Pub Date : 2022-07-01DOI: 10.1109/CBMS55023.2022.00061
Dehua Feng, Xi Chen, Xiaoyu Wang, Jiahuan Lv, Lin Bai, Shu Zhang, Zhiguo Zhou
In medical image classification, uncertainty estimation providing confidence of decision is part of interpretability of prediction model. Based on estimated uncertainty, physicians can pick out cases with high uncertainty for further inspection. However, in this uncertainty-informed decision referral, models may make wrong predictions with high certainty which leads to omission of false predictions. Therefore, we propose a method to set up a model which could make correct prediction with low uncertainty and wrong prediction with high uncertainty. We integrate uncertainty estimation into training phase and design a novel loss function “penalized entropy” by penalizing wrong but certain samples to improve the models' certainty performance. Experiments were conducted on three datasets: optical coherence tomography (OCT) image dataset for anti-vascular endothelial growth factor (anti- VEGF) effectiveness classification, OCT image dataset for diagnostic classification, and chest X-ray dataset for pneumonia classification. Performances were evaluated on both accuracy metrics such as accuracy, sensitivity, specificity, area under the curve (AVC), and certainty metrics which are accuracy vs. uncertainty (AvV), probability of correct results among certain predictions (PCC), and probability of uncertain results among wrong predictions (PUW). Results show that the method using the proposed loss function can achieve better or comparable accuracy and state-of-the-art certainty performance.
{"title":"Penalized Entropy: a novel loss function for uncertainty estimation and optimization in medical image classification","authors":"Dehua Feng, Xi Chen, Xiaoyu Wang, Jiahuan Lv, Lin Bai, Shu Zhang, Zhiguo Zhou","doi":"10.1109/CBMS55023.2022.00061","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00061","url":null,"abstract":"In medical image classification, uncertainty estimation providing confidence of decision is part of interpretability of prediction model. Based on estimated uncertainty, physicians can pick out cases with high uncertainty for further inspection. However, in this uncertainty-informed decision referral, models may make wrong predictions with high certainty which leads to omission of false predictions. Therefore, we propose a method to set up a model which could make correct prediction with low uncertainty and wrong prediction with high uncertainty. We integrate uncertainty estimation into training phase and design a novel loss function “penalized entropy” by penalizing wrong but certain samples to improve the models' certainty performance. Experiments were conducted on three datasets: optical coherence tomography (OCT) image dataset for anti-vascular endothelial growth factor (anti- VEGF) effectiveness classification, OCT image dataset for diagnostic classification, and chest X-ray dataset for pneumonia classification. Performances were evaluated on both accuracy metrics such as accuracy, sensitivity, specificity, area under the curve (AVC), and certainty metrics which are accuracy vs. uncertainty (AvV), probability of correct results among certain predictions (PCC), and probability of uncertain results among wrong predictions (PUW). Results show that the method using the proposed loss function can achieve better or comparable accuracy and state-of-the-art certainty performance.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132527024","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 : 2022-07-01DOI: 10.1109/CBMS55023.2022.00065
S. Zhong, Yuxuan Wu, Zhantao Liu, Zhaohong Pan, Bingsheng Huang, Qinqin Yang
The identification of lesions is critical for the diagnostic evaluation of prostate cancer. 68Ga-PSMA-11 PET/CT is a specific imaging for prostate cancer. However, this is extremely challenging considering the large number of lesions of varying size and uptake that may be distributed in various anatomical settings with different backgrounds throughout the body. In this paper, we propose a deep learning approach for automatic detection of whole-body prostate cancer lesions on PSMA imaging. We established and evaluated our model on the 68Ga-PSMA-11 PET/CT image dataset of 107 patients with metastatic prostate cancer, and finally obtained Precision, Recall and F1-score of 82.9%, 100% and 90.6%, respectively, on the independent test set. Preliminary tests confirmed the potential of our method for disease detection on a systemic scale. Increasing the amount of training data can further improve the performance of the proposed deep learning method.
