This paper introduces the real-time Healthcare 4.0 system, the VILIAlert system and a new approach that we propose for the robust assessment of it's performance. The VILIAlert system alerts clinicians when a patient's tidal volume value rises above the clinically accepted level of 8 ml/kg as beyond this point (> 8 ml/kg), a patient is considered high risk of permanent damage to their lungs. In order to ensure success with the VILIAlert system, the ideal scenario is to ensure that as soon as patients in the Intensive Care Unit experience tidal volume values beyond the 8 ml/kg level, a clinical intervention can be carried out so to minimise the risk of patients ever having permanent damage. The approach has been implemented in the Intensive Care Unit at the Royal Victoria Hospital Belfast, Northern Ireland demonstrating the potential for such an approach to be used across all hospitals in the region.
{"title":"Analysing the Performance of a Real-Time Healthcare 4.0 System using Shared Frailty Time to Event Models","authors":"A. Marshall, Aleksandar Novakovic","doi":"10.1109/CBMS.2019.00129","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00129","url":null,"abstract":"This paper introduces the real-time Healthcare 4.0 system, the VILIAlert system and a new approach that we propose for the robust assessment of it's performance. The VILIAlert system alerts clinicians when a patient's tidal volume value rises above the clinically accepted level of 8 ml/kg as beyond this point (> 8 ml/kg), a patient is considered high risk of permanent damage to their lungs. In order to ensure success with the VILIAlert system, the ideal scenario is to ensure that as soon as patients in the Intensive Care Unit experience tidal volume values beyond the 8 ml/kg level, a clinical intervention can be carried out so to minimise the risk of patients ever having permanent damage. The approach has been implemented in the Intensive Care Unit at the Royal Victoria Hospital Belfast, Northern Ireland demonstrating the potential for such an approach to be used across all hospitals in the region.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122589976","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}
F. Henriksen, Rune Jensen, H. Stensland, Dag Johansen, M. Riegler, P. Halvorsen
Deep learning using neural networks is becoming more and more popular. It is frequently used in areas like video analysis, image retrieval, traffic forecast and speech recognition. In this respect, the learning and training process usually requires a lot of data. However, in many areas, data is scarce which is definitely the case in our medical application scenario, i.e., polyp detection in the gastrointestinal tract. Here, colorectal cancer is on the list of most common cancer types, and often, the cancer arises from benign, adenomatous polyps containing dysplastic cells. Detection and removal of polyps can therefore prevent the development of cancer. % Due to high cost, time consumption, patient discomfort and in-accuracy of existing procedures, researchers have started to explore systems for automatic polyp detection to assist and automate current examination procedures. Following the current gained traction for neural networks, and the typical lack of medical data, we explore how data enhancements affect the training and evaluation of the networks in terms of polyp detection accuracy and particularly if it can be used to increase the detection rate. We also experiment with how various training techniques can be used to increase performance. Our experimental results show how data enhancement and training optimization can be used to increase different aspects of the performance, but we also point out mechanisms that have no, and even a negative, effect.
