T. Koutny, A. D. Cioppa, I. D. Falco, E. Tarantino, U. Scafuri, M. Krcma
A physiological model improves delivered healthcare, when constructing a medical device. Such a model comprises a number of parameters. While an analytical method determines model parameters, an evolutionary algorithm can improve them further. As evolutionary algorithms were designed on top of random-number generators, their results are not deterministic. This raises a concern about their applicability to medical devices. Medical-device algorithm must produce an output with a minimum guaranteed accuracy. Therefore, we applied de-randomized sequences to Meta-Differential Evolution instead of using a random-number generator. Eventually, we designed an optimization method based on zooming with derandomized sequences as an alternative to the Meta-Differential Evolution. As the experimental setup, we predicted glucose-level signal to cover a blind window of glucose-monitoring signal that results from a physiological lag in glucose transportation. Completely de-randomized differential evolution exhibited the same accuracy and precision as completely non-deterministic differential evolution. They produced 93% of glucose levels with relative error less than or equal to 15%.
{"title":"De–randomized Meta-Differential Evolution for Calculating and Predicting Glucose Levels","authors":"T. Koutny, A. D. Cioppa, I. D. Falco, E. Tarantino, U. Scafuri, M. Krcma","doi":"10.1109/CBMS.2019.00064","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00064","url":null,"abstract":"A physiological model improves delivered healthcare, when constructing a medical device. Such a model comprises a number of parameters. While an analytical method determines model parameters, an evolutionary algorithm can improve them further. As evolutionary algorithms were designed on top of random-number generators, their results are not deterministic. This raises a concern about their applicability to medical devices. Medical-device algorithm must produce an output with a minimum guaranteed accuracy. Therefore, we applied de-randomized sequences to Meta-Differential Evolution instead of using a random-number generator. Eventually, we designed an optimization method based on zooming with derandomized sequences as an alternative to the Meta-Differential Evolution. As the experimental setup, we predicted glucose-level signal to cover a blind window of glucose-monitoring signal that results from a physiological lag in glucose transportation. Completely de-randomized differential evolution exhibited the same accuracy and precision as completely non-deterministic differential evolution. They produced 93% of glucose levels with relative error less than or equal to 15%.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128321806","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}
Cassandra Williams, S. Wijewickrema, P. Piromchai, S. O'Leary
Virtual reality (VR) simulation has had various applications since its establishment as a means of replicating real-life scenarios. Surgical training is a rapidly growing area for the application of VR, and there is evidence to suggest that skills learned during VR training are transferable to the operating theatre. When developing an effective VR surgical training curriculum, practice distribution is a major factor. Research has found that distributed practice (sessions spread over time) is superior to massed practice (in one session). In this paper, we explore the effect of three forms of distributed practice (daily, weekly, and monthly) on skill retention in VR temporal bone surgery training. We show through a randomised trial that significant skill retention may be achieved irrespective of practice spacing for these three types of distributed practice if other factors of surgical education, such as task demonstration, performance feedback, and task repetition are properly incorporated into the program.
{"title":"The Effect of Practice Distribution on Skill Retention in Virtual Reality Temporal Bone Surgery Training","authors":"Cassandra Williams, S. Wijewickrema, P. Piromchai, S. O'Leary","doi":"10.1109/CBMS.2019.00101","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00101","url":null,"abstract":"Virtual reality (VR) simulation has had various applications since its establishment as a means of replicating real-life scenarios. Surgical training is a rapidly growing area for the application of VR, and there is evidence to suggest that skills learned during VR training are transferable to the operating theatre. When developing an effective VR surgical training curriculum, practice distribution is a major factor. Research has found that distributed practice (sessions spread over time) is superior to massed practice (in one session). In this paper, we explore the effect of three forms of distributed practice (daily, weekly, and monthly) on skill retention in VR temporal bone surgery training. We show through a randomised trial that significant skill retention may be achieved irrespective of practice spacing for these three types of distributed practice if other factors of surgical education, such as task demonstration, performance feedback, and task repetition are properly incorporated into the program.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124594464","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}
Deep Learning, which involves powerful black box predictors, has achieved a state-of-the-art performance in medical image analysis such as segmentation and classification for diagnosis. However, in spite of these successes, these methods focus exclusively on improving the accuracy of point predictions without assessing the quality of their outputs. Knowing how much confidence there is in a prediction is essential for gaining clinicians' trust in the technology. Monte-Carlo dropout in neural networks is equivalent to a specific variational approximation in Bayesian neural networks and is simple to implement without any changes in the network architecture. It is considered state-of-the-art for estimating uncertainty. However, in classification, it does not model the predictive probabilities. This means that we are not capturing the true underlying uncertainty in the prediction. In this paper, we propose an uncertainty estimation framework for classification by decomposing predictive probabilities into two main types of uncertainty in Bayesian modelling: aleatoric and epistemic uncertainty (representing uncertainty in the quality of the data and in the model parameters, respectively). We demonstrate that the proposed uncertainty quantification framework using the Bayesian Residual U-Net (BRUNet) provides additional insight for clinicians when analysing images with help from deep learners. In addition, we demonstrate how the resulting uncertainty depends on the dropout rates using images from nuclei in divergent medical images.
