V. D. A. Thomaz, César A. Sierra Franco, A. Raposo
Automatic polyp detection systems are an important tools to aid in the diagnosis and prevention of colorectal cancer. Currently, methods based on deep learning approaches have presented promising results. However, the performance of these techniques is highly associated with the use of large and varied data samples for training. This is one of the main limitations of applying Deep Learning techniques in the medical field since the amount of data for training is generally limited compared to nonmedical disciplines. This work proposes a novel method to increase the quantity and variability of training images from a publicly available colonoscopy dataset. The developed approach enrich the training data adding polyps to regions of nonpolypoid samples, creating automatically new data with their appropriate labels. Performance results show that convolutional neural networks trained in these syntactically-enhanced datasets improved the accuracy on polyps segmentation task while reducing the false positive rate. These results open new possibilities for advancing the study and implementation of new methods to automatically increase the number of samples in datasets for computer-assisted medical image analysis.
{"title":"Training Data Enhancements for Robust Polyp Segmentation in Colonoscopy Images","authors":"V. D. A. Thomaz, César A. Sierra Franco, A. Raposo","doi":"10.1109/CBMS.2019.00047","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00047","url":null,"abstract":"Automatic polyp detection systems are an important tools to aid in the diagnosis and prevention of colorectal cancer. Currently, methods based on deep learning approaches have presented promising results. However, the performance of these techniques is highly associated with the use of large and varied data samples for training. This is one of the main limitations of applying Deep Learning techniques in the medical field since the amount of data for training is generally limited compared to nonmedical disciplines. This work proposes a novel method to increase the quantity and variability of training images from a publicly available colonoscopy dataset. The developed approach enrich the training data adding polyps to regions of nonpolypoid samples, creating automatically new data with their appropriate labels. Performance results show that convolutional neural networks trained in these syntactically-enhanced datasets improved the accuracy on polyps segmentation task while reducing the false positive rate. These results open new possibilities for advancing the study and implementation of new methods to automatically increase the number of samples in datasets for computer-assisted medical image analysis.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"58 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":"126693889","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. Oğul, Alejandro Baldominos Gómez, Tunç Aşuroğlu, Ricardo Colomo Palacios
Sepsis is a life-threatening condition due to the reaction to an infection. With certain changes in circulatory system, sepsis may progress to septic shock if it is left untreated. Therefore, early prognosis of septic shock may facilitate implementing correct treatment and prevent more serious complications. In this study, we assess the feasibility of applying a computer-aided prognosis system for septic shock. The system is envisaged as a tool to predict septic shock at the time of sepsis onset using only vital signs which are collected routinely in intensive care units (ICUs). To this end, we evaluate the performances of computational methods that take the sequence of vital signs acquired until sepsis onset as input and report the possibility of progressing to a septic shock before any further clinical analysis is performed. Results show that an adaptation of multivariate dynamic time warping can reveal higher accuracy than other known time-series classification methods on a new dataset built from a public ICU database. We argue that the use of computational intelligence methods can promote computer-aided prognosis of septic shock in hospitalized environment to a certain degree.
{"title":"On Computer-Aided Prognosis of Septic Shock from Vital Signs","authors":"H. Oğul, Alejandro Baldominos Gómez, Tunç Aşuroğlu, Ricardo Colomo Palacios","doi":"10.1109/CBMS.2019.00028","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00028","url":null,"abstract":"Sepsis is a life-threatening condition due to the reaction to an infection. With certain changes in circulatory system, sepsis may progress to septic shock if it is left untreated. Therefore, early prognosis of septic shock may facilitate implementing correct treatment and prevent more serious complications. In this study, we assess the feasibility of applying a computer-aided prognosis system for septic shock. The system is envisaged as a tool to predict septic shock at the time of sepsis onset using only vital signs which are collected routinely in intensive care units (ICUs). To this end, we evaluate the performances of computational methods that take the sequence of vital signs acquired until sepsis onset as input and report the possibility of progressing to a septic shock before any further clinical analysis is performed. Results show that an adaptation of multivariate dynamic time warping can reveal higher accuracy than other known time-series classification methods on a new dataset built from a public ICU database. We argue that the use of computational intelligence methods can promote computer-aided prognosis of septic shock in hospitalized environment to a certain degree.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"13 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":"134478718","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}
Early disease identification through non-invasive and automatic techniques has gathered increasing interest by the scientific community in the last decades. In this context, Parkinsons Disease (PD) has received particular attention in that it is a severe and progressive neurodegenerative disease and, therefore, early diagnosis would provide more prompt and effective intervention strategies. This, in turn, would successfully influence the life expectancy of the patients. However, the acceptance of computer-based diagnosis by doctors is hampered by the black-box approach implemented by the most performing systems, such as Artificial Neural Networks and Support Vector Machines, which do not explicit the rules adopted by the system. In this context, we propose a Cartesian Genetic Programming, aimed at automatically identify PD through the analysis of handwriting performed by PD patients and healthy controls. The use of such approach is particularly interesting in that it allows to infer explicit models of classification and, at same time, to automatically identify a suitable subset of features relevant for a correct diagnosis. The approach has been evaluated on the features extracted from the handwriting samples contained in the publicly available PaHaW dataset. Experimental results show that our approach compares favorably with state-of-the-art methods and, more importantly, provides an explicit model of the classification criteria.