{"title":"Performance of Data Enhancements and Training Optimization for Neural Network: A Polyp Detection Case Study","authors":"F. Henriksen, Rune Jensen, H. Stensland, Dag Johansen, M. Riegler, P. Halvorsen","doi":"10.1109/CBMS.2019.00067","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00067","url":null,"abstract":"Deep learning using neural networks is becoming more and more popular. It is frequently used in areas like video analysis, image retrieval, traffic forecast and speech recognition. In this respect, the learning and training process usually requires a lot of data. However, in many areas, data is scarce which is definitely the case in our medical application scenario, i.e., polyp detection in the gastrointestinal tract. Here, colorectal cancer is on the list of most common cancer types, and often, the cancer arises from benign, adenomatous polyps containing dysplastic cells. Detection and removal of polyps can therefore prevent the development of cancer. % Due to high cost, time consumption, patient discomfort and in-accuracy of existing procedures, researchers have started to explore systems for automatic polyp detection to assist and automate current examination procedures. Following the current gained traction for neural networks, and the typical lack of medical data, we explore how data enhancements affect the training and evaluation of the networks in terms of polyp detection accuracy and particularly if it can be used to increase the detection rate. We also experiment with how various training techniques can be used to increase performance. Our experimental results show how data enhancement and training optimization can be used to increase different aspects of the performance, but we also point out mechanisms that have no, and even a negative, effect.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126265472","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}
Diabetic retinopathy occurs when the blood vessels inside the retina are damaged as a result of diabetes. Early diagnosis and treatment of this disease is crucial to avoid blindness. Analysis of retinal images such as funduscopy, ultrasonography, and optical coherence tomography (OCT) is typically used in the diagnosis of diabetic retinopathy. In recent years, various automated techniques including deep learning have been used for this purpose. In this paper, we explore how to use deep transfer learning for the diagnosis of diabetic retinopathy using OCT images. We retrain existing deep learning models for this task and investigate how a retrained model can be optimized. We demonstrate that using an optimized pre-trained model as a feature extractor and training a conventional classifier on these features is an effective way to diagnose diabetic retinopathy using OCT images. We show through experiments that the proposed method outperforms similar existing methods with respect to accuracy and training time.
{"title":"Identifying Diabetic Retinopathy from OCT Images using Deep Transfer Learning with Artificial Neural Networks","authors":"K. Islam, S. Wijewickrema, S. O'Leary","doi":"10.1109/CBMS.2019.00066","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00066","url":null,"abstract":"Diabetic retinopathy occurs when the blood vessels inside the retina are damaged as a result of diabetes. Early diagnosis and treatment of this disease is crucial to avoid blindness. Analysis of retinal images such as funduscopy, ultrasonography, and optical coherence tomography (OCT) is typically used in the diagnosis of diabetic retinopathy. In recent years, various automated techniques including deep learning have been used for this purpose. In this paper, we explore how to use deep transfer learning for the diagnosis of diabetic retinopathy using OCT images. We retrain existing deep learning models for this task and investigate how a retrained model can be optimized. We demonstrate that using an optimized pre-trained model as a feature extractor and training a conventional classifier on these features is an effective way to diagnose diabetic retinopathy using OCT images. We show through experiments that the proposed method outperforms similar existing methods with respect to accuracy and training time.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"28 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":"114990545","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}
R. Sicilia, E. Cordelli, M. Merone, E. Luperto, R. Papalia, G. Iannello, P. Soda
Prostate cancer is the most common form of cancer in Western countries and there is the need to develop clinical decision support systems able to support physicians in the diagnosis of clinical relevant prostate cancer and avoid useless invasive prostate biopsies. In this respect, this paper introduces a radiomic approach that classifies the prostate cancer aggressiveness by combining Three Orthogonal Planes-Local Binary Pattern (TOP - LBP) with other texture measures. Furthermore, to combat the skewed nature of class priors, our proposal employs a data augmentation technique. The results achieved on 99 samples are up-and-coming, they favorably compare against conventional PI-RADS-based approach, and they show also the benefit given by the introduction of TOP-LBP in the radiomic signature.
{"title":"Early Radiomic Experiences in Classifying Prostate Cancer Aggressiveness using 3D Local Binary Patterns","authors":"R. Sicilia, E. Cordelli, M. Merone, E. Luperto, R. Papalia, G. Iannello, P. Soda","doi":"10.1109/CBMS.2019.00078","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00078","url":null,"abstract":"Prostate cancer is the most common form of cancer in Western countries and there is the need to develop clinical decision support systems able to support physicians in the diagnosis of clinical relevant prostate cancer and avoid useless invasive prostate biopsies. In this respect, this paper introduces a radiomic approach that classifies the prostate cancer aggressiveness by combining Three Orthogonal Planes-Local Binary Pattern (TOP - LBP) with other texture measures. Furthermore, to combat the skewed nature of class priors, our proposal employs a data augmentation technique. The results achieved on 99 samples are up-and-coming, they favorably compare against conventional PI-RADS-based approach, and they show also the benefit given by the introduction of TOP-LBP in the radiomic signature.","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":"116570890","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}
M. A. Schwertner, S. Rigo, D. A. Araújo, Allan de Barcelos Silva, B. Eskofier
This paper presents an approach for natural language question answering over a knowledge base generated by a medical texts information extraction process. The primary objective is to present a solution to help practitioners in oncology healthcare clinical environment with an intuitive method to access stored data. We identify health professional's needs in terms of information and interface with EHR systems. After that, we demonstrate a proposal to allow the integration of information extraction from clinical notes, knowledge base generation, and natural language question answering. The primary contributions are the identification of a solution to health professionals needs regarding usability in information access, and the demonstration of advantages obtained in representing health contents in a knowledge base.
