Pub Date : 2020-07-01DOI: 10.1109/CBMS49503.2020.00030
Xuefei Liu, Jinbao Li, M. Shu
The existing methods of epileptic seizure prediction usually analyze the electroencephalogram (EEG) signals in the time domain, frequency domain or time-frequency domain. Although some good results have been achieved, the research and utilization of spatial information is still insufficient. Moreover, some studies extracted different features for different patients and achieved good results, but these methods are not universal and robust. Different from the previous methods, this paper propose a new feature processing method of EEG signal. All electrode signals on the scalp are considered as a whole, and fusing data from different regions to obtain spatial information. Then the correlation of first derivatives is used to obtain fluctuation information of signal caused by epilepsy, which further enlarge difference of signal in different seizures stages. In addition, we also design a post-processing strategy, which uses time-series information to rectify prediction results, so that the final result is more accurate. Finally, experimental results from the CHBMIT dataset show effectiveness of proposed method and strategy, while the extensive result confirms that our method is superior to several state-of-the-art methods in recent years.
{"title":"Epileptic Seizure Prediction Based on Region Correlation of EEG Signal","authors":"Xuefei Liu, Jinbao Li, M. Shu","doi":"10.1109/CBMS49503.2020.00030","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00030","url":null,"abstract":"The existing methods of epileptic seizure prediction usually analyze the electroencephalogram (EEG) signals in the time domain, frequency domain or time-frequency domain. Although some good results have been achieved, the research and utilization of spatial information is still insufficient. Moreover, some studies extracted different features for different patients and achieved good results, but these methods are not universal and robust. Different from the previous methods, this paper propose a new feature processing method of EEG signal. All electrode signals on the scalp are considered as a whole, and fusing data from different regions to obtain spatial information. Then the correlation of first derivatives is used to obtain fluctuation information of signal caused by epilepsy, which further enlarge difference of signal in different seizures stages. In addition, we also design a post-processing strategy, which uses time-series information to rectify prediction results, so that the final result is more accurate. Finally, experimental results from the CHBMIT dataset show effectiveness of proposed method and strategy, while the extensive result confirms that our method is superior to several state-of-the-art methods in recent years.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123720499","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 : 2020-07-01DOI: 10.1109/CBMS49503.2020.00035
Leonardo Souza Silva, R. V. Aranha, Matheus A. O. Ribeiro, L. R. Nakamura, Fátima L. S. Nunes
Temporal data visualization supports planning and decision-making processes as it helps understanding patterns and relationships among time-based data. In the Healthcare area, the anamnesis procedure offers to physicians a large volume of valuable information, which is usually analyzed considering temporal aspects. Contributing to overcome the limited use of three-dimensional (3D) space, in this article we present a VR approach named 3D Block ARL to support interactive visualization of medical temporal data where the interface design is based on VA concepts. Additionally, we use a rule-based learning method to associate users' preferences to graphical elements aiming to personalize the proposed 3D visualization interface. Our results indicate that VA can be a valuable resource to improve the design of Information Visualization interface tools in the context of temporal medical data as well as to personalize the visualizations according to the preferences of users.
{"title":"Exploring Visual Attention and Machine Learning in 3D Visualization of Medical Temporal Data","authors":"Leonardo Souza Silva, R. V. Aranha, Matheus A. O. Ribeiro, L. R. Nakamura, Fátima L. S. Nunes","doi":"10.1109/CBMS49503.2020.00035","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00035","url":null,"abstract":"Temporal data visualization supports planning and decision-making processes as it helps understanding patterns and relationships among time-based data. In the Healthcare area, the anamnesis procedure offers to physicians a large volume of valuable information, which is usually analyzed considering temporal aspects. Contributing to overcome the limited use of three-dimensional (3D) space, in this article we present a VR approach named 3D Block ARL to support interactive visualization of medical temporal data where the interface design is based on VA concepts. Additionally, we use a rule-based learning method to associate users' preferences to graphical elements aiming to personalize the proposed 3D visualization interface. Our results indicate that VA can be a valuable resource to improve the design of Information Visualization interface tools in the context of temporal medical data as well as to personalize the visualizations according to the preferences of users.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125506862","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 : 2020-07-01DOI: 10.1109/CBMS49503.2020.00057
P. Deshpande, A. Rasin, Roselyne B. Tchoua, J. Furst, D. Raicu, Sameer Kiran Antani
In clinical practice, large amounts of heterogeneous medical data are generated on a daily basis. This data has the potential to be used for biomedical research and as a diagnostic reference for physicians. However, leveraging heterogeneous data for analysis requires integrating it first. Integration process includes a pre-processing data cleaning phase that eliminates inconsistencies and errors originating from each data source. In this paper, we describe a workflow for cleaning heterogeneous biomedical data sources. Our novel data cleaning approach can be applied for replacement of missing text and to improve the number of relevant cases retrieved by search queries. When the threshold for missing category replacement is met, our results show that our method achieves a missing content replacement precision of 85%, which represents an improvement of 18% over the baseline state of our datasets.
