神经网络预测方法在抑郁症疗效评价中的应用研究
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摘要
本研究包括顺次承接的3个部分:临床试验、疗效影响因素分析和神经网络预测模型研究,分述如下。
目的
1.通过规范设计的临床试验,对比观察抗抑郁药物百优解与以百会、印堂为主穴的针刺疗法对抑郁症的临床疗效;
2.分析影响抑郁症临床疗效的因素,重点关注影响针刺疗法疗效的因素;
3.根据严谨真实的临床试验数据,建立一种适用于抑郁症临床治疗的预测模型,以影响抑郁症临床疗效的因素作为预测因子,对疗程末期的疗效进行预测,为制定和调整治疗方案,提高临床疗效提供量化依据。
方法
1.以两家三甲综合医院的针灸科门诊为中心,严格执行临床试验研究的诊断、纳入、排除标准,采用对患者告知的方法,将所有患者用随机数字表法划分为两个组:药物组和针刺组。药物组患者予以公认的抗抑郁药物百优解胶囊,口服,20mg/日;针刺组患者以百会、印堂为主穴,随证配穴,进针得气,加电时间30min。接受治疗的患者在治疗前一天以及治疗6周后进行HAMD量表和SDS量表测评。
依据试验数据比较药物组和针刺组的临床总疗效以及各类独立症状的改善情况,讨论百优解与针刺疗法各自的临床作用和疗效的特殊性。
2.对影响抑郁症治疗效果的因素进行文献综述,并根据临床试验结果分析不同发病年龄、不同病情严重程度、不同病程对百优解和针刺疗法临床疗效的影响。根据综述和试验结果,结合临床课题的实施计划,选取多个影响因素作为预测因子,为接下来建立神经网络预测模型提供输入变量。
3.选取神经网络作为建模方法,以影响抑郁症治疗效果的多种因素作为预测因子,以HAMD和SDS量表的减分率作为预测目标,依据人工神经网络原理,建立BP网络模型。在MATLAB环境下进行网络的创建、训练和仿真。样本数据来源于临床试验中收集的全部110例病例资料。样本分为训练样本和测试样本,用于网络的训练和仿真。通过在网络训练完毕后使用训练样本进行仿真,观察网络对已知样本的识别情况,评价网络的学习记忆能力;使用测试样本仿真,观察网络对未知样本的识别情况,评价网络的推理泛化能力。
结果
1.临床试验一:观察60例患者治疗前后HAMD和SDS评分,药物组和针刺组的临床总有效率分别为80%和83.33%,其中药物组显著疗效率为10%,针刺组显著疗效率为13.33%。两组患者HAMD减分率均值分别为37.34%和33.14%,SDS减分率均值分别为22.22%和20.23%。药物组和针刺组之间的临床总疗效比较未见明显差异,两组之间治疗前后量表评分的减分数比较也未见明显差异。
从HAMD各因子积分观察不同类别症状的改善情况,药物组和针刺组患者9类症状中的抑郁情绪、绝望感、兴趣减低、睡眠障碍、体重、焦虑/躯体变化和认知障碍等7类症状因子在治疗前后的积分存在显著差异。两组患者之间在治疗前后各症状因子的减分数比较均不存在显著性差异。
临床试验二:观察50例患者治疗前后HAMD和SDS评分,药物组和针刺组的临床总有效率分别为76%和80%,其中药物组显著疗效率为52%,针刺组显著疗效率为52%。两组患者HAMD减分率均值分别为55.85%和54.58%,SDS减分率均值分别为53.20%和56.45%。药物组和针刺组之间的临床总疗效未见明显差异,两组之间治疗前后量表评分的减分数也未见明显差异。
从HAMD各因子积分观察不同类别症状的改善情况,药物疗法对4类症状中的抑郁迟滞症状的改善有明显效果,针刺疗法对躯体症状和睡眠障碍症状取得了明显的改善效果。对药物组和针刺组之间各症状因子治疗前后的减分数进行组间T检验的结果表明,针刺疗法对躯体症状和睡眠障碍症状的治疗效果显著优于药物疗法。
在采用针刺治疗的过程中,患者没有出现副作用的表现;而服用百优解治疗的过程中,大多数患者都有不同程度副作用表现和临床反应,百优解的不同副作用在用药的前2周内表现最明显,随后逐渐有所减轻。从副反应量表评分的比较可知,在整个治疗过程中,针刺疗法与药物疗法相比,都存在着副作用少的显著优势,这种优势随着疗程的进行而愈加明显。
2.影响抑郁症疗效的因素复杂多样。在临床试验中观察发病年龄、病情严重程度、病程这3种因素与疗效的量化关系,结果如下:
药物组中,各年龄段的总有效率并无显著差异,但显著疗效在31-40岁和51-60组出现的比率较大。针刺组中,18-30岁组的临床总疗效(91.67%)显著优于其他年龄组;显著疗效出现在31-40岁和41-50岁组的比率大于其他两组;51-60岁组的临床总疗效(66.67%)和显著疗效率(0%)均低于其他年龄组。通过两组之间量表减分数的比较,针刺组51-60岁患者的量表减分数显著高于药物组51-60岁患者,表明针刺疗法对51-60岁组的临床疗效优于药物疗法。
药物组和针刺组的数据均显示,轻度抑郁患者的临床总疗效略优于中度抑郁患者,而中度抑郁组产生显著疗效的比率高于轻度抑郁组。两组之间量表减分数比较在轻度抑郁患者和中度抑郁患者中均未见显著性差异。
药物组中,病程3个月以下的患者临床总疗效优于病程较长的患者,但其疗效大多表现为患者抑郁症状的轻微改善,而显著疗效在病程较长的患者中出现的比率高于病程较短的患者;针刺组中,病程较短的患者临床总疗效优于病程较长的患者,并且产生显著疗效的比率随着病程的增长而呈现下降趋势。两组之间量表减分数比较在不同病程的患者中均未见显著性差异。
3.预测模型基于3层BP神经网络,输入层节点数与选取的影响因素个数相同,输出层节点数有2个,分别对应HAMD减分率和SDS减分率。