{"title":"Fostering Natural Language Question Answering Over Knowledge Bases in Oncology EHR","authors":"M. A. Schwertner, S. Rigo, D. A. Araújo, Allan de Barcelos Silva, B. Eskofier","doi":"10.1109/CBMS.2019.00102","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00102","url":null,"abstract":"This paper presents an approach for natural language question answering over a knowledge base generated by a medical texts information extraction process. The primary objective is to present a solution to help practitioners in oncology healthcare clinical environment with an intuitive method to access stored data. We identify health professional's needs in terms of information and interface with EHR systems. After that, we demonstrate a proposal to allow the integration of information extraction from clinical notes, knowledge base generation, and natural language question answering. The primary contributions are the identification of a solution to health professionals needs regarding usability in information access, and the demonstration of advantages obtained in representing health contents in a knowledge base.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"24 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":"125706827","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}
Oscar Cuadros, Bruno S. Faiçal, Paulo Barbosa, B. Hamann, A. Fabro, A. Traina
Airway-centered Interstitial Fibrosis (ACIF) is a histological pattern of Interstitial lung diseases. Its diagnosis requires a multidisciplinary approach, in which diverse information, such as clinical data, computed tomography data, and lung biopsy data, is analyzed. Biopsy samples are digitized at high-resolution. Of crucial interest are broncho-and bronchiolocentric remodeling with extracellular matrix deposition. To analyze an image, specialists have to explore it at low microscope magnification, select a region of interest and export a smaller specified sub-image to be interpreted at higher magnification. This process is performed several times, requiring hours, becoming a tiresome task. We propose a method to support pathologists to identify specific patterns of ACIF in high-resolution images from lung biopsies. This can be done by a) automatic microscope magnification reduction; b) computing the probability of pixels belonging to high-density regions; c) extracting Local Binary Patterns (LBP) of the high-and low-density regions; and d) visualizing them in color. We have evaluated our method on nine high-resolution lung biopsies. We have tested the LBP features of high-and low-density regions with the kNN algorithm and obtained a classification accuracy of 94.4%, which is the highest one reported in the literature for this type of data.
{"title":"How to Automatically Identify Regions of Interest in High-Resolution Images of Lung Biopsy for Interstitial Fibrosis Diagnosis","authors":"Oscar Cuadros, Bruno S. Faiçal, Paulo Barbosa, B. Hamann, A. Fabro, A. Traina","doi":"10.1109/CBMS.2019.00118","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00118","url":null,"abstract":"Airway-centered Interstitial Fibrosis (ACIF) is a histological pattern of Interstitial lung diseases. Its diagnosis requires a multidisciplinary approach, in which diverse information, such as clinical data, computed tomography data, and lung biopsy data, is analyzed. Biopsy samples are digitized at high-resolution. Of crucial interest are broncho-and bronchiolocentric remodeling with extracellular matrix deposition. To analyze an image, specialists have to explore it at low microscope magnification, select a region of interest and export a smaller specified sub-image to be interpreted at higher magnification. This process is performed several times, requiring hours, becoming a tiresome task. We propose a method to support pathologists to identify specific patterns of ACIF in high-resolution images from lung biopsies. This can be done by a) automatic microscope magnification reduction; b) computing the probability of pixels belonging to high-density regions; c) extracting Local Binary Patterns (LBP) of the high-and low-density regions; and d) visualizing them in color. We have evaluated our method on nine high-resolution lung biopsies. We have tested the LBP features of high-and low-density regions with the kNN algorithm and obtained a classification accuracy of 94.4%, which is the highest one reported in the literature for this type of data.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122253524","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}
Gabriele Piantadosi, S. Marrone, Antonio Galli, M. Sansone, Carlo Sansone
Nowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is increasingly succeeding as a complementary methodology for breast cancer, with Computer Aided Detection/Diagnosis (CAD) systems becoming essential technological tools to provide early detection and diagnosis of tumours. Several CADs make use of machine learning, resulting in a constant design of hand-crafted features aimed at better assisting the physician. In recent years, Deep learning (DL) approaches raised in popularity in many pattern recognition tasks thanks to their ability to learn compact hierarchical features that well fit the specific task to solve. If, on one and, this characteristic suggests to explore DL suitability for biomedical image processing, on the other, it is important to take into account the physiological inheritance of the images under analysis. With this goal in mind, in this work we propose "3TP U-Net", an U-Shaped Deep Convolutional Neural Network that exploits the well-known Three Time Points approach for the lesion segmentation task. Results show that our proposal is able to outperform not only the classical (non-deep) approaches but also some very recent deep proposal, achieving a median Dice Similarity Coefficient of 61.24%.