{"title":"Enhancing Recall Using Data Cleaning for Biomedical Big Data","authors":"P. Deshpande, A. Rasin, Roselyne B. Tchoua, J. Furst, D. Raicu, Sameer Kiran Antani","doi":"10.1109/CBMS49503.2020.00057","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00057","url":null,"abstract":"In clinical practice, large amounts of heterogeneous medical data are generated on a daily basis. This data has the potential to be used for biomedical research and as a diagnostic reference for physicians. However, leveraging heterogeneous data for analysis requires integrating it first. Integration process includes a pre-processing data cleaning phase that eliminates inconsistencies and errors originating from each data source. In this paper, we describe a workflow for cleaning heterogeneous biomedical data sources. Our novel data cleaning approach can be applied for replacement of missing text and to improve the number of relevant cases retrieved by search queries. When the threshold for missing category replacement is met, our results show that our method achieves a missing content replacement precision of 85%, which represents an improvement of 18% over the baseline state of our datasets.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"362 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122770929","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 : 2020-07-01DOI: 10.1109/CBMS49503.2020.00044
M. Scurti, Ernestina Menasalvas Ruiz, Maria-Esther Vidal, M. Torrente, D. Vogiatzis, G. Paliouras, M. Provencio, A. R. González
Cancer remains one of the major public health challenges worldwide. After cardiovascular diseases, cancer is one of the first causes of death and morbidity in Europe, with more than 4 million new cases and 1.9 million deaths per year. The suboptimal management of cancer patients during treatment and subsequent follows up are major obstacles in achieving better outcomes of the patients and especially regarding cost and quality of life In this paper, we present an initial data-driven approach to analyze the resources and services that are used more frequently by lung-cancer patients with the aim of identifying where the care process can be improved by paying a special attention on services before diagnosis to being able to identify possible lung-cancer patients before they are diagnosed and by reducing the length of stay in the hospital. Our approach has been built by analyzing the clinical notes of those oncological patients to extract this information and their relationships with other variables of the patient. Although the approach shown in this manuscript is very preliminary, it shows that quite interesting outcomes can be derived from further analysis.
{"title":"A Data-Driven Approach for Analyzing Healthcare Services Extracted from Clinical Records","authors":"M. Scurti, Ernestina Menasalvas Ruiz, Maria-Esther Vidal, M. Torrente, D. Vogiatzis, G. Paliouras, M. Provencio, A. R. González","doi":"10.1109/CBMS49503.2020.00044","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00044","url":null,"abstract":"Cancer remains one of the major public health challenges worldwide. After cardiovascular diseases, cancer is one of the first causes of death and morbidity in Europe, with more than 4 million new cases and 1.9 million deaths per year. The suboptimal management of cancer patients during treatment and subsequent follows up are major obstacles in achieving better outcomes of the patients and especially regarding cost and quality of life In this paper, we present an initial data-driven approach to analyze the resources and services that are used more frequently by lung-cancer patients with the aim of identifying where the care process can be improved by paying a special attention on services before diagnosis to being able to identify possible lung-cancer patients before they are diagnosed and by reducing the length of stay in the hospital. Our approach has been built by analyzing the clinical notes of those oncological patients to extract this information and their relationships with other variables of the patient. Although the approach shown in this manuscript is very preliminary, it shows that quite interesting outcomes can be derived from further analysis.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122041139","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 : 2020-07-01DOI: 10.1109/CBMS49503.2020.00029
A. Imran, Chao Huang, Hui Tang, Wei Fan, K. Cheung, M. To, Zhen Qian, D. Terzopoulos
Scoliosis is a congenital disease in which the spine is deformed from its normal shape. Radiography is the most cost-effective and accessible modality for imaging the spine. Conventional spinal assessment, diagnosis of scoliosis, and treatment planning relies on tedious and time-consuming manual analysis of spine radiographs that is susceptible to observer variation. A reliable, fully-automated method that can accurately identify vertebrae, a crucial step in image-guided scoliosis assessment, is presently unavailable in the literature. Leveraging a novel, deep-learning-based image segmentation model, we develop an end-to-end spine radiograph analysis pipeline that automatically provides an accurate segmentation and identification of the vertebrae, culminating in the reliable estimation of the Cobb angle, the most widely used measurement to quantify the magnitude of scoliosis. Our experimental results with anterior-posterior spine X-ray images indicate that our system is effective in the identification and labeling of vertebrae, and can potentially provide assistance to medical practitioners in the assessment of scoliosis.
