迷走神經性昏厥之早期診斷-利用類神經網路建立輔助診斷預測模型系統

Translated title of the contribution: Extract Prediction Factors of Vasovagal Syncope from Non-Medicine Tilt-Table Testing: An Artificial Neural Network Prediction Model

Wen-Chou Chi, Hung-Wen Chiu, Chun-An Cheng

Research output: Contribution to journalArticle

Abstract

The occurrence of syncope could easily lead to accidents and falls, which endangering the safety of patients enormously. Early diagnosis helps to reduce the harm caused by the disease. However, the causes of syncope are very complex, and its diagnosis is difficult. Tilting-table test is the appropriate way to assess the recurrent unexplained syncope. Some of the Tilting-table test using vasodilator agent to increase the positive rate and shorten the time, but pharmacological challenge test will decrease the specificity and increase opportunities for false positive. In this study, hemodynamic and body content data collected from cases of non-pharmacological Tilting-table, were analyzed to extract factors which are related to vasovagal syncope. These factors were applied in the neural networks to build up a model which can predict vasovagal syncope. Among total collection of 60 cases, 30 of vasovagal syncope confirmed cases and another 30 to Tilting-table test were the negative cases. The study use of non- pharmacological Tilting-table test collect the hemodynamic data from cases when they were supine, 3 minutes after tilt up test and prior to end of tilt up test. The results showed that patients with vasovagal syncope have lower body weight, body mass index and the mean blood pressure during supine position. During tilt test, there were higher heart rate variability low frequency / high frequency ratio (HRV LF/HF), lower baroreflex sensitivity (BRS) and total peripheral resistance index (TPRI) than negative group during presyncopal period. The body mass index, supine heart rate, Cardiac Index(CI) while tilt up 3 minutes , Left Ventricular Work Index(LVWI) while tilt up 3 minutes, and the mean blood pressure (MBP) while tilt up 3 minutes were selected from logistic regression analysis, which are related factors of vasovagal syncope. The model using those factors build by ANN show that the training group's sensitivity was 92%; specificity was 95% and accuracy was 93.75%.; the validation group have 100% accuracy, ROC threshold is 0.517, ROC area under the curve is 0.979; the overall sensitivity of 93%; specificity of 96%, accuracy was 95.5%. The model has good performance to predict vasovagal syncope.
Original languageTraditional Chinese
Pages (from-to)13-24
Number of pages12
Journal醫療資訊雜誌
Volume20
Issue number4
Publication statusPublished - 2011

Fingerprint

Vasovagal Syncope
Neural Networks (Computer)
Syncope
Body Mass Index
Sensitivity Training Groups
Heart Rate
Hemodynamics
Pharmacology
Blood Pressure
Baroreflex
Supine Position
Patient Safety
Vasodilator Agents
Vascular Resistance
Area Under Curve
Accidents
Early Diagnosis
Logistic Models
Body Weight
Regression Analysis

Cite this

迷走神經性昏厥之早期診斷-利用類神經網路建立輔助診斷預測模型系統. / Chi, Wen-Chou ; Chiu, Hung-Wen; Cheng, Chun-An .

In: 醫療資訊雜誌, Vol. 20, No. 4, 2011, p. 13-24.

Research output: Contribution to journalArticle

@article{9c74fb28a5d04cab868856d0ecf9ac08,
title = "迷走神經性昏厥之早期診斷-利用類神經網路建立輔助診斷預測模型系統",
abstract = "昏厥的發生易造成意外及跌倒,危害患者之安全甚鉅,但昏厥之成因相當複雜,因此診斷有其難度,而預測就更加困難,傾斜床檢查是評估反覆性原因不明昏厥之最佳工具。一部分的傾斜床檢查使用血管擴張藥物以增加陽性率及縮短時間,但使用藥物檢查會使其專一性下降,增加假陽性機會。本研究利用個案非藥物傾斜床檢查之血液動力學資料以及身體質量資料,蒐集與昏厥相關之因子,並將之透過類神經網路之方式建立昏厥之預測模型,提供臨床醫師作為決策之參考,協助診斷及預防暈厥所造成之傷害。研究共蒐集60個個案,30個為神經性昏厥確診個案,另30個為傾斜床測試陰性反應個案,利用非藥物傾斜床檢查評估迷走神經性昏厥,蒐集其性別、年齡、身體組成、平躺、檢查3分鐘以及傾斜檢查結束前之血液動力資料。結果發現,迷走神經性昏厥病患的體重、身體質量係數、平均血壓較低;傾斜測試下昏厥前期時,心跳、心率變異低頻/高頻比率上升、感壓反射敏感度下降較明顯、周邊血管阻力係數較低。透過基本資料、平躺及傾斜早期血流動力學及自律神經參數,運用邏輯式迴歸及類神經網路分析建立之預測模組其參數包含身體質量指數、平躺時心跳、傾斜3分鐘心輸出係數、傾斜3分鐘左心做功係數、傾斜3分鐘平均血壓。該模組整體敏感度(Sensitivity)為93{\%};專一度為(Specificity)96{\%},正確率(Accuracy)為95.5{\%},模組狀況佳可提早辨識發生的病患。",
keywords = "人工智慧, 迷走神徑昏厥, 類神經網路, 傾斜床測試, 決策支援系統, Artificial Intelligence, vasovagal syncope, Artificial Neural Network, Tilt-table testing, Decision support system",
author = "Wen-Chou Chi and Hung-Wen Chiu and Chun-An Cheng",
year = "2011",
language = "繁體中文",
volume = "20",
pages = "13--24",
journal = "醫療資訊雜誌",
issn = "1021-3155",
publisher = "社團法人台灣醫學資訊學會",
number = "4",

