Alejandro Ruiz-de-laCuadra, J. L. L. Cuadrado, I. González-Carrasco, B. Ruíz-Mezcua
Time expression recognition is one of the open issues in Natural Language Processing. These expressions are relevant to determine temporal aspects of the text as well as to establish relationships among facts described in said text. In the clinical domain, the temporal aspects are relevant to determine, for example, a sequence of facts in a clinical history. This paper presents research on the recognition of time expressions in Spanish according to the TIMEX3 standard. First, we establish HeidelTime, a well-known state of the art rule-based system, as a reference. Next, a hybrid model (a combination of bidirectional LSTM, CNN and CRF) is introduced to try to improve the results for the Spanish language. Both architectures have been tested with a Timex3 annotated Spanish corpus (TIMEBANK 1.0) to compare them. First, the results obtained show that the neural architecture obtains better results in Spanish. Finally, the neural architecture has been tested on a corpus of Clinical Notes (English and Spanish) in order to determine the results on this domain.
{"title":"Sequence Time Expression Recognition in the Spanish Clinical Narrative","authors":"Alejandro Ruiz-de-laCuadra, J. L. L. Cuadrado, I. González-Carrasco, B. Ruíz-Mezcua","doi":"10.1109/CBMS.2019.00074","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00074","url":null,"abstract":"Time expression recognition is one of the open issues in Natural Language Processing. These expressions are relevant to determine temporal aspects of the text as well as to establish relationships among facts described in said text. In the clinical domain, the temporal aspects are relevant to determine, for example, a sequence of facts in a clinical history. This paper presents research on the recognition of time expressions in Spanish according to the TIMEX3 standard. First, we establish HeidelTime, a well-known state of the art rule-based system, as a reference. Next, a hybrid model (a combination of bidirectional LSTM, CNN and CRF) is introduced to try to improve the results for the Spanish language. Both architectures have been tested with a Timex3 annotated Spanish corpus (TIMEBANK 1.0) to compare them. First, the results obtained show that the neural architecture obtains better results in Spanish. Finally, the neural architecture has been tested on a corpus of Clinical Notes (English and Spanish) in order to determine the results on this domain.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124384034","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}
César Antonio Ortiz Toro, Nuria Gutiérrez Sánchez, C. Gonzalo-Martín, Roberto Garrido García, A. R. González, Ernestina Menasalvas Ruiz
Alzheimer's disease (AD) is characterized by a progressive deterioration of cognitive and behavioral functions as a result of the atrophy of specific regions of the brain. It is estimated that by 2050 there will be 131.5 million people affected. Thus, there is an urgent need to find biological markers for its early detection and monitoring. In this work, it is present an analysis of textural radiomics features extracted from a gray matter probability volume, in a set of individual subcortical regions, from a number of different atlases, to identify subject with AD in a MRI. Also, significant subcortical regions for AD detection have been identified using a ReliefF relevance test. Experimental results using the ADNI1 database have proven the potential of some of the tested radiomic features as possible biomarkers for AD/CN differentiation.
{"title":"Radiomics Textural Features Extracted from Subcortical Structures of Grey Matter Probability for Alzheimers Disease Detection.","authors":"César Antonio Ortiz Toro, Nuria Gutiérrez Sánchez, C. Gonzalo-Martín, Roberto Garrido García, A. R. González, Ernestina Menasalvas Ruiz","doi":"10.1109/CBMS.2019.00084","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00084","url":null,"abstract":"Alzheimer's disease (AD) is characterized by a progressive deterioration of cognitive and behavioral functions as a result of the atrophy of specific regions of the brain. It is estimated that by 2050 there will be 131.5 million people affected. Thus, there is an urgent need to find biological markers for its early detection and monitoring. In this work, it is present an analysis of textural radiomics features extracted from a gray matter probability volume, in a set of individual subcortical regions, from a number of different atlases, to identify subject with AD in a MRI. Also, significant subcortical regions for AD detection have been identified using a ReliefF relevance test. Experimental results using the ADNI1 database have proven the potential of some of the tested radiomic features as possible biomarkers for AD/CN differentiation.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122000548","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}
Diabetes Mellitus is a life-long health condition that causes a high concentration of glucose in the blood. The foundation of the treatment and prevention of diabetes lays on reeducation and medication to prevent complications caused by high levels of glucose in the blood. New methods, products, and services, like those provided by the Ambient Assisted Living (AAL) domain, for supporting activities of the daily life of people with chronic diseases are effective to assist the management of diabetes disease. Particularly, Health Care Supportive Home (HSH) systems can provide an autonomous life in their residence to patients and supply them with autonomy through the use of different types of technologies. We present the software architecture of DiaManT@Home, a service-oriented HSH system that assists patients diagnosed with diabetes mellitus, and that enhances the self-management of their condition at home. DiaManT@Home was constructing following a well-established process based on the instantiation of a reference architecture for HSH systems. The software architecture of DiaManT@Home was assessed regarding the completeness of its software elements to achieve functional and non-functional requirements using prototypes. Results showed that DiaManT@Home can be considered as a complete solution to an HSH system for assisting patients at home. As future works, DiaManT@Home will be coded following the architecture presented in this work.
