A variety of deep learning architectures have been developed for the goal of predictive modelling in regards to detecting health diagnoses in medical records. Several models have placed strong emphases on temporal attention mechanisms and decay factors as a means to include highly temporally relevant information regarding the recency of medical event occurrence while facilitating medical code-level interpretability. In this study we utilise such models with a novel Electronic Patient Record (EPR) data set consisting of both diagnoses and medication data for the purpose of Adverse Drug Event (ADE) prediction. As such, a main contribution of this work is an empirical evaluation of two state-of-the-art deep learning architectures in terms of objective performance metrics for ADE prediction. We also assess the importance of attention mechanisms in regards to their usefulness for medical code-level interpretability, which may facilitate novel insights pertaining to the nature of ADE occurrence within the health care domain.
{"title":"An Investigation of Interpretable Deep Learning for Adverse Drug Event Prediction","authors":"J. Rebane, Isak Karlsson, P. Papapetrou","doi":"10.1109/CBMS.2019.00075","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00075","url":null,"abstract":"A variety of deep learning architectures have been developed for the goal of predictive modelling in regards to detecting health diagnoses in medical records. Several models have placed strong emphases on temporal attention mechanisms and decay factors as a means to include highly temporally relevant information regarding the recency of medical event occurrence while facilitating medical code-level interpretability. In this study we utilise such models with a novel Electronic Patient Record (EPR) data set consisting of both diagnoses and medication data for the purpose of Adverse Drug Event (ADE) prediction. As such, a main contribution of this work is an empirical evaluation of two state-of-the-art deep learning architectures in terms of objective performance metrics for ADE prediction. We also assess the importance of attention mechanisms in regards to their usefulness for medical code-level interpretability, which may facilitate novel insights pertaining to the nature of ADE occurrence within the health care domain.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122728078","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}
FAIR principles and the Open Data initiatives have motivated the publication of large volumes of data. Specifically, in the biomedical domain, the size of the data has increased exponentially in the last decade, and with the advances in the technologies to collect and generate data, a faster growth rate is expected for the next years. The available collections of data are characterized by the dominant dimensions of big data, i.e., they are not only large in volume, but they can be also heterogeneous and present quality issues. These data complexity problems impact on the typical tasks of data management, and particularly, in the task of integrating big biomedical data sources. We tackle the problem of big data integration and present a knowledge-driven framework able to extract and integrate data collected from structured and unstructured data sources. The proposed framework resorts to Natural Language Processing techniques to extract knowledge from unstructured data and short text. Furthermore, ontologies and controlled vocabularies, e.g., UMLS, are utilized to annotate the extracted entities and relations with terms from the ontology or controlled vocabulary. The annotated data is integrated into a knowledge graph. A unified schema is used to describe the meaning of the integrated data as well as the main properties and relations. As proof of concept, we show the results of applying the proposed framework to integrate clinical records from lung cancer patients with data extracted from open data sources like Drugbank and PubMed. The created knowledge graph enables the discovery of interactions between drugs in the treatments prescribed to lung cancer patients.
{"title":"Semantic Data Integration Techniques for Transforming Big Biomedical Data into Actionable Knowledge","authors":"Maria-Esther Vidal, S. Jozashoori","doi":"10.1109/CBMS.2019.00116","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00116","url":null,"abstract":"FAIR principles and the Open Data initiatives have motivated the publication of large volumes of data. Specifically, in the biomedical domain, the size of the data has increased exponentially in the last decade, and with the advances in the technologies to collect and generate data, a faster growth rate is expected for the next years. The available collections of data are characterized by the dominant dimensions of big data, i.e., they are not only large in volume, but they can be also heterogeneous and present quality issues. These data complexity problems impact on the typical tasks of data management, and particularly, in the task of integrating big biomedical data sources. We tackle the problem of big data integration and present a knowledge-driven framework able to extract and integrate data collected from structured and unstructured data sources. The proposed framework resorts to Natural Language Processing techniques to extract knowledge from unstructured data and short text. Furthermore, ontologies and controlled vocabularies, e.g., UMLS, are utilized to annotate the extracted entities and relations with terms from the ontology or controlled vocabulary. The annotated data is integrated into a knowledge graph. A unified schema is used to describe the meaning of the integrated data as well as the main properties and relations. As proof of concept, we show the results of applying the proposed framework to integrate clinical records from lung cancer patients with data extracted from open data sources like Drugbank and PubMed. The created knowledge graph enables the discovery of interactions between drugs in the treatments prescribed to lung cancer patients.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127571794","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}
A. Teles, I. Rodrigues, Davi Viana, Francisco Silva, L. Coutinho, M. Endler, R. A. Rabelo
Depression is a mental disorder characterized by persistent sadness, loss of interest, and a set of behavioral changes. The high prevalence of depression imposes a significant burden on the world population, demanding methods capable of monitoring and treating this mental disorder. Currently, a large number of mobile applications have been designed to provide support to depressive people. This paper aims to identify, analyze and characterize the current state of mobile applications focused on depression. To do so, we conducted a systematic review of applications for depression assistance. The two most popular mobile app stores (Google Play Store and Apple App Store) have been explored to find the most relevant apps. After applying the inclusion and exclusion criteria and performing the quality assessment of the results, 216 applications were selected for the data extraction phase, where we summarized their benefits and limitations and identified gaps and trends. The results of this review evidenced that there is a growth in the diversity of apps' purposes such as chatbot, online therapy, educational tools, mood tracker, testing, and self-help.