{"title":"Estimating Uncertainty in Deep Learning for Reporting Confidence to Clinicians when Segmenting Nuclei Image Data","authors":"Biraja Ghoshal, A. Tucker, B. Sanghera, W. Wong","doi":"10.1109/CBMS.2019.00072","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00072","url":null,"abstract":"Deep Learning, which involves powerful black box predictors, has achieved a state-of-the-art performance in medical image analysis such as segmentation and classification for diagnosis. However, in spite of these successes, these methods focus exclusively on improving the accuracy of point predictions without assessing the quality of their outputs. Knowing how much confidence there is in a prediction is essential for gaining clinicians' trust in the technology. Monte-Carlo dropout in neural networks is equivalent to a specific variational approximation in Bayesian neural networks and is simple to implement without any changes in the network architecture. It is considered state-of-the-art for estimating uncertainty. However, in classification, it does not model the predictive probabilities. This means that we are not capturing the true underlying uncertainty in the prediction. In this paper, we propose an uncertainty estimation framework for classification by decomposing predictive probabilities into two main types of uncertainty in Bayesian modelling: aleatoric and epistemic uncertainty (representing uncertainty in the quality of the data and in the model parameters, respectively). We demonstrate that the proposed uncertainty quantification framework using the Bayesian Residual U-Net (BRUNet) provides additional insight for clinicians when analysing images with help from deep learners. In addition, we demonstrate how the resulting uncertainty depends on the dropout rates using images from nuclei in divergent medical images.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121680392","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}
This paper reports a user study performed to assess the usability of a Web-based electronic informed consent application called DICE, which is aimed at supporting patients in the process of reading, understanding and using the informed consent as a trigger for further interaction with the team of care givers. In particular, we performed a questionnaire-based study and a series of individual semi-structured interviews to understand whether the application is usable and can be used in real-world settings, respectively. We found that patients could appreciate the availability of interactive tools like DICE, but health professionals believe that its actual adoption in current workflows and practices could be hampered by the chronic lack of time and health operators who could timely address the licit requests that such a tool could bring to light.
{"title":"Digitizing the Informed Consent: the Challenges to Design for Practices","authors":"Michela Assale, Erica Barbero, F. Cabitza","doi":"10.1109/CBMS.2019.00127","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00127","url":null,"abstract":"This paper reports a user study performed to assess the usability of a Web-based electronic informed consent application called DICE, which is aimed at supporting patients in the process of reading, understanding and using the informed consent as a trigger for further interaction with the team of care givers. In particular, we performed a questionnaire-based study and a series of individual semi-structured interviews to understand whether the application is usable and can be used in real-world settings, respectively. We found that patients could appreciate the availability of interactive tools like DICE, but health professionals believe that its actual adoption in current workflows and practices could be hampered by the chronic lack of time and health operators who could timely address the licit requests that such a tool could bring to light.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"75Suppl 2 Suppl 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129203538","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}
Available and objective clinical documents are important for research of assistant diagnosis, development of algorithms, and education. To facilitate the readability and variability of clinical documents, this paper presents a rule-based approach to develop a question-answer dataset for chest X-rays from a public collection of radiology examinations, including both images and radiologist narrative reports. Our method simplified the complicated reports via hand-selected keywords, generated more than 63 thousand question-answer pairs via hand-written patterns, and augmented the question-answer dataset to more than 130 thousand pairs via rule-based question answering. To the best of our knowledge, this is the first generated question-answer dataset for chest X-rays by rule-based method. The dataset is promising for future researches and applications such as visual question answering, computer-aided diagnosis and so on.