{"title":"Fully-Automated Analysis of Scoliosis from Spinal X-Ray Images","authors":"A. Imran, Chao Huang, Hui Tang, Wei Fan, K. Cheung, M. To, Zhen Qian, D. Terzopoulos","doi":"10.1109/CBMS49503.2020.00029","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00029","url":null,"abstract":"Scoliosis is a congenital disease in which the spine is deformed from its normal shape. Radiography is the most cost-effective and accessible modality for imaging the spine. Conventional spinal assessment, diagnosis of scoliosis, and treatment planning relies on tedious and time-consuming manual analysis of spine radiographs that is susceptible to observer variation. A reliable, fully-automated method that can accurately identify vertebrae, a crucial step in image-guided scoliosis assessment, is presently unavailable in the literature. Leveraging a novel, deep-learning-based image segmentation model, we develop an end-to-end spine radiograph analysis pipeline that automatically provides an accurate segmentation and identification of the vertebrae, culminating in the reliable estimation of the Cobb angle, the most widely used measurement to quantify the magnitude of scoliosis. Our experimental results with anterior-posterior spine X-ray images indicate that our system is effective in the identification and labeling of vertebrae, and can potentially provide assistance to medical practitioners in the assessment of scoliosis.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121532429","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 : 2020-07-01DOI: 10.1109/CBMS49503.2020.00083
K. Morris, T. Giovannetti, Sarah M. Lehman
There are many use cases in the areas of cognition studies, physical therapy, and other medical related fields that stand to benefit from the ability to study the activities of individuals at home instead of a clinical environment. By monitoring their daily movements, various behavioral models can be generated that can aid in the early detection, diagnostic, and recovery processes relating to certain ailments. Many approaches to monitoring a person's behavior in the home focus on instrumenting the individual in some way, such as using a smart watch or band, and trying to determine the types of activities in which the user is engaged, such as eating, sleeping, etc. This can be burdensome to the user as it requires vigilance to ensure the device is able to perform its task. We propose a method to unobtrusively monitor a persons movements within the home to generate an activity model through the use of a smart home sensor network. Using this model, we explore various methods to measure model differences that can be used to determine when an individual's activities deviate from an established routine. Our platform, the Automatic eXtensible Inferential Occupancy Monitor, or AXIOM, allows seamless data collection from multiple sensors as well as multi-vector predictive analysis using the generated activity model.