抑郁症是一种精神障碍，其特征是持续悲伤，失去兴趣和一系列行为改变。抑郁症的高患病率给世界人口带来了沉重负担，需要能够监测和治疗这种精神障碍的方法。目前，大量的移动应用程序被设计为为抑郁症患者提供支持。本文旨在识别、分析和描述当前专注于抑郁症的移动应用程序的状态。为此，我们对抑郁症援助申请进行了系统审查。我们在两个最流行的手机应用商店(Google Play Store和Apple app Store)中寻找最相关的应用。在应用纳入和排除标准并对结果进行质量评估后，我们选择了216个应用程序进入数据提取阶段，在此阶段我们总结了它们的优点和局限性，并确定了差距和趋势。这项调查的结果证明，应用程序的用途越来越多样化，比如聊天机器人、在线治疗、教育工具、情绪追踪器、测试和自助。
{"title":"Mobile Mental Health: A Review of Applications for Depression Assistance","authors":"A. Teles, I. Rodrigues, Davi Viana, Francisco Silva, L. Coutinho, M. Endler, R. A. Rabelo","doi":"10.1109/CBMS.2019.00143","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00143","url":null,"abstract":"Depression is a mental disorder characterized by persistent sadness, loss of interest, and a set of behavioral changes. The high prevalence of depression imposes a significant burden on the world population, demanding methods capable of monitoring and treating this mental disorder. Currently, a large number of mobile applications have been designed to provide support to depressive people. This paper aims to identify, analyze and characterize the current state of mobile applications focused on depression. To do so, we conducted a systematic review of applications for depression assistance. The two most popular mobile app stores (Google Play Store and Apple App Store) have been explored to find the most relevant apps. After applying the inclusion and exclusion criteria and performing the quality assessment of the results, 216 applications were selected for the data extraction phase, where we summarized their benefits and limitations and identified gaps and trends. The results of this review evidenced that there is a growth in the diversity of apps' purposes such as chatbot, online therapy, educational tools, mood tracker, testing, and self-help.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115839037","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}
Jongwoo Kim, L. Tran, E. Chew, Sameer Kiran Antani
Glaucoma is one of the most common eye diseases that can cause irreversible vision loss due to damage to the optic nerve. Ophthalmologists consider a cup to optic disc ratio greater than 0.3 to be suggestive of glaucoma. Unfortunately, there is high variability among ophthalmologists in estimating the ratio since it is not easy to reliably measure optic disc and cup areas in a fundus image. Therefore, this paper proposes automatic methods to segment the optic disc and cup areas. There are two steps to estimate the ratio: region of interest (ROI) area detection (where optic disc is in the center) from a fundus image, followed by optic disc and cup segmentation. This paper focuses on automated methods to segment the optic disc and cup from the ROI. Fully convolutional networks (FCN) with U-Net architectures are used for the segmentation. The RIGA dataset (composed of three different fundus image datasets: MESSIDOR, Bin Rushed, and Magrabi), containing 750 fundus images, is used to train and test the FCNs. Our proposed FCNs show relatively better performance than other existing algorithms. The best segmentation results for optic disc show 0.95 Jaccard index, 0.98 F-measure, and 0.99 accuracy. The best segmentation results for cup show 0.80 Jaccard index, 0.88 F-measure, and 0.99 accuracy.