{"title":"DCE-MRI Breast Lesions Segmentation with a 3TP U-Net Deep Convolutional Neural Network","authors":"Gabriele Piantadosi, S. Marrone, Antonio Galli, M. Sansone, Carlo Sansone","doi":"10.1109/CBMS.2019.00130","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00130","url":null,"abstract":"Nowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is increasingly succeeding as a complementary methodology for breast cancer, with Computer Aided Detection/Diagnosis (CAD) systems becoming essential technological tools to provide early detection and diagnosis of tumours. Several CADs make use of machine learning, resulting in a constant design of hand-crafted features aimed at better assisting the physician. In recent years, Deep learning (DL) approaches raised in popularity in many pattern recognition tasks thanks to their ability to learn compact hierarchical features that well fit the specific task to solve. If, on one and, this characteristic suggests to explore DL suitability for biomedical image processing, on the other, it is important to take into account the physiological inheritance of the images under analysis. With this goal in mind, in this work we propose \"3TP U-Net\", an U-Shaped Deep Convolutional Neural Network that exploits the well-known Three Time Points approach for the lesion segmentation task. Results show that our proposal is able to outperform not only the classical (non-deep) approaches but also some very recent deep proposal, achieving a median Dice Similarity Coefficient of 61.24%.","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":"127528902","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}
Paula Subías-Beltrán, Silvia Orte, E. Vargiu, Filippo Palumbo, Leonardo Angelini, Omar Abou Khaled, E. Mugellini, M. Caon
This paper presents the decision support system that has been defined and developed under the umbrella of the NESTORE project. The main goal of the proposed system is to help users in selecting coaching plans by proposing personalised recommendations based on their behaviours and preferences. Recognising such behaviours and their evolution over time is therefore a crucial element for tailoring the interaction of the system with the user. A three-layer system composed of pathways, coaching activity plans, and coaching events, constitutes the so-called coaching timeline on which the analysis is grounded. Various techniques are used to model and personalise the recommendations and feedback. Firstly, the indicators are extracted from disparate data sources, then these are modelled through a profiling system and, finally, recommendations on the pathways and coaching plans are performed through a scoring and a tagging system.