从网络训练的结果分析可知,网络对训练样本的识别效果较好,训练误差达到预期目标(均方误差mse=0.0010),对HAMD减分率和SDS减分率的预测拟合度较好(可决系数分别为0.9695、0.9751),且拟合精度较高,说明网络模型具有较好的学习“记忆”能力。
分析网络模型对未知样本的识别能力。按照处理方式一,与网络训练结果相比,网络测试的拟合程度比训练结果的拟合程度差,可决系数减小,并且网络模型的预测误差较大,对HAMD减分率的平均绝对误差为0.2123,均方误差为0.0679,对SDS减分率的平均绝对误差为0.1676,均方误差为0.0420。预测值与实际值存在显著的线性关系。按照处理方式二,5次实验均表明网络测试的拟合程度比训练结果的拟合程度差,网络模型对测试样本的预测误差大于对已知样本的预测误差。同时在测试组中可决系数出现有较大的波动,主要是因为验证组总体样本较少。
考虑治疗前HAMD各症状因子积分对疗效的影响,将其作为预测因子加入预测模型,选取临床试验一中HAMD减分率在25%-50%的42个病例资料作为样本数据,选用19-30-2网络结构,其他网络参数不变,通过使用训练样本自身仿真观察训练情况,使用留一个出来(leave one out)的方式处理样本以观察网络测试结果。结果表明,网络的学习“记忆”能力较好,对HAMD减分率和SDS减分率的预测拟合度较好(可决系数分别为0.8716,0.9539),且拟合精度较高。同时对未知样本的识别能力仍然不及对已知样本的识别,网络误差较大,对HAMD减分率的平均绝对误差为0.1736,均方误差为0.0445,对SDS减分率的平均绝对误差为0.1601,均方误差为0.0368;但预测误差与10-25-2结构模型的误差相比稍有减小,预测效果获得改善。
结论
1.药物疗法和针刺疗法对抑郁症均取得了明显的临床疗效,明显改善了抑郁症状;药物疗法和针刺疗法的疗效数据统计结果不存在有统计学意义的明显差异,但在临床实际中,两种不同疗法的疗效存在区别,并且两种疗法对各类独立症状的治疗作用也不尽相同;针刺疗法未产生负面效应。
2.影响抑郁症临床疗效的因素复杂多样。由试验结果可知,不同发病年龄、不同病情严重程度、不同病程的患者对药物疗法和针刺疗法均表现出了不同的敏感度。
3.建立的神经网络预测模型具有较好的学习记忆能力,而推理能力不及学习能力。通过增加样本量、改善数据预处理、改进网络模型等途径,有望为临床提供可靠性良好的预测方法。
It has been verified by clinical practice that the therapeutic effects for depression relate to many effect factors and the complex nonlinear interaction between them. While Artificial Neural Network (ANN) is a nonlinear system that simulates the information processing method of the human brain, with high ability to deal with nonlinear problems, and adapts to the modeling for such problems as with complex information, dark background knowledge, or indefinite inference rules. In this study, the back propagation (BP) neural network is used to estimate effectiveness for depression.
This study contains 3 continued linked parts : clinical trials, effect factors analysis and forecast modeling for effectiveness.
The objective are respectively that:
1. To study and analysis comparatively the clinical phenomenon of therapeutic effect of drug and acupuncture for treatment of depression;
2. To review and analysis the influence factors of clinical effectiveness for treatment of depression;
3. To modeling a forecast model aimed to forecast the quantitative effectiveness which will appear at the end of treatment course.
Correspondingly, the methods are shown below:
1. Two groups of the patients are chosen: the control group– 30 patients, received Prozac on 20 mg per day, during 6 weeks; and the acupuncture group– 30 patients, received acupuncture 30 minutes per time, 3 times a week, during 6 weeks.