{"title":"Software Architecture for Health Care Supportive Home Systems to Assist Patients with Diabetes Mellitus","authors":"Lina Garcés, I. Vicente, E. Nakagawa","doi":"10.1109/CBMS.2019.00060","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00060","url":null,"abstract":"Diabetes Mellitus is a life-long health condition that causes a high concentration of glucose in the blood. The foundation of the treatment and prevention of diabetes lays on reeducation and medication to prevent complications caused by high levels of glucose in the blood. New methods, products, and services, like those provided by the Ambient Assisted Living (AAL) domain, for supporting activities of the daily life of people with chronic diseases are effective to assist the management of diabetes disease. Particularly, Health Care Supportive Home (HSH) systems can provide an autonomous life in their residence to patients and supply them with autonomy through the use of different types of technologies. We present the software architecture of DiaManT@Home, a service-oriented HSH system that assists patients diagnosed with diabetes mellitus, and that enhances the self-management of their condition at home. DiaManT@Home was constructing following a well-established process based on the instantiation of a reference architecture for HSH systems. The software architecture of DiaManT@Home was assessed regarding the completeness of its software elements to achieve functional and non-functional requirements using prototypes. Results showed that DiaManT@Home can be considered as a complete solution to an HSH system for assisting patients at home. As future works, DiaManT@Home will be coded following the architecture presented in this work.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123076197","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}
H. Abbas, L. Alic, M. Rios, M. Abdul-Ghani, K. Qaraqe
In this paper, we revisit the data of the San Antonio Heart Study, and employ machine learning to predict the future development of type-2 diabetes. To build the prediction model, we use the support vector machines and ten features that are wellknown in the literature as strong predictors of future diabetes. Due to the unbalanced nature of the dataset in terms of the class labels, we use 10-fold cross-validation to train the model and a hold-out set to validate it. The results of this study show a validation accuracy of 84.1% with a recall rate of 81.1% averaged over 100 iterations. The outcomes of this study can help in identifying the population that is at high risk of developing type-2 diabetes in the future.
{"title":"Predicting Diabetes in Healthy Population through Machine Learning","authors":"H. Abbas, L. Alic, M. Rios, M. Abdul-Ghani, K. Qaraqe","doi":"10.1109/CBMS.2019.00117","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00117","url":null,"abstract":"In this paper, we revisit the data of the San Antonio Heart Study, and employ machine learning to predict the future development of type-2 diabetes. To build the prediction model, we use the support vector machines and ten features that are wellknown in the literature as strong predictors of future diabetes. Due to the unbalanced nature of the dataset in terms of the class labels, we use 10-fold cross-validation to train the model and a hold-out set to validate it. The results of this study show a validation accuracy of 84.1% with a recall rate of 81.1% averaged over 100 iterations. The outcomes of this study can help in identifying the population that is at high risk of developing type-2 diabetes in the future.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124612040","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}
Alejandro Baldominos Gómez, H. Oğul, Ricardo Colomo Palacios
Being able to detect an infection at an early stage is a clinical problem of the utmost importance. An infection not diagnosed on time might not only severely affect the infected patient's health, but also to spread and start a focus of contagion to other people. In this paper, we propose a clinical decision support system to automatically diagnose infections using physiological signals from the patients. The focus of the system is put on being able to deal with very small amounts of data (one aggregated record per patient and day), which eases the potential of the system in environments with low resources. Data has been acquired between April 2018 and January 2019 in two nursing homes in Spain, where nurses had also tested patients for infections. Machine learning models have then been created by aggregating measurements from days prior to the infection (lead) and after the infection started (lag) in order to generate features. The best model attained reports an AUROC of 0.722, using data from up to two days after the infection started. Interestingly, an AUROC of up to 0.692 is achieved when infection prognosis is considered; i.e., using only measurements prior to the manual recording of the infection to compose the dataset.
