2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)最新文献_第3页

Quality of Context Evaluating Approach in AAL Environment Using IoT Technology 基于物联网技术的AAL环境质量评估方法

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.55

Débora Cabral Nazário, Pedro J. Campos, E. C. Inacio, M. Dantas

引用次数: 17

Method for Intra-Surgical Phase Detection by Using Real-Time Medical Device Data 基于实时医疗器械数据的术中相位检测方法

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.65

N. Spangenberg, Christoph Augenstein, Bogdan Franczyk, M. Wagner, M. Apitz, H. Kenngott

{"title":"Method for Intra-Surgical Phase Detection by Using Real-Time Medical Device Data","authors":"N. Spangenberg, Christoph Augenstein, Bogdan Franczyk, M. Wagner, M. Apitz, H. Kenngott","doi":"10.1109/CBMS.2017.65","DOIUrl":"https://doi.org/10.1109/CBMS.2017.65","url":null,"abstract":"The analysis of surgical activities became a popular field of research in recent years. Various methods had been published to detect surgical phases in various data sources in the operating room. Objective of this research is to develop a method for utilizing real-time information to extract surgical activities. In this work we use fine-grained data of surgical devices and operating room equipment which is produced permanently during surgeries. This low-level data help describing the current surgical phases and reflect real-time status of the endoscope, insufflator, electrosurgical devices and light sources. This is the basis for the development of a structured process to extract surgical phase recognition models. We show how to integrate expert knowledge and transfer this information into an automated and scalable information system for surgical phase recognition. The artifact is developed by adapting the method engineering methodology to find a best practice for utilizing fine-grained data for intrasurgical activity detection. We evaluated our approach with 15 data sets of laparoscopic surgeries and obtained an accuracy rate of about 83% with this approach.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129787447","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: 10

Emotional State Recognition Using Advanced Machine Learning Techniques on EEG Data 基于脑电图数据的先进机器学习技术的情绪状态识别

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.156

Katerina Giannakaki, G. Giannakakis, C. Farmaki, V. Sakkalis

引用次数: 27

Detection and Management of Depression in Cancer Patients Using Augmented Reality Technologies, Multimodal Signal Processing and Persuasive Interfaces 利用增强现实技术、多模态信号处理和说服接口检测和管理癌症患者的抑郁症

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.43

A. Roniotis, H. Kondylakis, M. Tsiknakis

引用次数: 1

Relation between Fetal HRV and Value of Umbilical Cord Artery pH in Labor, a Study with Entropy Measures 胎儿HRV与产程脐带动脉pH值关系的熵测度研究

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.139

G. Manis, R. Sassi

引用次数: 3

Longitudinal Monitoring and Detection of Alzheimer's Type Dementia from Spontaneous Speech Data 基于自发语言数据的阿尔茨海默氏型痴呆的纵向监测和检测

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.41

S. Luz

引用次数: 35

Improving Diagnosis in Obstructive Sleep Apnea with Clinical Data: A Bayesian Network Approach 利用临床数据提高阻塞性睡眠呼吸暂停的诊断:贝叶斯网络方法

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.19

D. F. Santos, P. Rodrigues

{"title":"Improving Diagnosis in Obstructive Sleep Apnea with Clinical Data: A Bayesian Network Approach","authors":"D. F. Santos, P. Rodrigues","doi":"10.1109/CBMS.2017.19","DOIUrl":"https://doi.org/10.1109/CBMS.2017.19","url":null,"abstract":"In obstructive sleep apnea, respiratory effort is maintained but ventilation decreases/disappears because of the partial/total occlusion in the upper airway. It affects about 4% of men and 2% of women in the world population. The aim was to define an auxiliary diagnostic method that can support the decision to perform polysomnography (standard test), based on risk and diagnostic factors. Our sample performed polysomnography between January and May 2015. Two Bayesian classifiers were used to build the models: Naïve Bayes (NB) and Tree augmented Naïve Bayes (TAN), using all 39 variables or just a selection of 13. Area under the ROC curve, sensitivity, specificity, predictive values were evaluated using cross-validation. From a collected total of 241 patients, only 194 fulfill the inclusion criteria. 123 (63%) were male, with a mean age of 58 years old. 66 (34%) patients had a normal result and 128 (66%) a diagnostic of obstructive sleep apnea. The AUCs for each model were: NB39 - 72%; TAN39 - 79%; NB13 - 75% and TAN13 - 75%. The high (34%) proportion of normal results confirm the need for a preevaluation prior to polysomnography. The constant seeking of a validated model to screen patients with suspicion of obstructive sleep apnea is essential, especially at the level of primary care.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132414888","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: 5

Automatic Lymphocyte Detection on Gastric Cancer IHC Images Using Deep Learning 基于深度学习的胃癌免疫组化图像淋巴细胞自动检测

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.94

Emilio Garcia, Renato Hermoza, C. B. Castañón, Luis Cano, Miluska Castillo, Carlos Castanneda

引用次数: 53

Measuring Steatosis in Liver Biopsies Using Machine Learning and Morphological Imaging 使用机器学习和形态学成像测量肝脏活检中的脂肪变性

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.98

N. Giannakeas, M. Tsipouras, A. Tzallas, M. G. Vavva, Maria Tsimplakidou, E. Karvounis, R. Forlano, P. Manousou

引用次数: 3

Developing a Collaborative Knowledge System for Cancer Diseases 发展癌症疾病的协同知识系统

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.66

M. Giannoulis, Emmanouil I. Marakakis, H. Kondylakis

引用次数: 6