IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics最新文献_第9页

Modeling the Problem of Integral Geometry on the Family of Broken Lines Based on Tikhonov Regularization 基于Tikhonov正则化的折线族积分几何问题建模

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-27199-1_41

N. Uteuliev, G. Djaykov, A. O. Pirimbetov

引用次数: 0

Improving Gaze Estimation Performance Using Ensemble Loss Function 利用集成损失函数改进注视估计性能

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-27199-1_51

Seunghyun Kim, Seung-Gye Lee, J. Lee, Eui Chul Lee

引用次数: 0

On the Evaluation of Generated Stylised Lyrics Using Deep Generative Models: A Preliminary Study 基于深度生成模型的生成风格化歌词评价初探

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-27199-1_14

H. Hong, Sohyeon Kim, Jee Hang Lee

引用次数: 0

A Novel Methodology for Assessing and Modeling Manufacturing Processes: A Case Study for the Metallurgical Industry 制造过程评估与建模的新方法:冶金工业案例研究

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2021-10-28 DOI: 10.3390/app112110117

Jan Reschke, Diego Gallego-García, Sergio Gallego-García, M. García-García

{"title":"A Novel Methodology for Assessing and Modeling Manufacturing Processes: A Case Study for the Metallurgical Industry","authors":"Jan Reschke, Diego Gallego-García, Sergio Gallego-García, M. García-García","doi":"10.3390/app112110117","DOIUrl":"https://doi.org/10.3390/app112110117","url":null,"abstract":"Historically, researchers and practitioners have often failed to consider all the areas, factors, and implications of a process within an integrated manufacturing model. Thus, the aim of this research was to propose a holistic approach to manufacturing processes in order to assess their status and performance to improve target indicators such as product quality. For this purpose, a conceptual model is designed by identifying areas, flows, and indicators that are relevant to the assessment of a manufacturing system. Moreover, using the conceptual model, manufacturing systems can be modeled considering all related flows and decision-making options in the respective areas of production, maintenance, and quality. As a result, this model serves as the basis for the integral management and control of manufacturing systems in digital twin models for the regulation of process stability and quality with maintenance strategies. Thus, an assessment based on the conceptual model improves the knowledge level of all elements involved in the manufacturing of a product according to the desired quality specifications. The continuous monitoring of all areas and flows together with the optimal strategies in the quality and maintenance areas can enable companies to increase their profitability and customer service level. In this context, the discussion section lists key decision aspects for the assessment and improvement of manufacturing systems, while also providing a methodological sequence to evaluate and improve manufacturing systems. In conclusion, the conceptual approach allows better decision making, ensuring continuous optimization along the manufacturing asset lifecycle and providing a unique selling proposition for equipment producers and service engineering suppliers, as well as for production and assembly companies.","PeriodicalId":73284,"journal":{"name":"IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80384840","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: 9

CATAN: Chart-aware temporal attention network for adverse outcome prediction. 用于不良结果预测的图表感知时间注意网络。

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2021-08-01 Epub Date: 2021-10-15 DOI: 10.1109/ichi52183.2021.00024

Zelalem Gero, Joyce C Ho

{"title":"CATAN: Chart-aware temporal attention network for adverse outcome prediction.","authors":"Zelalem Gero, Joyce C Ho","doi":"10.1109/ichi52183.2021.00024","DOIUrl":"https://doi.org/10.1109/ichi52183.2021.00024","url":null,"abstract":"There is an increased adoption of electronic health record systems by a variety of hospitals and medical centers. This provides an opportunity to leverage automated computer systems in assisting healthcare workers. One of the least utilized but rich source of patient information is the unstructured clinical text. In this work, we develop CATAN, a chart-aware temporal attention network for learning patient representations from clinical notes. We introduce a novel representation where each note is considered a single unit, like a sentence, and composed of attention-weighted words. The notes in turn are aggregated into a patient representation using a second weighting unit, note attention. Unlike standard attention computations which focus only on the content of the note, we incorporate the chart-time for each note as a constraint for attention calculation. This allows our model to focus on notes closer to the prediction time. Using the MIMIC-III dataset, we empirically show that our patient representation and attention calculation achieves the best performance in comparison with various state-of-the-art baselines for one-year mortality prediction and 30-day hospital readmission. Moreover, the attention weights can be used to offer transparency into our model's predictions.","PeriodicalId":73284,"journal":{"name":"IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785859/pdf/nihms-1712844.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39721921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-transfer Deep Learning of Optical Coherence Tomography for Post-hoc Explanation of Macular Disease Classification. 光学相干断层成像的非转移深度学习对黄斑疾病分类的事后解释。