{"title":"Optic Disc and Cup Segmentation for Glaucoma Characterization Using Deep Learning","authors":"Jongwoo Kim, L. Tran, E. Chew, Sameer Kiran Antani","doi":"10.1109/CBMS.2019.00100","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00100","url":null,"abstract":"Glaucoma is one of the most common eye diseases that can cause irreversible vision loss due to damage to the optic nerve. Ophthalmologists consider a cup to optic disc ratio greater than 0.3 to be suggestive of glaucoma. Unfortunately, there is high variability among ophthalmologists in estimating the ratio since it is not easy to reliably measure optic disc and cup areas in a fundus image. Therefore, this paper proposes automatic methods to segment the optic disc and cup areas. There are two steps to estimate the ratio: region of interest (ROI) area detection (where optic disc is in the center) from a fundus image, followed by optic disc and cup segmentation. This paper focuses on automated methods to segment the optic disc and cup from the ROI. Fully convolutional networks (FCN) with U-Net architectures are used for the segmentation. The RIGA dataset (composed of three different fundus image datasets: MESSIDOR, Bin Rushed, and Magrabi), containing 750 fundus images, is used to train and test the FCNs. Our proposed FCNs show relatively better performance than other existing algorithms. The best segmentation results for optic disc show 0.95 Jaccard index, 0.98 F-measure, and 0.99 accuracy. The best segmentation results for cup show 0.80 Jaccard index, 0.88 F-measure, and 0.99 accuracy.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"54 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126767887","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}
Patients with Parkinson disease (PD) exhibit a gait disorder called festinating gait which is caused by deficiency of dopamine in the basal ganglia. To analyze gait of patients with PD, different spatiotemporal parameters such as stride length, cadence, and walking speed should be calculated. This paper aims to present a method to extract useful information represented by the positions of certain landmarks on the human body that can be used for analysis of PD patients' gait. This method is tested using 132 videos collected from 7 PD patients and 7 healthy controls. The positions of 4 body landmarks, namely body's center of gravity (COG), the position of the head, and the position of the feet, was computed using a total of more than 41000 of video frames. Results of object's movement plots show high level of accuracy in the calculation of the body landmarks.
{"title":"Extracting Body Landmarks from Videos for Parkinson Gait Analysis","authors":"H. Fleyeh, J. Westin","doi":"10.1109/CBMS.2019.00082","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00082","url":null,"abstract":"Patients with Parkinson disease (PD) exhibit a gait disorder called festinating gait which is caused by deficiency of dopamine in the basal ganglia. To analyze gait of patients with PD, different spatiotemporal parameters such as stride length, cadence, and walking speed should be calculated. This paper aims to present a method to extract useful information represented by the positions of certain landmarks on the human body that can be used for analysis of PD patients' gait. This method is tested using 132 videos collected from 7 PD patients and 7 healthy controls. The positions of 4 body landmarks, namely body's center of gravity (COG), the position of the head, and the position of the feet, was computed using a total of more than 41000 of video frames. Results of object's movement plots show high level of accuracy in the calculation of the body landmarks.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126909820","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}
A. Masino, Daniel Forsyth, H. Nuske, J. Herrington, Jeffrey W. Pennington, Yelena Kushleyeva, Christopher P. Bonafide
Consumer-grade wearables provide physiological measurements which may inform m-health applications that predict adverse outcomes. Autism Spectrum Disorder (ASD) represents a compelling example. Many individuals with ASD present with challenging behaviors that are preceded by physiological changes. Physiological measures could, therefore, support real-time interventions to avert challenging behaviors in various social settings. However, no prior research has demonstrated a methodological approach to detect these changes using wearable device data. We sought to demonstrate a machine learning approach that uses wearables data to differentiate physiological states associated with stressful and non-stressful scenarios in children with ASD. In a controlled laboratory setting, we collected heart rate and RR interval measurements during rest and during activities designed to mimic stress using a consumer-grade wearable device. Our analysis included 38 participants (22 ASD, 16 non-ASD). Following outlier removal, we extracted 20 statistical features from data collected during each patient's rest and stressful periods. Using nested leave-one-out cross-validation over 76 sample periods (38 rest / 38 stress), we trained and evaluated logistic regression (LR) and support vector machine (SVM) classifiers to label each validation sample as a rest or stressful period. The SVM and LR models achieved 93% and 87% accuracy, respectively. These results suggest that machine learning models combined with wearables data may support real-time m-health intervention applications.