{"title":"Infection Diagnosis using Biomedical Signals in Small Data Scenarios","authors":"Alejandro Baldominos Gómez, H. Oğul, Ricardo Colomo Palacios","doi":"10.1109/CBMS.2019.00018","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00018","url":null,"abstract":"Being able to detect an infection at an early stage is a clinical problem of the utmost importance. An infection not diagnosed on time might not only severely affect the infected patient's health, but also to spread and start a focus of contagion to other people. In this paper, we propose a clinical decision support system to automatically diagnose infections using physiological signals from the patients. The focus of the system is put on being able to deal with very small amounts of data (one aggregated record per patient and day), which eases the potential of the system in environments with low resources. Data has been acquired between April 2018 and January 2019 in two nursing homes in Spain, where nurses had also tested patients for infections. Machine learning models have then been created by aggregating measurements from days prior to the infection (lead) and after the infection started (lag) in order to generate features. The best model attained reports an AUROC of 0.722, using data from up to two days after the infection started. Interestingly, an AUROC of up to 0.692 is achieved when infection prognosis is considered; i.e., using only measurements prior to the manual recording of the infection to compose the dataset.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130654259","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}
Igor W. S. Falcão, D. Souza, Fabiola Araujo, J. Fernandes, Y. Pires, D. Cardoso, Marcos C. R. Seruffo
The development of systems that aim to spread public information, for example, public awareness campaigns for spotting the signs and symptoms of infectious diseases and how to prevent them, should be addressed by human-computer interaction, or more specifically, by devising interfaces that have a high degree of usability or user-friendliness. This paper examines a serious game that aims at providing people with the means of preventing diseases transmitted by the Aedes aegypti mosquito. An empirical test with groups of users was carried out to validate and observe the benefits of the game. The first group answered some questions about the diseases and how to combat the Aedes aegypti proliferation without playing the game and the second group answered the same questions after having played the game. The results showed a significant increase in the knowledge of Aedes aegypti and the diseases transmitted by it. As well as seeking to play the game to spread information about this disease, the purpose of this study is to understand the user's experience by interacting with a serious game and assisting in the evaluation of the usability and the cognitive benefits of serious games.
{"title":"Usability and Cognitive Benefits of a Serious Game to Combat Aedes Aegypti Mosquito","authors":"Igor W. S. Falcão, D. Souza, Fabiola Araujo, J. Fernandes, Y. Pires, D. Cardoso, Marcos C. R. Seruffo","doi":"10.1109/CBMS.2019.00145","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00145","url":null,"abstract":"The development of systems that aim to spread public information, for example, public awareness campaigns for spotting the signs and symptoms of infectious diseases and how to prevent them, should be addressed by human-computer interaction, or more specifically, by devising interfaces that have a high degree of usability or user-friendliness. This paper examines a serious game that aims at providing people with the means of preventing diseases transmitted by the Aedes aegypti mosquito. An empirical test with groups of users was carried out to validate and observe the benefits of the game. The first group answered some questions about the diseases and how to combat the Aedes aegypti proliferation without playing the game and the second group answered the same questions after having played the game. The results showed a significant increase in the knowledge of Aedes aegypti and the diseases transmitted by it. As well as seeking to play the game to spread information about this disease, the purpose of this study is to understand the user's experience by interacting with a serious game and assisting in the evaluation of the usability and the cognitive benefits of serious games.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132930198","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}
Silvia Panicacci, M. Donati, L. Fanucci, I. Bellini, F. Profili, P. Francesconi
Heart failure patients have become an important challenge for the healthcare system, since they represent a medical, social and economic problem. Early heart failure diagnoses can be very useful to improve patients' quality of life and to reduce the resources consumption, but they can be complex for the general practitioners. Data mining and machine learning techniques can really help in this field. The aim of this study is to validate some machine learning models to identify heart failure patients, starting from administrative data, and to make them transparent and interpretable. Despite the lack of clinical data, not available in Italy, but the most employed for the identification of heart failure patients, the results are comparable with the state-of-the-art ones and the models outperform the performances already obtained in Tuscany.
