Pub Date : 2023-01-01DOI: 10.5220/0011622900003414
Craig Leamy, Bilal Ahmad, Sarah Beecham, I. Richardson, Katie Crowley
{"title":"Launcher50+: An Android Launcher for Use by Older Adults","authors":"Craig Leamy, Bilal Ahmad, Sarah Beecham, I. Richardson, Katie Crowley","doi":"10.5220/0011622900003414","DOIUrl":"https://doi.org/10.5220/0011622900003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80533843","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: 10.5220/0011623900003414
Jin Zhao, Guocheng Wang, Daguo Huang, Yue Teng, Yichu Bai, Xudong Gao, Yi Zhou
{"title":"Development and Application of Regional Level Complete Inspection Management Platform","authors":"Jin Zhao, Guocheng Wang, Daguo Huang, Yue Teng, Yichu Bai, Xudong Gao, Yi Zhou","doi":"10.5220/0011623900003414","DOIUrl":"https://doi.org/10.5220/0011623900003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73754107","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: 10.5220/0011604300003414
L. Lella, I. Licata, C. Pristipino
{"title":"Artificial Intelligence Enabled Healthcare Ecosystem Model: AIEHEM Project","authors":"L. Lella, I. Licata, C. Pristipino","doi":"10.5220/0011604300003414","DOIUrl":"https://doi.org/10.5220/0011604300003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91457138","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: 10.5220/0011671700003414
A. Winter, Mattis Hartwig, T. Kirsten
: In this paper, we aim to predict the patient’s length of stay (LOS) after they are dismissed from the emergency department and transferred to the next hospital unit. An accurate prediction has positive effects for patients, doctors and hospital administrators. We extract a dataset of 181,797 patients from the United States and perform a set of feature engineering steps. For the prediction we use a CatBoost regression architecture with a specifically implemented loss function. The results are compared with baseline models and results from related work on other use cases. With an average absolute error of 2.36 days in the newly defined use case of post ED LOS prediction, we outperform baseline models achieve comparable results to use cases from intensive care unit LOS prediction. The approach can be used as a new baseline for further improvements of the prediction.
{"title":"Predicting Hospital Length of Stay of Patients Leaving the Emergency Department","authors":"A. Winter, Mattis Hartwig, T. Kirsten","doi":"10.5220/0011671700003414","DOIUrl":"https://doi.org/10.5220/0011671700003414","url":null,"abstract":": In this paper, we aim to predict the patient’s length of stay (LOS) after they are dismissed from the emergency department and transferred to the next hospital unit. An accurate prediction has positive effects for patients, doctors and hospital administrators. We extract a dataset of 181,797 patients from the United States and perform a set of feature engineering steps. For the prediction we use a CatBoost regression architecture with a specifically implemented loss function. The results are compared with baseline models and results from related work on other use cases. With an average absolute error of 2.36 days in the newly defined use case of post ED LOS prediction, we outperform baseline models achieve comparable results to use cases from intensive care unit LOS prediction. The approach can be used as a new baseline for further improvements of the prediction.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88675258","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":"Knowledge Graph Based Trustworthy Medical Code Recommendations","authors":"Mutahira Khalid, Asim Abbas, Hassan Sajjad, Hassan Khattak, Tahir Hameed, S. Bukhari","doi":"10.5220/0011925700003414","DOIUrl":"https://doi.org/10.5220/0011925700003414","url":null,"abstract":".","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77167029","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 : 2022-11-02DOI: 10.48550/arXiv.2211.01147
Yakini Tchouka, Jean-François Couchot, David Laiymani
Unstructured textual data is at the heart of healthcare systems. For obvious privacy reasons, these documents are not accessible to researchers as long as they contain personally identifiable information. One way to share this data while respecting the legislative framework (notably GDPR or HIPAA) is, within the medical structures, to de-identify it, i.e. to detect the personal information of a person through a Named Entity Recognition (NER) system and then replacing it to make it very difficult to associate the document with the person. The challenge is having reliable NER and substitution tools without compromising confidentiality and consistency in the document. Most of the conducted research focuses on English medical documents with coarse substitutions by not benefiting from advances in privacy. This paper shows how an efficient and differentially private de-identification approach can be achieved by strengthening the less robust de-identification method and by adapting state-of-the-art differentially private mechanisms for substitution purposes. The result is an approach for de-identifying clinical documents in French language, but also generalizable to other languages and whose robustness is mathematically proven.