{"title":"Automatic Detection of Prostate Cancer Systemic Lesions Based on Deep Learning and 68Ga-PSMA-11 PET/CT","authors":"S. Zhong, Yuxuan Wu, Zhantao Liu, Zhaohong Pan, Bingsheng Huang, Qinqin Yang","doi":"10.1109/CBMS55023.2022.00065","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00065","url":null,"abstract":"The identification of lesions is critical for the diagnostic evaluation of prostate cancer. 68Ga-PSMA-11 PET/CT is a specific imaging for prostate cancer. However, this is extremely challenging considering the large number of lesions of varying size and uptake that may be distributed in various anatomical settings with different backgrounds throughout the body. In this paper, we propose a deep learning approach for automatic detection of whole-body prostate cancer lesions on PSMA imaging. We established and evaluated our model on the 68Ga-PSMA-11 PET/CT image dataset of 107 patients with metastatic prostate cancer, and finally obtained Precision, Recall and F1-score of 82.9%, 100% and 90.6%, respectively, on the independent test set. Preliminary tests confirmed the potential of our method for disease detection on a systemic scale. Increasing the amount of training data can further improve the performance of the proposed deep learning method.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130894535","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 : 2022-07-01DOI: 10.1109/CBMS55023.2022.00066
Abhinandan Panda, Srinivas Pinisetty, P. Roop
Diabetes is a global health threat, and its prevalence is rising at an alarming rate. Diabetes is the cause of severe complications in vital organs of the body. So, diabetes must be detected early for timely treatment and to prevent the condition from escalating to severe consequences. Many AI and machine learning approaches have been proposed for the non-invasive continuous monitoring of diabetes. However, using such informal methods in healthcare monitoring raises concerns about reliability. Furthermore, deploying an AI-based solution to continuously monitor a person's health state on resource-constrained embedded devices is a concern. We overcome these shortcomings in this work by proposing a formal runtime monitoring system for the first time for diabetes detection using Electrocardiogram (ECG) sensing. We implement a data mining model from the ECG features to infer ECG policies and thereby synthesize a formal verification monitor based on the policies. Using a diabetes dataset, we evaluate the verification monitor's performance compared to other proposed models.
{"title":"Policy-Based Diabetes Detection using Formal Runtime Verification Monitors","authors":"Abhinandan Panda, Srinivas Pinisetty, P. Roop","doi":"10.1109/CBMS55023.2022.00066","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00066","url":null,"abstract":"Diabetes is a global health threat, and its prevalence is rising at an alarming rate. Diabetes is the cause of severe complications in vital organs of the body. So, diabetes must be detected early for timely treatment and to prevent the condition from escalating to severe consequences. Many AI and machine learning approaches have been proposed for the non-invasive continuous monitoring of diabetes. However, using such informal methods in healthcare monitoring raises concerns about reliability. Furthermore, deploying an AI-based solution to continuously monitor a person's health state on resource-constrained embedded devices is a concern. We overcome these shortcomings in this work by proposing a formal runtime monitoring system for the first time for diabetes detection using Electrocardiogram (ECG) sensing. We implement a data mining model from the ECG features to infer ECG policies and thereby synthesize a formal verification monitor based on the policies. Using a diabetes dataset, we evaluate the verification monitor's performance compared to other proposed models.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115627901","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 : 2022-07-01DOI: 10.1109/CBMS55023.2022.00075
Semin Kim, Huisu Yoon, Jonghan Lee, S. Yoo
Facial wrinkle is very important in measuring aging. Image processing-based methods have been proposed for wrinkle detection, but their performance was not enough because wrinkles have a wide variety of thickness, shape, orientation, and vague boundaries. Recently, deep learning-based methods have been widely applied in the field of image recognition with a lot of labeled image dataset. To extend this technology to facial wrinkle detection, labeling work for wrinkles to generate ground truth is very important. However, it is difficult to label wrinkles accurately because of the wide variety. In this paper, we propose a semiautomatic labeling strategy incorporating a texture map and a deep learning model. Specifically, the proposed method extracted the texture map from an original image and removed non-wrinkle textures on the map by multiplying with a roughly labeled wrinkle mask. Then, the map is converted into ground truth by thresholding. Using the ground truth, a deep learning model was trained with the original image and the texture map. The trained model was evaluated with facial images obtained from real skin diagnosis devices, and the results showed superior performance to those of existing image processing-based methods.
{"title":"Semi-automatic Labeling and Training Strategy for Deep Learning-based Facial Wrinkle Detection","authors":"Semin Kim, Huisu Yoon, Jonghan Lee, S. Yoo","doi":"10.1109/CBMS55023.2022.00075","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00075","url":null,"abstract":"Facial wrinkle is very important in measuring aging. Image processing-based methods have been proposed for wrinkle detection, but their performance was not enough because wrinkles have a wide variety of thickness, shape, orientation, and vague boundaries. Recently, deep learning-based methods have been widely applied in the field of image recognition with a lot of labeled image dataset. To extend this technology to facial wrinkle detection, labeling work for wrinkles to generate ground truth is very important. However, it is difficult to label wrinkles accurately because of the wide variety. In this paper, we propose a semiautomatic labeling strategy incorporating a texture map and a deep learning model. Specifically, the proposed method extracted the texture map from an original image and removed non-wrinkle textures on the map by multiplying with a roughly labeled wrinkle mask. Then, the map is converted into ground truth by thresholding. Using the ground truth, a deep learning model was trained with the original image and the texture map. The trained model was evaluated with facial images obtained from real skin diagnosis devices, and the results showed superior performance to those of existing image processing-based methods.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"2674 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122537572","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 : 2022-07-01DOI: 10.1109/CBMS55023.2022.00079
Laura Carolina Martínez Esmeral, A. Uhl
In this paper, we provide an overview of techniques for human subject identification from biomedical signals, highlighting the potential threat for patient privacy considering public repositories of medical data. After an in-depth review of lesser known approaches, we conclude that performing a disentanglement and elimination of the identity related attributes from the medical image data is a potential solution for this problem.