{"title":"Automatic Diagnosis of Parkinson Disease through Handwriting Analysis: A Cartesian Genetic Programming Approach","authors":"R. Senatore, A. D. Cioppa, A. Marcelli","doi":"10.1109/CBMS.2019.00071","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00071","url":null,"abstract":"Early disease identification through non-invasive and automatic techniques has gathered increasing interest by the scientific community in the last decades. In this context, Parkinsons Disease (PD) has received particular attention in that it is a severe and progressive neurodegenerative disease and, therefore, early diagnosis would provide more prompt and effective intervention strategies. This, in turn, would successfully influence the life expectancy of the patients. However, the acceptance of computer-based diagnosis by doctors is hampered by the black-box approach implemented by the most performing systems, such as Artificial Neural Networks and Support Vector Machines, which do not explicit the rules adopted by the system. In this context, we propose a Cartesian Genetic Programming, aimed at automatically identify PD through the analysis of handwriting performed by PD patients and healthy controls. The use of such approach is particularly interesting in that it allows to infer explicit models of classification and, at same time, to automatically identify a suitable subset of features relevant for a correct diagnosis. The approach has been evaluated on the features extracted from the handwriting samples contained in the publicly available PaHaW dataset. Experimental results show that our approach compares favorably with state-of-the-art methods and, more importantly, provides an explicit model of the classification criteria.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"11 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":"133033477","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}
S. Mariani, F. Zambonelli, Ákos Tényi, Isaac Cano, J. Roca
Clinical research and practice are rapidly changing mostly due to Information and Communication Technology, especially, as Machine Learning (ML) offers great potential for predictive and personalised medicine. Nevertheless, barriers are still existing for widespread adoption of ML tools, as highlighted by studies from the European Union. In this paper, we propose an architecture for a Decision Support System assisting clinicians in assessing health risk of patients by delivering "Risk Prediction as a Service". By leveraging standard web technologies as well as the PMML and PFA formats for exchange of trained models, we achieve ubiquitous access to predictions, ease of deployment, and seamless interoperability, while promoting collaboration.
{"title":"Risk Prediction as a Service: a DSS Architecture Promoting Interoperability and Collaboration","authors":"S. Mariani, F. Zambonelli, Ákos Tényi, Isaac Cano, J. Roca","doi":"10.1109/CBMS.2019.00069","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00069","url":null,"abstract":"Clinical research and practice are rapidly changing mostly due to Information and Communication Technology, especially, as Machine Learning (ML) offers great potential for predictive and personalised medicine. Nevertheless, barriers are still existing for widespread adoption of ML tools, as highlighted by studies from the European Union. In this paper, we propose an architecture for a Decision Support System assisting clinicians in assessing health risk of patients by delivering \"Risk Prediction as a Service\". By leveraging standard web technologies as well as the PMML and PFA formats for exchange of trained models, we achieve ubiquitous access to predictions, ease of deployment, and seamless interoperability, while promoting collaboration.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"25 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":"124503519","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}
N. Larburu, Mónica Arrúe, I. Macía, Jon Kerexeta, Naiara Muro
Digital revolution in health enables clinicians to access huge amount of data that can be exploited for decision making. However, the lack of integration of the various data sources, the existence of data sources not directly exploitable (e.g. free text, image, signals, genomic sequences) and the lack of digital data models (i.e. digital representation of the data) make such exploitation difficult. The development of effective Decision Support Systems (DSS) in concrete clinical contexts involves the development of appropriate and integrated representations of them, together with new paradigms for the exploitation, modeling and visualization of data oriented to decision-making. The European project DESIREE aims to contribute to the development of a system with these characteristics that has application to decision making by the Breast Committee. In particular, the visual analytics tool can contribute to the exploitation of clinical data in Breast Cancer.