{"title":"Exploring Machine Learning Algorithms to Identify Heart Failure Patients: the Tuscany Region Case Study","authors":"Silvia Panicacci, M. Donati, L. Fanucci, I. Bellini, F. Profili, P. Francesconi","doi":"10.1109/CBMS.2019.00088","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00088","url":null,"abstract":"Heart failure patients have become an important challenge for the healthcare system, since they represent a medical, social and economic problem. Early heart failure diagnoses can be very useful to improve patients' quality of life and to reduce the resources consumption, but they can be complex for the general practitioners. Data mining and machine learning techniques can really help in this field. The aim of this study is to validate some machine learning models to identify heart failure patients, starting from administrative data, and to make them transparent and interpretable. Despite the lack of clinical data, not available in Italy, but the most employed for the identification of heart failure patients, the results are comparable with the state-of-the-art ones and the models outperform the performances already obtained in Tuscany.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133425830","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}
Gerardo Lagunes García, Lucía Prieto Santamaría, Eduardo P. García del Valle, M. Zanin, Ernestina Menasalvas Ruiz, A. R. González
Nowadays there is a huge amount of medical information that can be retrieved from different sources, both structured and unstructured. Internet has plenty of textual sources with medical knowledge (books, scientific papers, specialized web pages, etc.), but not all of them are publicly available. Wikipedia is a free, open and worldwide accessible source of knowledge. It contains more than 150,000 articles of medical content in the form of texts (non-structured information) that can be mined. The aim of this work is to study whether the evolution of information contained in Wikipedia medical articles can be used in a research context. The study has been focused on extracting the elements, from Wikipedia disease articles, that can be used to guide a diagnosis process, support the creation of diagnostic systems, or analyze the similarities between diseases, among others.
{"title":"Wikipedia Disease Articles: An Analysis of their Content and Evolution","authors":"Gerardo Lagunes García, Lucía Prieto Santamaría, Eduardo P. García del Valle, M. Zanin, Ernestina Menasalvas Ruiz, A. R. González","doi":"10.1109/CBMS.2019.00136","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00136","url":null,"abstract":"Nowadays there is a huge amount of medical information that can be retrieved from different sources, both structured and unstructured. Internet has plenty of textual sources with medical knowledge (books, scientific papers, specialized web pages, etc.), but not all of them are publicly available. Wikipedia is a free, open and worldwide accessible source of knowledge. It contains more than 150,000 articles of medical content in the form of texts (non-structured information) that can be mined. The aim of this work is to study whether the evolution of information contained in Wikipedia medical articles can be used in a research context. The study has been focused on extracting the elements, from Wikipedia disease articles, that can be used to guide a diagnosis process, support the creation of diagnostic systems, or analyze the similarities between diseases, among others.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130131497","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}
Atherosclerosis is a multifactorial disease that affects a significant number of people during their lifetime. It is a pathology clinically silent for years that develops with a gradual thickening of the vessel walls and a consecutive formation of plaque. This is the cause of several dangerous conditions such as ischemic stroke, the most common cause of stroke in middle-aged people. To avoid and reduce these events a continuos and meticulous monitoring of patients with any carotid diseases is necessary. This paper presents an objective measure of the progression of a carotid patology. An index capable of distinguishing between the initial state of thickening of the carotid arterial walls and the successive presence of more serious plaque has been defined. The presence of thickening or plaque is estimated by evaluating Heart Rate Variability. This is a non-invasive approach, able to estimate characteristic parameters in an easy and efficient way, constituting an accurate and optimum instrument for a real-time continuous monitoring. Locally Weighted Learning has been used to automatically find a relationship between these parameters and the presence of a disorder, tested on an available dataset.
{"title":"An Objective Measure of Carotid Disease Based on a Multiparameter Approach","authors":"Laura Verde, G. Pietro","doi":"10.1109/CBMS.2019.00121","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00121","url":null,"abstract":"Atherosclerosis is a multifactorial disease that affects a significant number of people during their lifetime. It is a pathology clinically silent for years that develops with a gradual thickening of the vessel walls and a consecutive formation of plaque. This is the cause of several dangerous conditions such as ischemic stroke, the most common cause of stroke in middle-aged people. To avoid and reduce these events a continuos and meticulous monitoring of patients with any carotid diseases is necessary. This paper presents an objective measure of the progression of a carotid patology. An index capable of distinguishing between the initial state of thickening of the carotid arterial walls and the successive presence of more serious plaque has been defined. The presence of thickening or plaque is estimated by evaluating Heart Rate Variability. This is a non-invasive approach, able to estimate characteristic parameters in an easy and efficient way, constituting an accurate and optimum instrument for a real-time continuous monitoring. Locally Weighted Learning has been used to automatically find a relationship between these parameters and the presence of a disorder, tested on an available dataset.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114137455","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}
The paper presents the Pulse project, a European project founded under the Horizon 2020 program.
这篇论文介绍了Pulse项目，这是一个在“地平线2020”计划下成立的欧洲项目。
{"title":"PULSE: Participatory Urban Living for Sustainable Environment","authors":"M. Ottaviano, M. Cabrera-Umpiérrez, M. Arredondo","doi":"10.1109/CBMS.2019.00022","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00022","url":null,"abstract":"The paper presents the Pulse project, a European project founded under the Horizon 2020 program.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"278 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134261960","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：