{"title":"An Easy-to-use and Robust Approach for the Differentially Private De-Identification of Clinical Textual Documents","authors":"Yakini Tchouka, Jean-François Couchot, David Laiymani","doi":"10.48550/arXiv.2211.01147","DOIUrl":"https://doi.org/10.48550/arXiv.2211.01147","url":null,"abstract":"Unstructured textual data is at the heart of healthcare systems. For obvious privacy reasons, these documents are not accessible to researchers as long as they contain personally identifiable information. One way to share this data while respecting the legislative framework (notably GDPR or HIPAA) is, within the medical structures, to de-identify it, i.e. to detect the personal information of a person through a Named Entity Recognition (NER) system and then replacing it to make it very difficult to associate the document with the person. The challenge is having reliable NER and substitution tools without compromising confidentiality and consistency in the document. Most of the conducted research focuses on English medical documents with coarse substitutions by not benefiting from advances in privacy. This paper shows how an efficient and differentially private de-identification approach can be achieved by strengthening the less robust de-identification method and by adapting state-of-the-art differentially private mechanisms for substitution purposes. The result is an approach for de-identifying clinical documents in French language, but also generalizable to other languages and whose robustness is mathematically proven.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90704179","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 : 2022-01-01DOI: 10.5220/0010980500003123
Tom Lorenz, Mirco Baseniak, L. Münch, Ina Schiering, S. V. Müller
: Navigational abilities and wayfinding are important skills for participation in society. Landmark-based navigation is considered as an important basic wayfinding strategy. This strategy is used as the underlying concept for a rehabilitation training for people with topological disorientation. A digitization of this approach is proposed based on a smartphone application employing Augmented Realty concepts. This application allows to describe routes based on landmarks and a training of the defined routes. It is developed in an agile, interdisciplinary research process taking especially usability and privacy aspects into account.
{"title":"Digitization of Landmark Training for Topographical Disorientation: Opportunities of Smart Devices and Augmented Reality","authors":"Tom Lorenz, Mirco Baseniak, L. Münch, Ina Schiering, S. V. Müller","doi":"10.5220/0010980500003123","DOIUrl":"https://doi.org/10.5220/0010980500003123","url":null,"abstract":": Navigational abilities and wayfinding are important skills for participation in society. Landmark-based navigation is considered as an important basic wayfinding strategy. This strategy is used as the underlying concept for a rehabilitation training for people with topological disorientation. A digitization of this approach is proposed based on a smartphone application employing Augmented Realty concepts. This application allows to describe routes based on landmarks and a training of the defined routes. It is developed in an agile, interdisciplinary research process taking especially usability and privacy aspects into account.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74041216","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 : 2022-01-01DOI: 10.5220/0010916600003123
Janusz Wojtusiak, Negin Asadzadehzanjani
: This position paper argues the need for more details than simple statistical accuracy measures when comparing machine learning models constructed for patient outcome prediction. First, statistical accuracy measures are briefly discussed, including AROC, APRC, predictive accuracy, precision, recall, and their variants. Then, model correlation plots are introduced that compare outputs from two models. Finally, a more detailed analysis of inputs to the models is presented. The discussions are illustrated with two classification problems in predicting patient mortality and high utilization of medical services.