{"title":"Breast Cancer Digital Patient Model to Capture and Visualize Real World Data","authors":"N. Larburu, Mónica Arrúe, I. Macía, Jon Kerexeta, Naiara Muro","doi":"10.1109/CBMS.2019.00132","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00132","url":null,"abstract":"Digital revolution in health enables clinicians to access huge amount of data that can be exploited for decision making. However, the lack of integration of the various data sources, the existence of data sources not directly exploitable (e.g. free text, image, signals, genomic sequences) and the lack of digital data models (i.e. digital representation of the data) make such exploitation difficult. The development of effective Decision Support Systems (DSS) in concrete clinical contexts involves the development of appropriate and integrated representations of them, together with new paradigms for the exploitation, modeling and visualization of data oriented to decision-making. The European project DESIREE aims to contribute to the development of a system with these characteristics that has application to decision making by the Breast Committee. In particular, the visual analytics tool can contribute to the exploitation of clinical data in Breast Cancer.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"37 2 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":"126067242","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}
Electrocardiogram signals are often used in medicine. An important aspect of analyzing this data is identifying and classifying the type of beat. This classification is often done through an automated algorithm. Recent advancements in neural networks and deep learning have led to high classification accuracy. However, adoption of neural network models into clinical practice is limited due to the black-box nature of the classification method. In this work, the use of variational auto encoders to learn human-interpretable encodings for the beat types is analyzed. It is demonstrated that using this method, an interpretable and explainable representation of normal and paced beats can be achieved with neural networks.
{"title":"Interpretable ECG Beat Embedding using Disentangled Variational Auto-Encoders","authors":"T. V. Steenkiste, D. Deschrijver, T. Dhaene","doi":"10.1109/CBMS.2019.00081","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00081","url":null,"abstract":"Electrocardiogram signals are often used in medicine. An important aspect of analyzing this data is identifying and classifying the type of beat. This classification is often done through an automated algorithm. Recent advancements in neural networks and deep learning have led to high classification accuracy. However, adoption of neural network models into clinical practice is limited due to the black-box nature of the classification method. In this work, the use of variational auto encoders to learn human-interpretable encodings for the beat types is analyzed. It is demonstrated that using this method, an interpretable and explainable representation of normal and paced beats can be achieved with neural networks.","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":"131178995","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}
J. R. Anaraki, Saeed Samet, M. Shehata, K. Aubrey-Bassler, E. Karami, Saba Samet, Andrew J. Smith
Portable ultrasound is increasingly used to assess jugular venous pressure (JVP) to approximate volume status in patients with congestive heart failure (CHF). Traditionally, increases in jugular venous pressure height signify increasing circulating blood volume. Emerging evidence, suggests that JVP correlates well with sonographic images of the internal jugular vein (IJV). This paper represents a preliminary investigation on the ability of cross-sectional area (CSA) of the IJV to measure relative changes in circulating blood volume. Fourteen healthy subjects had serial transverse ultrasound videos of their IJV captured while lying at five angles designed to simulate relative changes in blood volume. Ultrasound videos of the IJV were both manually and semi-automatically segmented, the CSA was measured, outliers were detected and removed, and Rotation Forest classifier was used to classify the data. By limiting the number of classes from five to three and removing outliers the accuracies improved from 59.50% to 91.05% and 62.74% to 91.89% for manual and semi-automatic segmentation, respectively. This pilot demonstrated that serial measurement of the CSA of the IJV in combination with machine learning techniques represents a viable opportunity to monitor changes in circulating blood volume in healthy subjects, setting the stage for a trial monitoring of patients with CHF.
{"title":"Detecting Relative Changes in Circulating Blood Volume using Ultrasound and Simulation","authors":"J. R. Anaraki, Saeed Samet, M. Shehata, K. Aubrey-Bassler, E. Karami, Saba Samet, Andrew J. Smith","doi":"10.1109/CBMS.2019.00063","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00063","url":null,"abstract":"Portable ultrasound is increasingly used to assess jugular venous pressure (JVP) to approximate volume status in patients with congestive heart failure (CHF). Traditionally, increases in jugular venous pressure height signify increasing circulating blood volume. Emerging evidence, suggests that JVP correlates well with sonographic images of the internal jugular vein (IJV). This paper represents a preliminary investigation on the ability of cross-sectional area (CSA) of the IJV to measure relative changes in circulating blood volume. Fourteen healthy subjects had serial transverse ultrasound videos of their IJV captured while lying at five angles designed to simulate relative changes in blood volume. Ultrasound videos of the IJV were both manually and semi-automatically segmented, the CSA was measured, outliers were detected and removed, and Rotation Forest classifier was used to classify the data. By limiting the number of classes from five to three and removing outliers the accuracies improved from 59.50% to 91.05% and 62.74% to 91.89% for manual and semi-automatic segmentation, respectively. This pilot demonstrated that serial measurement of the CSA of the IJV in combination with machine learning techniques represents a viable opportunity to monitor changes in circulating blood volume in healthy subjects, setting the stage for a trial monitoring of patients with CHF.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"12 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":"131438366","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}
Jaime Benjumea, E. Dorronzoro, J. Ropero, O. Rivera, A. Carrasco
Privacy is a major concern for breast cancer patients. When patients use mobile health applications (mHealth apps), many sensitive data are handled by the application developers. General Data Protection Regulation (GDPR) arises as a solution to privacy issues. In this paper, we analyze the privacy policy of a sample of mHealth apps for breast cancer patients, developing a scale to check if GDPR is complied. Despite privacy is a key factor in the adoption of the use of mHealth apps, the low level of compliance with the GDPR of the analyzed applications was quite surprising. Thus, application developers must be concerned about this matter.