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2021-08-01 Epub Date: 2021-10-15 DOI: 10.1109/ichi52183.2021.00020

Raisul Arefin, Manar D Samad, Furkan A Akyelken, Arash Davanian

{"title":"Non-transfer Deep Learning of Optical Coherence Tomography for Post-hoc Explanation of Macular Disease Classification.","authors":"Raisul Arefin, Manar D Samad, Furkan A Akyelken, Arash Davanian","doi":"10.1109/ichi52183.2021.00020","DOIUrl":"https://doi.org/10.1109/ichi52183.2021.00020","url":null,"abstract":"Deep transfer learning is a popular choice for classifying monochromatic medical images using models that are pretrained by natural images with color channels. This choice may introduce unnecessarily redundant model complexity that can limit explanations of such model behavior and outcomes in the context of medical imaging. To investigate this hypothesis, we develop a configurable deep convolutional neural network (CNN) to classify four macular disease conditions using retinal optical coherence tomography (OCT) images. Our proposed non-transfer deep CNN model (acc: 97.9%) outperforms existing transfer learning models such as ResNet-50 (acc: 89.0%), ResNet-101 (acc: 96.7%), VGG-19 (acc: 93.3%), Inception-V3 (acc: 95.8%) in the same retinal OCT image classification task. We perform post-hoc analysis of the trained model and model extracted image features, which reveals that only eight out of 256 filter kernels are active at our final convolutional layer. The convolutional responses of these selective eight filters yield image features that efficiently separate four macular disease classes even when projected onto two-dimensional principal component space. Our findings suggest that many deep learning parameters and their computations are redundant and expensive for retinal OCT image classification, which are expected to be more intense when using transfer learning. Additionally, we provide clinical interpretations of our misclassified test images identifying manifest artifacts, shadowing of useful texture, false texture representing fluids, and other confounding factors. These clinical explanations along with model optimization via kernel selection can improve the classification accuracy, computational costs, and explainability of model outcomes.","PeriodicalId":73284,"journal":{"name":"IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9511893/pdf/nihms-1836373.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40379439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Methods for Determining the Optimal Sampling Step of Signals in the Process of Device and Computer Integration 设备与计算机集成过程中信号最优采样步长的确定方法

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-98404-5_44

H. Zaynidinov, Dhananjay Singh, S. Makhmudjanov, I. Yusupov

引用次数: 1

The Value of eCoaching in the COVID-19 Pandemic to Promote Adherence to Self-isolation and Quarantine COVID-19大流行期间教学对促进自我隔离隔离的价值

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-98404-5_39

J. V. Klooster, Joris Elmar van Gend, M. A. Schreijer, Elles Riek de Witte, J. V. Gemert-Pijnen

引用次数: 1

Electronic Dictionary and Translator of Bilingual Turkish Languages 土耳其双语电子词典和翻译器

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-98404-5_4

E. Nazirova, Shakhnoza Abidova, Sh. Sh. Yuldasheva

引用次数: 0

The Role of Artificial Intelligence (AI) in Assisting Applied Natya Therapy for Relapse Prevention in De-addiction 人工智能(AI)在协助应用Natya疗法预防戒毒复发中的作用

IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-98404-5_28

Dimple Kaur Malhotra

引用次数: 0