摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-关于精神健康疾病和条件的新研究-精神病是一篇报道的主题。根据NewsRx记者从葡萄牙里斯本发来的新闻报道，研究表明：“尽管多年的研究，我们仍然无法可靠地预测谁可能从电休克疗法(ECT)中受益。由于我们用尽了传统统计分析的可能，ECT仍然是一个很好的机器学习方法的候选，因为它具有大量的数据集，通过脑电图(EEG)和其他客观测量获取数据。我们的新闻编辑从Hospital Beatriz Angelo那里获得了一条引文，“对6个数据库的系统回顾导致了使用机器学习方法来检查预测ECT治疗反应的数据的26个艺术文献的全文检查。所确定的文章使用了广泛的数据类型，包括结构和功能成像数据(n=15)，临床数据(n=5)。结合临床和影像学数据(n=2)、EEG(n=3)、评价疗效预测的临床指标包括抑郁症(n=21)和精神病(n=4),脑内多个解剖区域的变化对ECT疗效具有预测价值,这些变化主要集中在边缘系统和相关网络上,预测抑郁症对ECT疗效良好的临床特征包括较短的ECT疗效。ECT治疗后再入院精神病患者复发的可能性也有可能预测,其中EG信号传递熵越高,反应越好。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Mental Health Diseases and Conditions - Psychosis is the subject of a report. According to news report ing originating from Lisbon, Portugal, by NewsRx correspondents, research stated , "Despite years of research, we are still not able to reliably predict who migh t benefit from electroconvulsive therapy (ECT) treatment. As we exhaust what is possible using traditional statistical analysis, ECT remains a good candidate fo r machine learning approaches due to the large data sets with data captured thro ugh electroencephalography (EEG) and other objective measures." Our news editors obtained a quote from the research from Hospital Beatriz Angelo , "A systematic review of 6 databases led to the full-text examination of 26 art icles using machine learning approaches in examining data predicting response to ECT treatment. The identified articles used a wide variety of data types coveri ng structural and functional imaging data (n = 15), clinical data (n = 5), a com bination of clinical and imaging data (n = 2), EEG (n = 3), and social media pos ts (n = 1). The clinical indications in which response prediction was assessed w ere depression (n = 21) and psychosis (n = 4). Changes in multiple anatomical re gions in the brain were identified as holding a predictive value for response to ECT. These primarily centered on the limbic system and associated networks. Cli nical features predicting good response to ECT in depression included shorter du ration, lower severity, higher medication dose, psychotic features, low cortisol levels, and positive family history. It has also been possible to predict the l ikelihood of relapse of readmission with psychosis after ECT treatment, includin g a better response if higher transfer entropy was calculated from EEG signals."
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