{"title":"Rule-Based Method to Develop Question-Answer Dataset from Chest X-Ray Reports","authors":"Jie Wang, Hairong Lv, R. Jiang, Zhen Xie","doi":"10.1109/CBMS.2019.00016","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00016","url":null,"abstract":"Available and objective clinical documents are important for research of assistant diagnosis, development of algorithms, and education. To facilitate the readability and variability of clinical documents, this paper presents a rule-based approach to develop a question-answer dataset for chest X-rays from a public collection of radiology examinations, including both images and radiologist narrative reports. Our method simplified the complicated reports via hand-selected keywords, generated more than 63 thousand question-answer pairs via hand-written patterns, and augmented the question-answer dataset to more than 130 thousand pairs via rule-based question answering. To the best of our knowledge, this is the first generated question-answer dataset for chest X-rays by rule-based method. The dataset is promising for future researches and applications such as visual question answering, computer-aided diagnosis and so on.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132260957","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}
Marjan Najafabadipour, M. Zanin, A. R. González, C. Gonzalo-Martín, B. García, V. Calvo, J. L. Cruz-Bermúdez, M. Provencio, Ernestina Menasalvas Ruiz
The widespread adoption of Electronic Health Records (EHRs) is generating an ever-increasing amount of unstructured clinical texts. Processing time expressions from these domain-specific-texts is crucial for the discovery of patterns that can help in the detection of medical events and building the patient's natural history. In medical domain, the recognition of time information from texts is challenging due to their lack of structure; usage of various formats, styles and abbreviations; their domain specific nature; writing quality; and the presence of ambiguous expressions. Furthermore, despite of Spanish occupying the second position in the world ranking of number of native speakers, to the best of our knowledge, no Natural Language Processing (NLP) tools have been introduced for the recognition of time expressions from clinical texts, written in this particular language. Therefore, in this paper, we propose a Temporal Tagger for identifying and normalizing time expressions appeared in Spanish clinical texts. We further compare our Temporal Tagger with the Spanish version of SUTime. By using a large dataset comprising EHRs of people suffering from lung cancer, we show that our developed Temporal Tagger, with an F1 score of 0.93, outperforms SUTime, with an F1 score of 0.797.
{"title":"Recognition of Time Expressions in Spanish Electronic Health Records","authors":"Marjan Najafabadipour, M. Zanin, A. R. González, C. Gonzalo-Martín, B. García, V. Calvo, J. L. Cruz-Bermúdez, M. Provencio, Ernestina Menasalvas Ruiz","doi":"10.1109/CBMS.2019.00025","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00025","url":null,"abstract":"The widespread adoption of Electronic Health Records (EHRs) is generating an ever-increasing amount of unstructured clinical texts. Processing time expressions from these domain-specific-texts is crucial for the discovery of patterns that can help in the detection of medical events and building the patient's natural history. In medical domain, the recognition of time information from texts is challenging due to their lack of structure; usage of various formats, styles and abbreviations; their domain specific nature; writing quality; and the presence of ambiguous expressions. Furthermore, despite of Spanish occupying the second position in the world ranking of number of native speakers, to the best of our knowledge, no Natural Language Processing (NLP) tools have been introduced for the recognition of time expressions from clinical texts, written in this particular language. Therefore, in this paper, we propose a Temporal Tagger for identifying and normalizing time expressions appeared in Spanish clinical texts. We further compare our Temporal Tagger with the Spanish version of SUTime. By using a large dataset comprising EHRs of people suffering from lung cancer, we show that our developed Temporal Tagger, with an F1 score of 0.93, outperforms SUTime, with an F1 score of 0.797.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133481025","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}
Jonathan S. Ramos, M. Cazzolato, Bruno S. Faiçal, M. Nogueira-Barbosa, C. Traina, A. Traina
Segmentation of medical images is critical for making several processes of analysis and classification more reliable. With the growing number of people presenting back pain and related problems, the semi-automatic segmentation and 3D reconstruction of vertebral bodies became even more important to support decision making. A 3D reconstruction allows a fast and objective analysis of each vertebrae condition, which may play a major role in surgical planning and evaluation of suitable treatments. In this paper, we propose 3DBGrowth, which develops a 3D reconstruction over the efficient Balanced Growth method for 2D images. We also take advantage of the slope coefficient from the annotation time to reduce the total number of annotated slices, reducing the time spent on manual annotation. We show experimental results on a representative dataset with 17 MRI exams demonstrating that our approach significantly outperforms the competitors and, on average, only 37% of the total slices with vertebral body content must be annotated without losing performance/accuracy. Compared to the state-of-the-art methods, we have achieved a Dice Score gain of over 5% with comparable processing time. Moreover, 3DBGrowth works well with imprecise seed points, which reduces the time spent on manual annotation by the specialist.