{"title":"m-Health and Autism: Recognizing Stress and Anxiety with Machine Learning and Wearables Data","authors":"A. Masino, Daniel Forsyth, H. Nuske, J. Herrington, Jeffrey W. Pennington, Yelena Kushleyeva, Christopher P. Bonafide","doi":"10.1109/CBMS.2019.00144","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00144","url":null,"abstract":"Consumer-grade wearables provide physiological measurements which may inform m-health applications that predict adverse outcomes. Autism Spectrum Disorder (ASD) represents a compelling example. Many individuals with ASD present with challenging behaviors that are preceded by physiological changes. Physiological measures could, therefore, support real-time interventions to avert challenging behaviors in various social settings. However, no prior research has demonstrated a methodological approach to detect these changes using wearable device data. We sought to demonstrate a machine learning approach that uses wearables data to differentiate physiological states associated with stressful and non-stressful scenarios in children with ASD. In a controlled laboratory setting, we collected heart rate and RR interval measurements during rest and during activities designed to mimic stress using a consumer-grade wearable device. Our analysis included 38 participants (22 ASD, 16 non-ASD). Following outlier removal, we extracted 20 statistical features from data collected during each patient's rest and stressful periods. Using nested leave-one-out cross-validation over 76 sample periods (38 rest / 38 stress), we trained and evaluated logistic regression (LR) and support vector machine (SVM) classifiers to label each validation sample as a rest or stressful period. The SVM and LR models achieved 93% and 87% accuracy, respectively. These results suggest that machine learning models combined with wearables data may support real-time m-health intervention applications.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128323313","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}
Sandro Luís Freire de Castro Silva, N. Antônio, Marcelo Fornazin, R. Santos
There are several challenges to support healthcare context; however, it is necessary to investigate a current reality in the Computer-Based Medical Systems (CBMS) field: the incorporation of emergent systems. This paper presents a review on how emergent systems have been investigated in last decade in the context of CBMS conference series. Results show that CBMS strategies should consider that these systems cannot be treated as something simple and that a deepest analysis can show its real complexity.
{"title":"Looking for Emergent Systems in Computer-Based Medical Systems: A Review from the Last Decade","authors":"Sandro Luís Freire de Castro Silva, N. Antônio, Marcelo Fornazin, R. Santos","doi":"10.1109/CBMS.2019.00055","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00055","url":null,"abstract":"There are several challenges to support healthcare context; however, it is necessary to investigate a current reality in the Computer-Based Medical Systems (CBMS) field: the incorporation of emergent systems. This paper presents a review on how emergent systems have been investigated in last decade in the context of CBMS conference series. Results show that CBMS strategies should consider that these systems cannot be treated as something simple and that a deepest analysis can show its real complexity.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125343604","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}
Hearing Loss (HL) is a highly prevalent chronic disease (the 5th cause of disability world-wide), which increases the risk of cognitive decline, mental illness, and depression, and furthermore leads to social isolation, unemployment/early retirement, loss of income and work discrimination. To enable successful holistic management of HL, appropriate public health policies for HL prevention, early diagnosis, long-term treatment and rehabilitation are required. In addition, HL management would benefit from detection and prevention of cognitive decline; protection from noise; and initiatives for socioeconomic inclusion of HL patients. However, the evidence for forming such policies and enabling true holistic HL management is lacking. Specifically, holistic HL management policies require access to and analysis of heterogeneous data sources. In EVOTION, such big data from five different clinical organizations are available and continuous acquisition of real-time data produced by sensors and hearing aids used by HL patients will support their continuous update. In order to utilize these data in forming holistic HL management policies, EVOTION is developing an integrated IT platform supporting: 1) the acquisition and analysis of heterogeneous big data related to HL; 2) policy decision making, i.e. selection of effective interventions related to the holistic management of HL based on the outcomes of 1) and the formulation of related public health policies; and 3) specification and continuous monitoring of the effects of such policies in a sustainable manner.