{"title":"Towards The Use of Smart Home Sensor Networks to Generate Predictive Activity Models","authors":"K. Morris, T. Giovannetti, Sarah M. Lehman","doi":"10.1109/CBMS49503.2020.00083","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00083","url":null,"abstract":"There are many use cases in the areas of cognition studies, physical therapy, and other medical related fields that stand to benefit from the ability to study the activities of individuals at home instead of a clinical environment. By monitoring their daily movements, various behavioral models can be generated that can aid in the early detection, diagnostic, and recovery processes relating to certain ailments. Many approaches to monitoring a person's behavior in the home focus on instrumenting the individual in some way, such as using a smart watch or band, and trying to determine the types of activities in which the user is engaged, such as eating, sleeping, etc. This can be burdensome to the user as it requires vigilance to ensure the device is able to perform its task. We propose a method to unobtrusively monitor a persons movements within the home to generate an activity model through the use of a smart home sensor network. Using this model, we explore various methods to measure model differences that can be used to determine when an individual's activities deviate from an established routine. Our platform, the Automatic eXtensible Inferential Occupancy Monitor, or AXIOM, allows seamless data collection from multiple sensors as well as multi-vector predictive analysis using the generated activity model.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114740285","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 : 2020-07-01DOI: 10.1109/CBMS49503.2020.00104
Bisakh Mondal, N. Das, K. Santosh, M. Nasipuri
Identifying skin diseases, such as leprosy, Tinea Versicolor, and Vitiligo identification is one of the challenging tasks. Therefore, skin disease identification success rate is comparatively poor as compared to the other computer vision tasks. Traditional Deep Learning (DL) models are not successful in this domain due to the lack of a huge number of data. To address the problem, in the present work, we introduced a customized Generative Adversarial Network (GAN) to generate synthetic data. With data augmentation, we achieved maximum 94.25% recognition accuracy using DensenNet-121, which was 10.95% better than when no augmentation was employed. Source code is publicly available at https://github.com/DVLP-CMATERJU/SkinDiseases_GenerativeAI.git GitHub.
{"title":"Improved Skin Disease Classification Using Generative Adversarial Network","authors":"Bisakh Mondal, N. Das, K. Santosh, M. Nasipuri","doi":"10.1109/CBMS49503.2020.00104","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00104","url":null,"abstract":"Identifying skin diseases, such as leprosy, Tinea Versicolor, and Vitiligo identification is one of the challenging tasks. Therefore, skin disease identification success rate is comparatively poor as compared to the other computer vision tasks. Traditional Deep Learning (DL) models are not successful in this domain due to the lack of a huge number of data. To address the problem, in the present work, we introduced a customized Generative Adversarial Network (GAN) to generate synthetic data. With data augmentation, we achieved maximum 94.25% recognition accuracy using DensenNet-121, which was 10.95% better than when no augmentation was employed. Source code is publicly available at https://github.com/DVLP-CMATERJU/SkinDiseases_GenerativeAI.git GitHub.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133984513","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 : 2020-07-01DOI: 10.1109/CBMS49503.2020.00021
Dharitri Misra, Michael Gill, Janice S. Lee, Sameer Kiran Antani
Cone-beam computed tomography (CBCT) images are used in craniofacial research for diagnosing dentofacial deformities, skeletal malocclusion severity and to assist in virtual surgical planning. There is a need for automated guidance in predicting regions that could most benefit from surgical intervention. As a part of the effort to conduct such experiments, it is preferable to remove soft tissues in the craniofacial region in CBCT images. However, this front end "data preparation" step is non-trivial for CBCT images due to the inherent fluctuations in the intensity of tissues and bones caused by photon scattering of cone beam shaped X-rays during image acquisition. In this paper, we describe our automated segmentation approach for segmenting anterior tissues in more than 600 3D CBCT images with good result, by combining a selected set of 2D image processing techniques in conjunction with certain facial biometric parameters.
{"title":"Segmentation of Anterior Tissues in Craniofacial Cone-Beam CT Images","authors":"Dharitri Misra, Michael Gill, Janice S. Lee, Sameer Kiran Antani","doi":"10.1109/CBMS49503.2020.00021","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00021","url":null,"abstract":"Cone-beam computed tomography (CBCT) images are used in craniofacial research for diagnosing dentofacial deformities, skeletal malocclusion severity and to assist in virtual surgical planning. There is a need for automated guidance in predicting regions that could most benefit from surgical intervention. As a part of the effort to conduct such experiments, it is preferable to remove soft tissues in the craniofacial region in CBCT images. However, this front end \"data preparation\" step is non-trivial for CBCT images due to the inherent fluctuations in the intensity of tissues and bones caused by photon scattering of cone beam shaped X-rays during image acquisition. In this paper, we describe our automated segmentation approach for segmenting anterior tissues in more than 600 3D CBCT images with good result, by combining a selected set of 2D image processing techniques in conjunction with certain facial biometric parameters.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131836951","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 : 2020-07-01DOI: 10.1109/CBMS49503.2020.00036
Prasetia Utama Putra, K. Shima, Koji Shimatani
We present a Go/NoGo 3D game equipped with an eye tracker that records subjects' responses and his gaze position on the monitor over time. The proposed system consists of two functions: training that allows an instructor to modify the game's parameters and make a customized test; and evaluation in which the instructor can fix the parameters to create a standardized test. During the experiment, subjects were required to respond only to Go character by pressing a spacebar. The experimental results from 59 participants demonstrated that one's response time and its variability correlated with one's gaze behavior. Subjects with higher gaze modulation tended to respond faster and more stable. We also observed that utilizing the proposed system we could monitor the improvements in an Autism Spectrum Disorder child during his rehabilitation: his gaze modulation increased and his response time became more steady. In brief, utilizing the proposed system, we could effectively measure participants' response time variability of NoGo errors and their gaze trajectory area, which previous studies found to have a strong relationship with symptoms of mental disorders.