{"title":"3DBGrowth: Volumetric Vertebrae Segmentation and Reconstruction in Magnetic Resonance Imaging","authors":"Jonathan S. Ramos, M. Cazzolato, Bruno S. Faiçal, M. Nogueira-Barbosa, C. Traina, A. Traina","doi":"10.1109/CBMS.2019.00091","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00091","url":null,"abstract":"Segmentation of medical images is critical for making several processes of analysis and classification more reliable. With the growing number of people presenting back pain and related problems, the semi-automatic segmentation and 3D reconstruction of vertebral bodies became even more important to support decision making. A 3D reconstruction allows a fast and objective analysis of each vertebrae condition, which may play a major role in surgical planning and evaluation of suitable treatments. In this paper, we propose 3DBGrowth, which develops a 3D reconstruction over the efficient Balanced Growth method for 2D images. We also take advantage of the slope coefficient from the annotation time to reduce the total number of annotated slices, reducing the time spent on manual annotation. We show experimental results on a representative dataset with 17 MRI exams demonstrating that our approach significantly outperforms the competitors and, on average, only 37% of the total slices with vertebral body content must be annotated without losing performance/accuracy. Compared to the state-of-the-art methods, we have achieved a Dice Score gain of over 5% with comparable processing time. Moreover, 3DBGrowth works well with imprecise seed points, which reduces the time spent on manual annotation by the specialist.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130602180","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}
Millions of people worldwide suffer from neurological disorders such as epilepsy, movement disorders, and obsessive-compulsive disorder (OCD), depression, and delirium. To provide relief from these disorders, brain stimulation therapies have been shown to be effective at controlling onsets of seizures, tremors, dyskinesia, dystonia, and OCD episodes. Current development of brain stimulation therapies has pivoted toward closed-loop control of sensing onset events and correspondingly delivering adaptive stimulation. Development of closed-loop brain stimulation therapies for neurological disorders rely on the identification of neural biomarkers. As such, a brain signal monitoring system that can chronically record these neurological events is essential to the continued development of neuromodulation systems and therapies. Through analyzing clinical data, neural disorder biomarkers can be identified and novel therapies can be optimized. This paper outlines the development of a translational deep brain stimulation monitoring system utilizing Medtronic's RC+S System to help clinicians and patients accurately record and document neural disorder onset events. With this neural data, stimulation therapy parameters can be adjusted using the system without requiring an in-person office visit. The system is capable of wirelessly communicating with multiple implanted neurostimulators, monitoring disorder onset biomarkers, and periodically downloading real-time brain signal data as well as loop recordings triggered by device-detected disorder onset events. This translational system and neural disorder onset data can be used to optimize therapies, minimize symptom onsets, enable episodic care management, and improve chronic care management.