{"title":"A Decision Support System to Propose Coaching Plans for Seniors","authors":"Paula Subías-Beltrán, Silvia Orte, E. Vargiu, Filippo Palumbo, Leonardo Angelini, Omar Abou Khaled, E. Mugellini, M. Caon","doi":"10.1109/CBMS.2019.00123","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00123","url":null,"abstract":"This paper presents the decision support system that has been defined and developed under the umbrella of the NESTORE project. The main goal of the proposed system is to help users in selecting coaching plans by proposing personalised recommendations based on their behaviours and preferences. Recognising such behaviours and their evolution over time is therefore a crucial element for tailoring the interaction of the system with the user. A three-layer system composed of pathways, coaching activity plans, and coaching events, constitutes the so-called coaching timeline on which the analysis is grounded. Various techniques are used to model and personalise the recommendations and feedback. Firstly, the indicators are extracted from disparate data sources, then these are modelled through a profiling system and, finally, recommendations on the pathways and coaching plans are performed through a scoring and a tagging system.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127026516","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}
M. Bouamrane, R. Meiklem, Mark D. Dunlop, D. Kingsmore, P. Thomson, K. Stevenson, S. Greenwood
In 2013, the UK national renal registry established 57,000 adults in the UK were treated for advanced kidney failure, 23,683 (42%) receiving haemodialysis. Haemodialysis patients face some of the highest treatment burden in the National Health Service (NHS) and are among the most 'expensive' to treat. In addition, patients endure complex treatment trajectories. In this study, we have sought to gather and synthesise the opinion of clinical and Human Computer Interaction (HCI) domain experts (n=9) to establish a set of initial design requirements in order to test the feasibility of developing a digital aid (i.e. electronic haemodialysis patient portal) to support patients in the course of their treatment. Expert feedback was gathered by means of interviews and focus groups in order to instruct design requirements for a haemodialysis patient portal.
{"title":"Haemodialysis Electronic Patient Portal: A Design Requirements Analysis and Feasibility Study with Domain Experts","authors":"M. Bouamrane, R. Meiklem, Mark D. Dunlop, D. Kingsmore, P. Thomson, K. Stevenson, S. Greenwood","doi":"10.1109/CBMS.2019.00051","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00051","url":null,"abstract":"In 2013, the UK national renal registry established 57,000 adults in the UK were treated for advanced kidney failure, 23,683 (42%) receiving haemodialysis. Haemodialysis patients face some of the highest treatment burden in the National Health Service (NHS) and are among the most 'expensive' to treat. In addition, patients endure complex treatment trajectories. In this study, we have sought to gather and synthesise the opinion of clinical and Human Computer Interaction (HCI) domain experts (n=9) to establish a set of initial design requirements in order to test the feasibility of developing a digital aid (i.e. electronic haemodialysis patient portal) to support patients in the course of their treatment. Expert feedback was gathered by means of interviews and focus groups in order to instruct design requirements for a haemodialysis patient portal.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"2 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":"122251598","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}
Carol. Leroy, Yvonne Kammerer, Uwe Oestermeier, Karsten Büringer, M. Bitzer, Peter Gerjets
Experimental research investigating the processes or influencing factors of diagnostic reasoning or diagnostic success is predominantly conducted with case descriptions spanning no more than one A4 page. In this paper, we argue for a more authentic task setting in the form of multiple documents case descriptions, and make suggestions how to design them to suit different research questions. We further review methods used in previous studies, such as think-aloud protocols and written justifications of diagnoses, and discuss how they can be used in order to assess the cognitive processes underlying diagnostic reasoning in more detail. Additionally, based on findings from the field of multiple documents comprehension, we outline how participants' gaze behavior on and their interaction with the documents might also be used to assess processes of information comparison and corroboration during reading as part of participants' diagnostic reasoning process.
{"title":"Inferential Reasoning Driving Clinical Diagnosis: Suggestions for New Assessment Approaches","authors":"Carol. Leroy, Yvonne Kammerer, Uwe Oestermeier, Karsten Büringer, M. Bitzer, Peter Gerjets","doi":"10.1109/CBMS.2019.00113","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00113","url":null,"abstract":"Experimental research investigating the processes or influencing factors of diagnostic reasoning or diagnostic success is predominantly conducted with case descriptions spanning no more than one A4 page. In this paper, we argue for a more authentic task setting in the form of multiple documents case descriptions, and make suggestions how to design them to suit different research questions. We further review methods used in previous studies, such as think-aloud protocols and written justifications of diagnoses, and discuss how they can be used in order to assess the cognitive processes underlying diagnostic reasoning in more detail. Additionally, based on findings from the field of multiple documents comprehension, we outline how participants' gaze behavior on and their interaction with the documents might also be used to assess processes of information comparison and corroboration during reading as part of participants' diagnostic reasoning process.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"88 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":"132783320","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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