}

TY - JOUR

T1 - 迷走神經性昏厥之早期診斷-利用類神經網路建立輔助診斷預測模型系統

AU - Chi, Wen-Chou

AU - Chiu, Hung-Wen

AU - Cheng, Chun-An

PY - 2011

Y1 - 2011

N2 - 昏厥的發生易造成意外及跌倒,危害患者之安全甚鉅,但昏厥之成因相當複雜,因此診斷有其難度,而預測就更加困難,傾斜床檢查是評估反覆性原因不明昏厥之最佳工具。一部分的傾斜床檢查使用血管擴張藥物以增加陽性率及縮短時間,但使用藥物檢查會使其專一性下降,增加假陽性機會。本研究利用個案非藥物傾斜床檢查之血液動力學資料以及身體質量資料,蒐集與昏厥相關之因子,並將之透過類神經網路之方式建立昏厥之預測模型,提供臨床醫師作為決策之參考,協助診斷及預防暈厥所造成之傷害。研究共蒐集60個個案,30個為神經性昏厥確診個案,另30個為傾斜床測試陰性反應個案,利用非藥物傾斜床檢查評估迷走神經性昏厥,蒐集其性別、年齡、身體組成、平躺、檢查3分鐘以及傾斜檢查結束前之血液動力資料。結果發現,迷走神經性昏厥病患的體重、身體質量係數、平均血壓較低;傾斜測試下昏厥前期時,心跳、心率變異低頻/高頻比率上升、感壓反射敏感度下降較明顯、周邊血管阻力係數較低。透過基本資料、平躺及傾斜早期血流動力學及自律神經參數,運用邏輯式迴歸及類神經網路分析建立之預測模組其參數包含身體質量指數、平躺時心跳、傾斜3分鐘心輸出係數、傾斜3分鐘左心做功係數、傾斜3分鐘平均血壓。該模組整體敏感度(Sensitivity)為93%;專一度為(Specificity)96%,正確率(Accuracy)為95.5%,模組狀況佳可提早辨識發生的病患。

AB - 昏厥的發生易造成意外及跌倒,危害患者之安全甚鉅,但昏厥之成因相當複雜,因此診斷有其難度,而預測就更加困難,傾斜床檢查是評估反覆性原因不明昏厥之最佳工具。一部分的傾斜床檢查使用血管擴張藥物以增加陽性率及縮短時間,但使用藥物檢查會使其專一性下降,增加假陽性機會。本研究利用個案非藥物傾斜床檢查之血液動力學資料以及身體質量資料,蒐集與昏厥相關之因子,並將之透過類神經網路之方式建立昏厥之預測模型,提供臨床醫師作為決策之參考,協助診斷及預防暈厥所造成之傷害。研究共蒐集60個個案,30個為神經性昏厥確診個案,另30個為傾斜床測試陰性反應個案,利用非藥物傾斜床檢查評估迷走神經性昏厥,蒐集其性別、年齡、身體組成、平躺、檢查3分鐘以及傾斜檢查結束前之血液動力資料。結果發現,迷走神經性昏厥病患的體重、身體質量係數、平均血壓較低;傾斜測試下昏厥前期時,心跳、心率變異低頻/高頻比率上升、感壓反射敏感度下降較明顯、周邊血管阻力係數較低。透過基本資料、平躺及傾斜早期血流動力學及自律神經參數,運用邏輯式迴歸及類神經網路分析建立之預測模組其參數包含身體質量指數、平躺時心跳、傾斜3分鐘心輸出係數、傾斜3分鐘左心做功係數、傾斜3分鐘平均血壓。該模組整體敏感度(Sensitivity)為93%;專一度為(Specificity)96%,正確率(Accuracy)為95.5%,模組狀況佳可提早辨識發生的病患。

KW - 人工智慧

KW - 迷走神徑昏厥

KW - 類神經網路

KW - 傾斜床測試

KW - 決策支援系統

KW - Artificial Intelligence

KW - vasovagal syncope

KW - Artificial Neural Network

KW - Tilt-table testing

KW - Decision support system

M3 - 文章

VL - 20

SP - 13

EP - 24

JO - 醫療資訊雜誌

JF - 醫療資訊雜誌

SN - 1021-3155

IS - 4

ER -