{"title":"A Translational Wireless Deep Brain Stimulation Monitoring System for Chronic Brain Signal Recording to Automate Neural Disorder Onset Recording","authors":"William Drew, T. Denison, S. Stanslaski","doi":"10.1109/CBMS.2019.00027","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00027","url":null,"abstract":"Millions of people worldwide suffer from neurological disorders such as epilepsy, movement disorders, and obsessive-compulsive disorder (OCD), depression, and delirium. To provide relief from these disorders, brain stimulation therapies have been shown to be effective at controlling onsets of seizures, tremors, dyskinesia, dystonia, and OCD episodes. Current development of brain stimulation therapies has pivoted toward closed-loop control of sensing onset events and correspondingly delivering adaptive stimulation. Development of closed-loop brain stimulation therapies for neurological disorders rely on the identification of neural biomarkers. As such, a brain signal monitoring system that can chronically record these neurological events is essential to the continued development of neuromodulation systems and therapies. Through analyzing clinical data, neural disorder biomarkers can be identified and novel therapies can be optimized. This paper outlines the development of a translational deep brain stimulation monitoring system utilizing Medtronic's RC+S System to help clinicians and patients accurately record and document neural disorder onset events. With this neural data, stimulation therapy parameters can be adjusted using the system without requiring an in-person office visit. The system is capable of wirelessly communicating with multiple implanted neurostimulators, monitoring disorder onset biomarkers, and periodically downloading real-time brain signal data as well as loop recordings triggered by device-detected disorder onset events. This translational system and neural disorder onset data can be used to optimize therapies, minimize symptom onsets, enable episodic care management, and improve chronic care management.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133273348","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}
Health is reaching a point where machines are more accurate than humans, or at least as accurate but with less effort, in more and more applications. However, accuracy alone is not enough, explanation and understanding is equally important to clinicians, governments, and patients. Possibly leading to loss of health benefits potentially realized through increasingly accurate algorithms. However, various techniques exist for auditing machine learning systems via insightful visualisations. Modelling best practices, parallel computations and open source technologies facilitate implementation of these techniques. This paper leverages several of these methods to increase interpretability for a black-box clinical risk calculator, hopefully opening the door to a better adoption of modern machine learning pipelines in the healthcare sector.
{"title":"Inspecting a Machine Learning Based Clinical Risk Calculator: A Practical Perspective","authors":"Q. Thurier, Ning Hua, L. Boyle, A. Spyker","doi":"10.1109/CBMS.2019.00073","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00073","url":null,"abstract":"Health is reaching a point where machines are more accurate than humans, or at least as accurate but with less effort, in more and more applications. However, accuracy alone is not enough, explanation and understanding is equally important to clinicians, governments, and patients. Possibly leading to loss of health benefits potentially realized through increasingly accurate algorithms. However, various techniques exist for auditing machine learning systems via insightful visualisations. Modelling best practices, parallel computations and open source technologies facilitate implementation of these techniques. This paper leverages several of these methods to increase interpretability for a black-box clinical risk calculator, hopefully opening the door to a better adoption of modern machine learning pipelines in the healthcare sector.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121774779","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}
Digital biofeedback technologies are used in physical rehabilitation to improve motor learning and enhance engagement with therapies, but they are unfrequently used in breast cancer rehabilitation. Digital biofeedback interventions should be custom-made for the specific breast cancer context. The WHO ICF Core Set for Breast Cancer describes this context by itemising the biopsychosocial and environmental factors associated with breast cancer. We analysed this Core Set to identify opportunities for biofeedback intervention, and to make recommendations for successful, inclusive design of digital biofeedback interventions in breast cancer rehabilitation. Impairments of strength, joint movement and upper limb function present opportunities for the development of digital biofeedback interventions. Factors related to sensory loss, lymphoedema, chemotherapy-related cognitive dysfunction and fatigue should be considered when designing and evaluating biofeedback systems.
{"title":"Biofeedback in Breast Cancer Rehabilitation: Applying the WHO ICF Core Set to Identify Opportunities and Recommendations","authors":"Louise Brennan, E. Dorronzoro, B. Caulfield","doi":"10.1109/CBMS.2019.00122","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00122","url":null,"abstract":"Digital biofeedback technologies are used in physical rehabilitation to improve motor learning and enhance engagement with therapies, but they are unfrequently used in breast cancer rehabilitation. Digital biofeedback interventions should be custom-made for the specific breast cancer context. The WHO ICF Core Set for Breast Cancer describes this context by itemising the biopsychosocial and environmental factors associated with breast cancer. We analysed this Core Set to identify opportunities for biofeedback intervention, and to make recommendations for successful, inclusive design of digital biofeedback interventions in breast cancer rehabilitation. Impairments of strength, joint movement and upper limb function present opportunities for the development of digital biofeedback interventions. Factors related to sensory loss, lymphoedema, chemotherapy-related cognitive dysfunction and fatigue should be considered when designing and evaluating biofeedback systems.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127311463","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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