{"title":"Privacy in Mobile Health Applications for Breast Cancer Patients","authors":"Jaime Benjumea, E. Dorronzoro, J. Ropero, O. Rivera, A. Carrasco","doi":"10.1109/CBMS.2019.00131","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00131","url":null,"abstract":"Privacy is a major concern for breast cancer patients. When patients use mobile health applications (mHealth apps), many sensitive data are handled by the application developers. General Data Protection Regulation (GDPR) arises as a solution to privacy issues. In this paper, we analyze the privacy policy of a sample of mHealth apps for breast cancer patients, developing a scale to check if GDPR is complied. Despite privacy is a key factor in the adoption of the use of mHealth apps, the low level of compliance with the GDPR of the analyzed applications was quite surprising. Thus, application developers must be concerned about this matter.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"18 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":"131534436","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 General Data Protection Regulation (GDPR) is lengthy and it is essential to resume the impacts of it in specific use-cases to diminish the gap between system developers and regulators. Computer-aided diagnosis is one of such use-cases with increased importance on clinical screening programs. The regulation has distinct mentions that affect automated-decision solutions and healthcare records. This work identifies the fundamental legal issues, challenges and opportunities for this scenario and propose architectural guidelines to tackle them. The result is purely theoretical, however it is based on known architectures such as signaling networks, already applied in the telecommunication sector.
{"title":"GDPR Impacts and Opportunities for Computer-Aided Diagnosis Guidelines and Legal Perspectives","authors":"Micael Pedrosa, C. Costa, Julian Dorado","doi":"10.1109/CBMS.2019.00128","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00128","url":null,"abstract":"The General Data Protection Regulation (GDPR) is lengthy and it is essential to resume the impacts of it in specific use-cases to diminish the gap between system developers and regulators. Computer-aided diagnosis is one of such use-cases with increased importance on clinical screening programs. The regulation has distinct mentions that affect automated-decision solutions and healthcare records. This work identifies the fundamental legal issues, challenges and opportunities for this scenario and propose architectural guidelines to tackle them. The result is purely theoretical, however it is based on known architectures such as signaling networks, already applied in the telecommunication sector.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"13 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":"123967576","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}
Laura Lopez-Perez, Liss Hernández, M. Ottaviano, E. Martinelli, T. Poli, L. Licitra, M. Arredondo, G. Fico
Head and Neck Cancer is the seventh cancer in incidence worldwide and this high mortality is due to the major cases are diagnosed in advanced stages. Currently, the selection of treatment is based on the Tumor-lymph-Nodes-Metastasis prognostic system. This system only considers a few risk factors, being inadequate due to the heterogeneity of such tumors. Within BD2Decide project, an Integrated Decision Support System is being implemented to link data coming from different disciplines with the purpose of providing the necessary information to tailor treatment and care delivery pathways to each Head and Neck Cancer patient. A clinical study with more than 1000 of patients is used to validate the system.
{"title":"BD2Decide: Big Data and Models for Personalized Head and Neck Cancer Decision Support","authors":"Laura Lopez-Perez, Liss Hernández, M. Ottaviano, E. Martinelli, T. Poli, L. Licitra, M. Arredondo, G. Fico","doi":"10.1109/CBMS.2019.00024","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00024","url":null,"abstract":"Head and Neck Cancer is the seventh cancer in incidence worldwide and this high mortality is due to the major cases are diagnosed in advanced stages. Currently, the selection of treatment is based on the Tumor-lymph-Nodes-Metastasis prognostic system. This system only considers a few risk factors, being inadequate due to the heterogeneity of such tumors. Within BD2Decide project, an Integrated Decision Support System is being implemented to link data coming from different disciplines with the purpose of providing the necessary information to tailor treatment and care delivery pathways to each Head and Neck Cancer patient. A clinical study with more than 1000 of patients is used to validate the system.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"54 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":"128488086","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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