{"title":"EVOTION – Big Data Supporting Public Hearing Health Policies","authors":"J. Christensen, N. H. Pontoppidan","doi":"10.1109/CBMS.2019.00012","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00012","url":null,"abstract":"Hearing Loss (HL) is a highly prevalent chronic disease (the 5th cause of disability world-wide), which increases the risk of cognitive decline, mental illness, and depression, and furthermore leads to social isolation, unemployment/early retirement, loss of income and work discrimination. To enable successful holistic management of HL, appropriate public health policies for HL prevention, early diagnosis, long-term treatment and rehabilitation are required. In addition, HL management would benefit from detection and prevention of cognitive decline; protection from noise; and initiatives for socioeconomic inclusion of HL patients. However, the evidence for forming such policies and enabling true holistic HL management is lacking. Specifically, holistic HL management policies require access to and analysis of heterogeneous data sources. In EVOTION, such big data from five different clinical organizations are available and continuous acquisition of real-time data produced by sensors and hearing aids used by HL patients will support their continuous update. In order to utilize these data in forming holistic HL management policies, EVOTION is developing an integrated IT platform supporting: 1) the acquisition and analysis of heterogeneous big data related to HL; 2) policy decision making, i.e. selection of effective interventions related to the holistic management of HL based on the outcomes of 1) and the formulation of related public health policies; and 3) specification and continuous monitoring of the effects of such policies in a sustainable manner.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121817252","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}
Ricardo Wandré Dias Pedro, Ariane Machado-Lima, Fátima L. S. Nunes
Breast cancer is one of the most common cancers that affect women worldwide being responsible for about 15% of all deaths related to cancer in the world. Mammography is one of the main techniques to help early detection of breast cancer. Although there are some characteristics that should be considered to discriminate benign and malignant masses, only about 15 to 30% of the cases sent to biopsies are malignant. To aid in the diagnosis of this disease, several CAD systems were proposed and developed to make a second opinion to the physicians, but the theory of formal languages is underexplored in this field. This paper presents a new syntactic approach to discriminate benign and malignant masses in digital mammography. Preliminary results showed that this approach is very promising, since our classifier achieved accuracies from 80% to 100% depending on the model and features used, applied on two different databases.
{"title":"A New Syntactic Approach for Masses Classification in Digital Mammograms","authors":"Ricardo Wandré Dias Pedro, Ariane Machado-Lima, Fátima L. S. Nunes","doi":"10.1109/CBMS.2019.00083","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00083","url":null,"abstract":"Breast cancer is one of the most common cancers that affect women worldwide being responsible for about 15% of all deaths related to cancer in the world. Mammography is one of the main techniques to help early detection of breast cancer. Although there are some characteristics that should be considered to discriminate benign and malignant masses, only about 15 to 30% of the cases sent to biopsies are malignant. To aid in the diagnosis of this disease, several CAD systems were proposed and developed to make a second opinion to the physicians, but the theory of formal languages is underexplored in this field. This paper presents a new syntactic approach to discriminate benign and malignant masses in digital mammography. Preliminary results showed that this approach is very promising, since our classifier achieved accuracies from 80% to 100% depending on the model and features used, applied on two different databases.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128292991","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}
BigO (bigoprogram.eu) is an EU-funded project that collects objective evidence on the causes of obesity in local communities and helps public health authorities design effective counter obesity interventions. A novel technological platform is being built relying on mobile devices and sensors for data acquisition combined with big data analytics and visualization. During the 4 year project duration the BigO platform will be used by 9000 school and age-matched obese children and adolescents as sources for community data. Led by Aristotle University of Thessaloniki, the project brings together schools, health and clinical scientists, technology providers, personal health solutions businesses and mobile communication providers in Greece, Sweden, Ireland, Spain and the Netherlands.
{"title":"Big Data Against Childhood Obesity, the BigO Project","authors":"A. Delopoulos","doi":"10.1109/CBMS.2019.00023","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00023","url":null,"abstract":"BigO (bigoprogram.eu) is an EU-funded project that collects objective evidence on the causes of obesity in local communities and helps public health authorities design effective counter obesity interventions. A novel technological platform is being built relying on mobile devices and sensors for data acquisition combined with big data analytics and visualization. During the 4 year project duration the BigO platform will be used by 9000 school and age-matched obese children and adolescents as sources for community data. Led by Aristotle University of Thessaloniki, the project brings together schools, health and clinical scientists, technology providers, personal health solutions businesses and mobile communication providers in Greece, Sweden, Ireland, Spain and the Netherlands.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129784106","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：