Outcome Prediction in Moderate or Severe Head Injury Using an Artificial Neural Network

以類神經元網路預測中重度頭部外傷病患之預後

許明暉(Min-Huei Hsu) ； 李友專(Yu-Chuan Li) ； 邱文達(Wen-Ta Chiu) ； 顏如娟(Ju-Chuan Yen)

《北市醫學雜誌》 2卷3期 (2005/03) Pp. 272-277

https://doi.org/10.6200/TCMJ.2005.2.3.06

摘要

目的：目前已知之頭部外傷預後預測模式大多為兩極性結果之選擇，如存活相對於死亡；良好的預後相對於不良之預後。如欲預測更詳細的預後等級則需建構更複雜的預測模式。方法：在本實驗中我們嘗試以類神經元網路預測中重度頭部外傷病患在格拉斯哥預後量表中五個等級（死亡、植物人狀態、嚴重殘障、中度殘障、恢復良好）之落點。分析資料來源為臺灣頭部外傷資料庫1995年6月1日至1998年5月30日收錄之個案。資料庫中共有18583筆資料，每筆資料有132個變數。在剔除輕度頭部外傷（昏迷指數≧13）與資料不完整之個案後個案數為4460。首先我們使用逐步邏輯回歸篩選出10個具統計意義之變數，再用這個變數來建構類神經網路。結果：此一類神經網路預測有75.8%為準確，14.6%預測較實際為悲觀，9.6%預測較實際為樂觀。此一類神經網路對於不同預後的預測能力有顯著差異；預測死亡與恢復良好準確性最高，預測植物人狀態準確性最差。結論：我們認為此一類神經網路可作為神經外科醫師為頭部外傷病患進行預後預測之參考。

關鍵字

頭部外傷 ； 類神經元網路 ； 預後預測

並列摘要

Objective: Many studies have constructed predictive models for outcome after traumatic brain injury. Most of these attempts focused on dichotomous result, such as alive vs dead or good outcome vs poor outcome. If we want to predict more specific levels of outcome, we need more sophisticated models. We conducted this study to determine if artificial neural network modeling would predict outcome in five levels of Glasgow Outcome Scale (death, persistent vegetative state, severe disability, moderate disability, and good recovery) after moderate to severe head injury. Methods: The database was collected from a nation-wide epidemiological study of traumatic brain injury in Taiwan from July 1, 1995 to June 30, 1998. There were total 18583 records in this database and each record had thirty-two parameters. After pruning the records with minor cases (GCS≥13) and missing data in the 132 variables, the number of cases decreased from 18583 to 4460. A step-wise logistic regression was applied to the remaining data set and 10 variables were selected as being statically significant in predicting outcome. These 10 variables were used as the input neurons for constructing neural network. Results: Overall, 75.8% of predictions of this model were correct, 14.6% were pessimistic, and 9.6% optimistic. This neural network model demonstrated a significant difference of performance between different levels of Glasgow Outcome Scale. The prediction performance of dead or good recovery is best and the prediction of vegetative state is worst. Conclusion: An artificial neural network may provide a useful ”second opinion” to assist neurosurgeon to predict outcome after traumatic brain injury.

並列關鍵字

head injury ； artificial neural network ； outcome prediction