoid or mitigate similar issues. In addition, we present strategies for dealing with each challenge using the 5W1H model.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"45 1","pages":"155-166"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76010804","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 : 2023-01-01DOI: 10.5220/0011641700003414
Rajanikant Ghate, Sumiti Saharan, Rahee Walambe
{"title":"Predicting the Socio Economic Status of end Users of a Maternal Health App by Machine Learning","authors":"Rajanikant Ghate, Sumiti Saharan, Rahee Walambe","doi":"10.5220/0011641700003414","DOIUrl":"https://doi.org/10.5220/0011641700003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"40 1","pages":"86-93"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76185721","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 : 2023-01-01DOI: 10.5220/0011628300003414
S. Moontaha, A. Kappattanavar, Pascal Hecker, B. Arnrich
: EEG measures have become prominent with the increasing popularity of non-invasive, portable EEG sensors for neuro-physiological measures to assess cognitive load. In this paper, utilizing a four-channel wearable EEG device, the brain activity data from eleven participants were recorded while watching a relaxation video and performing three cognitive load tasks. The data was pre-processed using outlier rejection based on a movement filter, spectral filtering, common average referencing, and normalization. Four frequency-domain feature sets were extracted from 30-second windows encompassing the power of δ , θ , α , β and γ frequency bands, the respective ratios, and the asymmetry features of each band. A personalized and generalized model was built for the binary classification between the relaxation and cognitive load tasks and self-reported labels. The asymmetry feature set outperformed the band ratio feature sets with a mean classification accuracy of 81.7% for the personalized model and 78% for the generalized model. A similar result for the models from the self-reported labels necessitates utilizing asymmetry features for cognitive load classification. Extracting high-level features from asymmetry features in the future may surpass the performance. Moreover, the better performance of the personalized model leads to future work to update pre-trained generalized models on personal data.
{"title":"Wearable EEG-Based Cognitive Load Classification by Personalized and Generalized Model Using Brain Asymmetry","authors":"S. Moontaha, A. Kappattanavar, Pascal Hecker, B. Arnrich","doi":"10.5220/0011628300003414","DOIUrl":"https://doi.org/10.5220/0011628300003414","url":null,"abstract":": EEG measures have become prominent with the increasing popularity of non-invasive, portable EEG sensors for neuro-physiological measures to assess cognitive load. In this paper, utilizing a four-channel wearable EEG device, the brain activity data from eleven participants were recorded while watching a relaxation video and performing three cognitive load tasks. The data was pre-processed using outlier rejection based on a movement filter, spectral filtering, common average referencing, and normalization. Four frequency-domain feature sets were extracted from 30-second windows encompassing the power of δ , θ , α , β and γ frequency bands, the respective ratios, and the asymmetry features of each band. A personalized and generalized model was built for the binary classification between the relaxation and cognitive load tasks and self-reported labels. The asymmetry feature set outperformed the band ratio feature sets with a mean classification accuracy of 81.7% for the personalized model and 78% for the generalized model. A similar result for the models from the self-reported labels necessitates utilizing asymmetry features for cognitive load classification. Extracting high-level features from asymmetry features in the future may surpass the performance. Moreover, the better performance of the personalized model leads to future work to update pre-trained generalized models on personal data.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"35 1","pages":"41-51"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76686804","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 : 2023-01-01DOI: 10.5220/0011690500003414
María Jurado-Camino, David Chushig-Muzo, C. Soguero-Ruíz, Pablo de Miguel-Bohoyo, I. Mora-Jiménez
{"title":"On the Use of Generative Adversarial Networks to Predict Health Status Among Chronic Patients","authors":"María Jurado-Camino, David Chushig-Muzo, C. Soguero-Ruíz, Pablo de Miguel-Bohoyo, I. Mora-Jiménez","doi":"10.5220/0011690500003414","DOIUrl":"https://doi.org/10.5220/0011690500003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"46 1","pages":"167-178"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73878190","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 : 2023-01-01DOI: 10.5220/0011777300003414
Marc-Robin Gruener, Jessica Helbling, Hyung-Il Koh, Victoire Stalder, T. Kowatsch
: This paper aims to assess how the top-funded digital health companies in T1DM can create value for customers and which implications this has in terms of scalability. Med tech companies, academia, and policymakers should be able to make better strategic decisions based on the findings provided. Companies were identified using a leading venture capital database, PitchBook. Our analysis revealed that 50% of the thirty top-funded companies pursue a Layer Player strategy to generate value for T1DM patients. We recommend that companies in T1DM focus more on automated services such as conversational agents to improve scalability. In terms of scalability, many companies have room for improvement by increasingly relying on automated services, among other things.
{"title":"Top-Funded Digital Health Companies Offering Services for Type-1 Diabetes Patients: Business Models and Scalability Considerations","authors":"Marc-Robin Gruener, Jessica Helbling, Hyung-Il Koh, Victoire Stalder, T. Kowatsch","doi":"10.5220/0011777300003414","DOIUrl":"https://doi.org/10.5220/0011777300003414","url":null,"abstract":": This paper aims to assess how the top-funded digital health companies in T1DM can create value for customers and which implications this has in terms of scalability. Med tech companies, academia, and policymakers should be able to make better strategic decisions based on the findings provided. Companies were identified using a leading venture capital database, PitchBook. Our analysis revealed that 50% of the thirty top-funded companies pursue a Layer Player strategy to generate value for T1DM patients. We recommend that companies in T1DM focus more on automated services such as conversational agents to improve scalability. In terms of scalability, many companies have room for improvement by increasingly relying on automated services, among other things.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"124 1","pages":"603-608"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83519684","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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