{"title":"Patient identification methods based on medical imagery and their impact on patient privacy and open medical data","authors":"Laura Carolina Martínez Esmeral, A. Uhl","doi":"10.1109/CBMS55023.2022.00079","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00079","url":null,"abstract":"In this paper, we provide an overview of techniques for human subject identification from biomedical signals, highlighting the potential threat for patient privacy considering public repositories of medical data. After an in-depth review of lesser known approaches, we conclude that performing a disentanglement and elimination of the identity related attributes from the medical image data is a potential solution for this problem.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131444876","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 : 2022-07-01DOI: 10.1109/CBMS55023.2022.00078
Liang Xiao
Clinical decision-making is closely related with the activity of argumentation among alternative options. In recent years, theories and languages have been developed for argumentation and evidence-based decision support. However, a systematic study of argument representation using evidence in the medicine domain is missing. In this paper, an Evidence-based Argumentation Graph is proposed. A Clinical Argumentation scheme and a Patient Preference Argumentation scheme guide their construction. Arguments can be represented using clinical and patient preference evidence and semantically integrated in the graph. Clinical decision support is delivered to clinicians and patients together. The method is demonstrated using a case study of decision support for patients suspected with breast cancer.
{"title":"Towards Evidence-based Argumentation Graph for Clinical Decision Support","authors":"Liang Xiao","doi":"10.1109/CBMS55023.2022.00078","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00078","url":null,"abstract":"Clinical decision-making is closely related with the activity of argumentation among alternative options. In recent years, theories and languages have been developed for argumentation and evidence-based decision support. However, a systematic study of argument representation using evidence in the medicine domain is missing. In this paper, an Evidence-based Argumentation Graph is proposed. A Clinical Argumentation scheme and a Patient Preference Argumentation scheme guide their construction. Arguments can be represented using clinical and patient preference evidence and semantically integrated in the graph. Clinical decision support is delivered to clinicians and patients together. The method is demonstrated using a case study of decision support for patients suspected with breast cancer.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116993963","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 : 2022-05-30DOI: 10.1109/CBMS55023.2022.00019
Jan Andre Fagereng, Vajira Lasantha Thambawita, A. Storaas, S. Parasa, T. Lange, P. Halvorsen, M. Riegler
Early identification of a polyp in the lower gas-trointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) systems to detect polyps can improve detection accuracy and efficiency and save the time of the domain experts called endoscopists. Lack of annotated data is a common challenge when building CAD systems. Generating synthetic medical data is an active research area to overcome the problem of having relatively few true positive cases in the medical domain. To be able to efficiently train machine learning (ML) models, which are the core of CAD systems, a considerable amount of data should be used. In this respect, we propose the PolypConnect pipeline, which can convert non-polyp images into polyp images to increase the size of training datasets for training. We present the whole pipeline with quantitative and qualitative evaluations involving endoscopists. The polyp segmentation model trained using synthetic data, and real data shows a 5.1% improvement of mean intersection over union (mIOU), compared to the model trained only using real data. The codes of all the experiments are available on GitHub to reproduce the results.
