Psychopharmacological Prediction by Artificial Neural Networks

以類神經網路預測精神藥理反應

林朝誠(Chao-Cheng Lin)

《台灣精神醫學》 22卷4期 (2008/12) Pp. 254-267

https://doi.org/10.29478/TJP.200812.0002

摘要

類神經網路能提供超越傳統統計方法之限制的優勢，本綜說將介紹類神經網路的觀念與範疇，回顧以類神經網路探討憂鬱症及精神分裂症之精神藥理反應預測之文獻報告。結果顯示大部份的研究中，類神經網路的預測力跟羅吉斯迴歸(Logistic Regression)類似或更好，合併臨床及基因資料之模型可能比只有臨床資料之模型具有更高的預測準確度。最後，選擇適當的類神經網路來使用，可以把手邊的資料及複雜的、互動的、及多面向的精神藥理學資料做最佳化的運用。未來需要有一些前瞻性研究將類神經網路實際地用在不同的臨床環境，才能證實這類的人工智慧系統是否對於病人的臨床預後具有重要的影響力。

關鍵字

人工智慧 ； 類神經網路 ； 抗憂鬱劑 ； 抗精神病劑

並列摘要

Background: Artificial neural network (ANN) can offer specific advantages with respect to classical statistical techniques. This article is designed to acquaint psychiatrists with concepts and paradigms related to ANN. Method: Literature dealing with pharmacological prediction of depression and schizophrenia was reviewed. Results: In most studies, ANN was found to have similar or better predictive performance than logistic regression. Models combining clinical and genetic data had a higher predictive accuracy than those using clinical data alone. Family of ANN, when appropriately selected and used, permits the maximization of what can be derived from available data and from complex, dynamic, and multidimensional psychopharmacological data. Conclusion: Future prospective studies can use the ANN models in real-life, and diverse clinical settings are critical in determining whether this type of system will have important clinical impact on patient outcome.

並列關鍵字

artificial intelligent ； neural network ； antidepressant ； antipsychotics

參考文獻

Dreiseitl S,Ohno-Machado L.(2002).Logistic regression and artificial neural network classification models: a methodology review.J Biomed Inform.35,352-359.

Lin CC,Wang YC,Chen JY(2008).Artificial neural network prediction of clozapine response with combined pharmacogenetic and clinical data.Comput Methods Programs Biomed.91,91-99.

Kramer TL,Owen RR,Cannon D(2003).How well do automated performance measures assess guideline implementation for new-onset depression in the Veterans Health Administration?.Jt Comm J Qual Saf.29,479-489.

Serretti A,Olgiati P,Liebman MN(2007).Clinical prediction of antidepressant response in mood disorders: Linear multivariate vs. neural network models.Psychiatry Res.152,223-231.

Mathew SJ,Ho S(2006).Etiology and neurobiology of social anxiety disorder.J Clin Psychiatry.67,129,13.