{"title":"Discussion on Comparing Machine Learning Models for Health Outcome Prediction","authors":"Janusz Wojtusiak, Negin Asadzadehzanjani","doi":"10.5220/0010916600003123","DOIUrl":"https://doi.org/10.5220/0010916600003123","url":null,"abstract":": This position paper argues the need for more details than simple statistical accuracy measures when comparing machine learning models constructed for patient outcome prediction. First, statistical accuracy measures are briefly discussed, including AROC, APRC, predictive accuracy, precision, recall, and their variants. Then, model correlation plots are introduced that compare outputs from two models. Finally, a more detailed analysis of inputs to the models is presented. The discussions are illustrated with two classification problems in predicting patient mortality and high utilization of medical services.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74422771","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 : 2022-01-01DOI: 10.5220/0010828000003123
Simon Geller, Sebastian Müller, S. Scheider, C. Woopen, S. Meister
: Due to new technological innovations, the increase in lifestyle products, and the digitalisation of healthcare the volume of personal health data is constantly growing. However, in order to use, re-use, and link personalised health data and, thus, unlock their potential benefits in health research, the authors of the data need to voluntarily give their informed consent. That is a major challenge to health data research, because the classic informed consent process requires the immense administrative burden to ask for consent, every time personal health data is accessed. In this paper we argue that all alternative consent models that have been developed to tackle this problem, either do not reduce administrative burdens significantly or do not conform to the informed consent ideal. That is why we used the design thinking approach to develop an alternative consent model that we call the value-based consent model . This model has the potential to reduce administrative burdens while empowering research subjects to autonomously translate their values into consent decisions.
{"title":"Value-based Consent Model: A Design Thinking Approach for Enabling Informed Consent in Medical Data Research","authors":"Simon Geller, Sebastian Müller, S. Scheider, C. Woopen, S. Meister","doi":"10.5220/0010828000003123","DOIUrl":"https://doi.org/10.5220/0010828000003123","url":null,"abstract":": Due to new technological innovations, the increase in lifestyle products, and the digitalisation of healthcare the volume of personal health data is constantly growing. However, in order to use, re-use, and link personalised health data and, thus, unlock their potential benefits in health research, the authors of the data need to voluntarily give their informed consent. That is a major challenge to health data research, because the classic informed consent process requires the immense administrative burden to ask for consent, every time personal health data is accessed. In this paper we argue that all alternative consent models that have been developed to tackle this problem, either do not reduce administrative burdens significantly or do not conform to the informed consent ideal. That is why we used the design thinking approach to develop an alternative consent model that we call the value-based consent model . This model has the potential to reduce administrative burdens while empowering research subjects to autonomously translate their values into consent decisions.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78175683","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 : 2022-01-01DOI: 10.5220/0011012800003123
Émilien Arnaud, Mahmoud Elbattah, Maxime Gignon, Gilles Dequen
: The advent of transformer models has allowed for tremendous progress in the Natural Language Processing (NLP) domain. Pretrained transformers could successfully deliver the state-of-the-art performance in a myriad of NLP tasks. This study presents an application of transformers to learn contextual embeddings from free-text triage notes, widely recorded at the emergency department. A large-scale retrospective cohort of triage notes of more than 260K records was provided by the University Hospital of Amiens-Picardy in France. We utilize a set of Bidirectional Encoder Representations from Transformers (BERT) for the French language. The quality of embeddings is empirically examined based on a set of clustering models. In this regard, we provide a comparative analysis of popular models including CamemBERT , FlauBERT , and mBART . The study could be generally regarded as an addition to the ongoing contributions of applying the BERT approach in the healthcare context.
{"title":"Learning Embeddings from Free-text Triage Notes using Pretrained Transformer Models","authors":"Émilien Arnaud, Mahmoud Elbattah, Maxime Gignon, Gilles Dequen","doi":"10.5220/0011012800003123","DOIUrl":"https://doi.org/10.5220/0011012800003123","url":null,"abstract":": The advent of transformer models has allowed for tremendous progress in the Natural Language Processing (NLP) domain. Pretrained transformers could successfully deliver the state-of-the-art performance in a myriad of NLP tasks. This study presents an application of transformers to learn contextual embeddings from free-text triage notes, widely recorded at the emergency department. A large-scale retrospective cohort of triage notes of more than 260K records was provided by the University Hospital of Amiens-Picardy in France. We utilize a set of Bidirectional Encoder Representations from Transformers (BERT) for the French language. The quality of embeddings is empirically examined based on a set of clustering models. In this regard, we provide a comparative analysis of popular models including CamemBERT , FlauBERT , and mBART . The study could be generally regarded as an addition to the ongoing contributions of applying the BERT approach in the healthcare context.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80477177","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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