{"title":"PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps","authors":"Jan Andre Fagereng, Vajira Lasantha Thambawita, A. Storaas, S. Parasa, T. Lange, P. Halvorsen, M. Riegler","doi":"10.1109/CBMS55023.2022.00019","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00019","url":null,"abstract":"Early identification of a polyp in the lower gas-trointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) systems to detect polyps can improve detection accuracy and efficiency and save the time of the domain experts called endoscopists. Lack of annotated data is a common challenge when building CAD systems. Generating synthetic medical data is an active research area to overcome the problem of having relatively few true positive cases in the medical domain. To be able to efficiently train machine learning (ML) models, which are the core of CAD systems, a considerable amount of data should be used. In this respect, we propose the PolypConnect pipeline, which can convert non-polyp images into polyp images to increase the size of training datasets for training. We present the whole pipeline with quantitative and qualitative evaluations involving endoscopists. The polyp segmentation model trained using synthetic data, and real data shows a 5.1% improvement of mean intersection over union (mIOU), compared to the model trained only using real data. The codes of all the experiments are available on GitHub to reproduce the results.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116428454","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 : 2022-05-30DOI: 10.1109/CBMS55023.2022.00013
Lena Mondrejevski, Ioanna Miliou, Annaclaudia Montanino, David Pitts, Jaakko Hollmén, P. Papapetrou
Although Machine Learning can be seen as a promising tool to improve clinical decision-making, it remains limited by access to healthcare data. Healthcare data is sensitive, requiring strict privacy practices, and typically stored in data silos, making traditional Machine Learning challenging. Federated Learning can counteract those limitations by training Machine Learning models over data silos while keeping the sensitive data localized. This study proposes a Federated Learning workflow for Intensive Care Unit mortality prediction. Hereby, the applicability of Federated Learning as an alternative to Centralized Machine Learning and Local Machine Learning is investigated by introducing Federated Learning to the binary classification problem of predicting Intensive Care Unit mortality. We extract multivariate time series data from the MIMIC-III database (lab values and vital signs), and benchmark the predictive performance of four deep sequential classifiers (FRNN, LSTM, GRU, and 1DCNN) varying the patient history window lengths (8h, 16h, 24h, and 48h) and the number of Federated Learning clients (2, 4, and 8). The experiments demonstrate that both Centralized Machine Learning and Federated Learning are comparable in terms of AUPRC and F1-score. Furthermore, the federated approach shows superior performance over Local Machine Learning. Thus, Federated Learning can be seen as a valid and privacy-preserving alternative to Centralized Machine Learning for classifying Intensive Care Unit mortality when the sharing of sensitive patient data between hospitals is not possible.
{"title":"FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction","authors":"Lena Mondrejevski, Ioanna Miliou, Annaclaudia Montanino, David Pitts, Jaakko Hollmén, P. Papapetrou","doi":"10.1109/CBMS55023.2022.00013","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00013","url":null,"abstract":"Although Machine Learning can be seen as a promising tool to improve clinical decision-making, it remains limited by access to healthcare data. Healthcare data is sensitive, requiring strict privacy practices, and typically stored in data silos, making traditional Machine Learning challenging. Federated Learning can counteract those limitations by training Machine Learning models over data silos while keeping the sensitive data localized. This study proposes a Federated Learning workflow for Intensive Care Unit mortality prediction. Hereby, the applicability of Federated Learning as an alternative to Centralized Machine Learning and Local Machine Learning is investigated by introducing Federated Learning to the binary classification problem of predicting Intensive Care Unit mortality. We extract multivariate time series data from the MIMIC-III database (lab values and vital signs), and benchmark the predictive performance of four deep sequential classifiers (FRNN, LSTM, GRU, and 1DCNN) varying the patient history window lengths (8h, 16h, 24h, and 48h) and the number of Federated Learning clients (2, 4, and 8). The experiments demonstrate that both Centralized Machine Learning and Federated Learning are comparable in terms of AUPRC and F1-score. Furthermore, the federated approach shows superior performance over Local Machine Learning. Thus, Federated Learning can be seen as a valid and privacy-preserving alternative to Centralized Machine Learning for classifying Intensive Care Unit mortality when the sharing of sensitive patient data between hospitals is not possible.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131639099","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 : 2022-05-09DOI: 10.1109/CBMS55023.2022.00014
A. Storaas, A. Aasberg, P. Halvorsen, M. Riegler, Inga Strumke
Tacrolimus is one of the cornerstone immunosup-pressive drugs in most transplantation centers worldwide following solid organ transplantation. Therapeutic drug monitoring of tacrolimus is necessary in order to avoid rejection of the transplanted organ or severe side effects. However, finding the right dose for a given patient is challenging, even for experienced clinicians. Consequently, a tool that can accurately estimate the drug exposure for individual dose adaptions would be of high clinical value. In this work, we propose a new technique using machine learning to estimate the tacrolimus exposure in kidney transplant recipients. Our models achieve predictive errors that are at the same level as an established population pharmacokinetic model, but are faster to develop and require less knowledge about the pharmacokinetic properties of the drug.
{"title":"Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning","authors":"A. Storaas, A. Aasberg, P. Halvorsen, M. Riegler, Inga Strumke","doi":"10.1109/CBMS55023.2022.00014","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00014","url":null,"abstract":"Tacrolimus is one of the cornerstone immunosup-pressive drugs in most transplantation centers worldwide following solid organ transplantation. Therapeutic drug monitoring of tacrolimus is necessary in order to avoid rejection of the transplanted organ or severe side effects. However, finding the right dose for a given patient is challenging, even for experienced clinicians. Consequently, a tool that can accurately estimate the drug exposure for individual dose adaptions would be of high clinical value. In this work, we propose a new technique using machine learning to estimate the tacrolimus exposure in kidney transplant recipients. Our models achieve predictive errors that are at the same level as an established population pharmacokinetic model, but are faster to develop and require less knowledge about the pharmacokinetic properties of the drug.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121827218","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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