2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI)最新文献

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Towards Model-informed Precision Dosing with Expert-in-the-loop Machine Learning 利用专家在环机器学习实现模型知情的精确定量

2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2021-06-28 DOI: 10.1109/IRI51335.2021.00053

Yihuang Kang, Y. Chiu, Ming-Yen Lin, F. Su, Sheng-Tai Huang

{"title":"Towards Model-informed Precision Dosing with Expert-in-the-loop Machine Learning","authors":"Yihuang Kang, Y. Chiu, Ming-Yen Lin, F. Su, Sheng-Tai Huang","doi":"10.1109/IRI51335.2021.00053","DOIUrl":"https://doi.org/10.1109/IRI51335.2021.00053","url":null,"abstract":"Machine Learning (ML) and its applications have been transforming our lives but it is also creating issues related to the development of fair, accountable, transparent, and ethical Artificial Intelligence. As the ML models are not fully comprehensible yet, it is obvious that we still need humans to be part of algorithmic decision-making processes. In this paper, we consider a ML framework that may accelerate model learning and improve its interpretability by incorporating human experts into the model learning loop. We propose a novel human-in-the-loop ML framework aimed at dealing with learning problems that the cost of data annotation is high and the lack of appropriate data to model the association between the target tasks and the input features. With an application to precision dosing, our experimental results show that the approach can learn interpretable rules from data and may potentially lower experts' workload by replacing data annotation with rule representation editing. The approach may also help remove algorithmic bias by introducing experts' feedback into the iterative model learning process.","PeriodicalId":293393,"journal":{"name":"2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117220799","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}

引用次数: 1

Detecting Anomalous User Behavior in Remote Patient Monitoring 在远程患者监测中检测异常用户行为

2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2021-06-22 DOI: 10.1109/IRI51335.2021.00011

Deepti Gupta, Maanak Gupta, Smriti Bhatt, A. Tosun

{"title":"Detecting Anomalous User Behavior in Remote Patient Monitoring","authors":"Deepti Gupta, Maanak Gupta, Smriti Bhatt, A. Tosun","doi":"10.1109/IRI51335.2021.00011","DOIUrl":"https://doi.org/10.1109/IRI51335.2021.00011","url":null,"abstract":"The growth in Remote Patient Monitoring (RPM) services using wearable and non-wearable Internet of Medical Things (IoMT) promises to improve the quality of diagnosis and facilitate timely treatment for a gamut of medical conditions. At the same time, the proliferation of IoMT devices increases the potential for malicious activities that can lead to catastrophic results including theft of personal information, data breach, and compromised medical devices, putting human lives at risk. IoMT devices generate tremendous amount of data that reflect user behavior patterns including both personal and day-to-day social activities along with daily routine health monitoring. In this context, there are possibilities of anomalies generated due to various reasons including unexpected user behavior, faulty sensor, or abnormal values from malicious/compromised devices. To address this problem, there is an imminent need to develop a framework for securing the smart health care infrastructure to identify and mitigate anomalies. In this paper, we present an anomaly detection model for RPM utilizing IoMT and smart home devices. We propose Hidden Markov Model (HMM) based anomaly detection that analyzes normal user behavior in the context of RPM comprising both smart home and smart health devices, and identifies anomalous user behavior. We design a testbed with multiple IoMT devices and home sensors to collect data and use the HMM model to train using network and user behavioral data. Proposed HMM based anomaly detection model achieved over 98% accuracy in identifying the anomalies in the context of RPM.","PeriodicalId":293393,"journal":{"name":"2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121806883","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}

引用次数: 13

Detection of Alzheimer's Disease Using Graph-Regularized Convolutional Neural Network Based on Structural Similarity Learning of Brain Magnetic Resonance Images 基于脑磁共振图像结构相似性学习的图正则化卷积神经网络检测阿尔茨海默病

2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2021-02-25 DOI: 10.1109/IRI51335.2021.00051

Kuo Yang, E. Mohammed, B. Far

{"title":"Detection of Alzheimer's Disease Using Graph-Regularized Convolutional Neural Network Based on Structural Similarity Learning of Brain Magnetic Resonance Images","authors":"Kuo Yang, E. Mohammed, B. Far","doi":"10.1109/IRI51335.2021.00051","DOIUrl":"https://doi.org/10.1109/IRI51335.2021.00051","url":null,"abstract":"Objective: This paper presents an Alzheimer's disease (AD) detection method based on learning structural similarity between Magnetic Resonance Images (MRIs) and representing the similarity as a graph. Methods: We construct the similarity graph using embedded features of the input image (i.e., Non-Demented (ND), Very Mild Demented (VMD), Mild Demented (MD), and Moderated Demented (MDTD)). We experiment and compare different dimension-reduction and clustering algorithms to construct the best similarity graph to capture the similarity between the same class images using the cosine distance as a similarity measure. We utilize the similarity graph to present (sample) the training data to a convolutional neural network (CNN). We use the similarity graph as a regularizer in the loss function of a CNN model to minimize the distance between the input images and their k-nearest neighbours in the similarity graph while minimizing the categorical cross-entropy loss between the training image predictions and the actual image class labels. Results: We conduct a baseline experiment with a standard, none-tuned VGG-19 CNN model with different settings and compare the results to identify the best settings to classify (ND vs VMD vs MD vs MDTD). Conclusion: Our method achieves an adequate performance on the testing dataset (accuracy = 0.71, area under receiver operating characteristics curve = 0.86, F1 measure = 0.71). Significance: The classification results show that utilizing a structural similarity graph with none tuned standard CNN model can achieve adequate prediction accuracy and motivate fine-tuning several pre-trained models for better classification performance.","PeriodicalId":293393,"journal":{"name":"2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134561562","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}

引用次数: 1

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