利用来自澳大利亚、新西兰和日本的专家和社区精神病学服务的数据,采用神经网络方法优化抑郁症治疗。

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Aidan Cousins, Lucas Nakano, Emma Schofield, Rasa Kabaila
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引用次数: 3

摘要

本研究探讨了应用递归神经网络优化抑郁症的药物治疗。来自澳大利亚、新西兰和日本的专家和社区精神病服务机构的458名参与者的临床数据集是从一个名为Psynary的现有定制网络工具中提取出来的。这些数据包括基线和自我完成的评论,用于训练和完善一种新的算法,该算法是一种完全连接的网络特征提取器,长短期记忆算法首先进行孤立训练,然后由于数据的低维数而使用缓慢的学习速率进行整合和退火。在处理患者回顾资料前预测抑郁缓解的准确率为49.8%。仅处理2条评论后,准确率为76.5%。当考虑更换药物时,更换药物的准确率为97.4%,召回率为71.4%。预测效果最好的药物是抗精神病药物(88%)和选择性血清素再摄取抑制剂(87.9%)。这是首个为优化所有抑郁症亚型的治疗方法而创建一体化算法的研究。减少抑郁症患者的治疗优化时间可能导致早期缓解,从而减少与该病症相关的高水平残疾。此外,在心理健康状况对心理健康服务的压力越来越大的情况下,利用基于网络的工具进行远程监测和机器/深度学习算法,可以帮助专科和初级保健的临床医生将专科心理保健扩展到更大的患者群体。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan.

A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan.

A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan.

A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan.

This study investigated the application of a recurrent neural network for optimising pharmacological treatment for depression. A clinical dataset of 458 participants from specialist and community psychiatric services in Australia, New Zealand and Japan were extracted from an existing custom-built, web-based tool called Psynary . This data, which included baseline and self-completed reviews, was used to train and refine a novel algorithm which was a fully connected network feature extractor and long short-term memory algorithm was firstly trained in isolation and then integrated and annealed using slow learning rates due to the low dimensionality of the data. The accuracy of predicting depression remission before processing patient review data was 49.8%. After processing only 2 reviews, the accuracy was 76.5%. When considering a change in medication, the precision of changing medications was 97.4% and the recall was 71.4% . The medications with predicted best results were antipsychotics (88%) and selective serotonin reuptake inhibitors (87.9%). This is the first study that has created an all-in-one algorithm for optimising treatments for all subtypes of depression. Reducing treatment optimisation time for patients suffering with depression may lead to earlier remission and hence reduce the high levels of disability associated with the condition. Furthermore, in a setting where mental health conditions are increasing strain on mental health services, the utilisation of web-based tools for remote monitoring and machine/deep learning algorithms may assist clinicians in both specialist and primary care in extending specialist mental healthcare to a larger patient community.

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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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