{"title":"Catchicken: A Serious Game Based on the Go/NoGo Task to Estimate Inattentiveness and Impulsivity Symptoms","authors":"Prasetia Utama Putra, K. Shima, Koji Shimatani","doi":"10.1109/CBMS49503.2020.00036","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00036","url":null,"abstract":"We present a Go/NoGo 3D game equipped with an eye tracker that records subjects' responses and his gaze position on the monitor over time. The proposed system consists of two functions: training that allows an instructor to modify the game's parameters and make a customized test; and evaluation in which the instructor can fix the parameters to create a standardized test. During the experiment, subjects were required to respond only to Go character by pressing a spacebar. The experimental results from 59 participants demonstrated that one's response time and its variability correlated with one's gaze behavior. Subjects with higher gaze modulation tended to respond faster and more stable. We also observed that utilizing the proposed system we could monitor the improvements in an Autism Spectrum Disorder child during his rehabilitation: his gaze modulation increased and his response time became more steady. In brief, utilizing the proposed system, we could effectively measure participants' response time variability of NoGo errors and their gaze trajectory area, which previous studies found to have a strong relationship with symptoms of mental disorders.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115717697","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 : 2020-07-01DOI: 10.1109/CBMS49503.2020.00041
M. Bampa, P. Papapetrou, J. Hollmén
We present a clustering framework for identifying patient groups with Adverse Drug Reactions from Electronic Health Records (EHRs). The increased adoption of EHRs has brought changes in the way drug safety surveillance is carried out and plays an important role in effective drug regulation. Unsupervised machine learning methods using EHRs as their input can identify patients that share common meaningful information, without the need for expert input. In this work, we propose a generalized framework that exploits the strengths of different clustering algorithms and via clustering aggregation identifies consensus patient cluster profiles. Moreover, the inherent hierarchical structure of diagnoses and medication codes is exploited. We assess the statistical significance of the produced clusterings by applying a randomization technique that keeps the data distribution margins fixed, as we are interested in evaluating information that is not conveyed by the marginal distributions. The experimental findings suggest that the framework produces medically meaningful patient groups with regard to adverse drug events by investigating two use-cases, i.e., aplastic anaemia and drug-induced skin eruption.
{"title":"A Clustering Framework for Patient Phenotyping with Application to Adverse Drug Events","authors":"M. Bampa, P. Papapetrou, J. Hollmén","doi":"10.1109/CBMS49503.2020.00041","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00041","url":null,"abstract":"We present a clustering framework for identifying patient groups with Adverse Drug Reactions from Electronic Health Records (EHRs). The increased adoption of EHRs has brought changes in the way drug safety surveillance is carried out and plays an important role in effective drug regulation. Unsupervised machine learning methods using EHRs as their input can identify patients that share common meaningful information, without the need for expert input. In this work, we propose a generalized framework that exploits the strengths of different clustering algorithms and via clustering aggregation identifies consensus patient cluster profiles. Moreover, the inherent hierarchical structure of diagnoses and medication codes is exploited. We assess the statistical significance of the produced clusterings by applying a randomization technique that keeps the data distribution margins fixed, as we are interested in evaluating information that is not conveyed by the marginal distributions. The experimental findings suggest that the framework produces medically meaningful patient groups with regard to adverse drug events by investigating two use-cases, i.e., aplastic anaemia and drug-induced skin eruption.","PeriodicalId":121059,"journal":{"name":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"483 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114647803","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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