2. Review the effect factors of clinical effectiveness for treatment of depression from document material, and analysis what influence the factors of age, severity degree, and disease course have set on the effectiveness.
3. Build a forecast model based on neural network method, with the quantitative values of relevant factors of effect as input variables of the network, while the changes in scales scores between pre and post treatment are set as output variables. The collected 110 cases are divided into training samples and testing samples and the neural network is trained and tested.
It can be concluded from the results that:
1. Notable positive effect and reduction in depressive symptoms are shown in both drug group and acupuncture group; Significant difference is not indicated between the effectiveness of drug and acupuncture. However, the therapeutic effects of these two therapies are difference;No negative effects of acupuncture is found.
2. There are many effect factors of clinical effectiveness for treatment of depression between which the complex interactions also influence the effectiveness. Concluded from the clinical data in our trial, the different therapeutic effects are established in different groups compartmentalized by onset age、severity degree、disease course.
3. The neural network model in this research has got rather ideal results of estimation in identification for the known samples, however, its predicting ability for unknown samples has not achieved such ideal results as that for known samples. The forecast model based on neural network is expected to be a helpful method for clinical research and practice with the work of expanding the sample size and improving the neural network model.
目的
1.通过规范设计的临床试验,对比观察抗抑郁药物百优解与以百会、印堂为主穴的针刺疗法对抑郁症的临床疗效;
2.分析影响抑郁症临床疗效的因素,重点关注影响针刺疗法疗效的因素;
3.根据严谨真实的临床试验数据,建立一种适用于抑郁症临床治疗的预测模型,以影响抑郁症临床疗效的因素作为预测因子,对疗程末期的疗效进行预测,为制定和调整治疗方案,提高临床疗效提供量化依据。
方法
1.以两家三甲综合医院的针灸科门诊为中心,严格执行临床试验研究的诊断、纳入、排除标准,采用对患者告知的方法,将所有患者用随机数字表法划分为两个组:药物组和针刺组。药物组患者予以公认的抗抑郁药物百优解胶囊,口服,20mg/日;针刺组患者以百会、印堂为主穴,随证配穴,进针得气,加电时间30min。接受治疗的患者在治疗前一天以及治疗6周后进行HAMD量表和SDS量表测评。
依据试验数据比较药物组和针刺组的临床总疗效以及各类独立症状的改善情况,讨论百优解与针刺疗法各自的临床作用和疗效的特殊性。
2.对影响抑郁症治疗效果的因素进行文献综述,并根据临床试验结果分析不同发病年龄、不同病情严重程度、不同病程对百优解和针刺疗法临床疗效的影响。根据综述和试验结果,结合临床课题的实施计划,选取多个影响因素作为预测因子,为接下来建立神经网络预测模型提供输入变量。
3.选取神经网络作为建模方法,以影响抑郁症治疗效果的多种因素作为预测因子,以HAMD和SDS量表的减分率作为预测目标,依据人工神经网络原理,建立BP网络模型。在MATLAB环境下进行网络的创建、训练和仿真。样本数据来源于临床试验中收集的全部110例病例资料。样本分为训练样本和测试样本,用于网络的训练和仿真。通过在网络训练完毕后使用训练样本进行仿真,观察网络对已知样本的识别情况,评价网络的学习记忆能力;使用测试样本仿真,观察网络对未知样本的识别情况,评价网络的推理泛化能力。
结果
1.临床试验一:观察60例患者治疗前后HAMD和SDS评分,药物组和针刺组的临床总有效率分别为80%和83.33%,其中药物组显著疗效率为10%,针刺组显著疗效率为13.33%。两组患者HAMD减分率均值分别为37.34%和33.14%,SDS减分率均值分别为22.22%和20.23%。药物组和针刺组之间的临床总疗效比较未见明显差异,两组之间治疗前后量表评分的减分数比较也未见明显差异。
从HAMD各因子积分观察不同类别症状的改善情况,药物组和针刺组患者9类症状中的抑郁情绪、绝望感、兴趣减低、睡眠障碍、体重、焦虑/躯体变化和认知障碍等7类症状因子在治疗前后的积分存在显著差异。两组患者之间在治疗前后各症状因子的减分数比较均不存在显著性差异。
临床试验二:观察50例患者治疗前后HAMD和SDS评分,药物组和针刺组的临床总有效率分别为76%和80%,其中药物组显著疗效率为52%,针刺组显著疗效率为52%。两组患者HAMD减分率均值分别为55.85%和54.58%,SDS减分率均值分别为53.20%和56.45%。药物组和针刺组之间的临床总疗效未见明显差异,两组之间治疗前后量表评分的减分数也未见明显差异。
从HAMD各因子积分观察不同类别症状的改善情况,药物疗法对4类症状中的抑郁迟滞症状的改善有明显效果,针刺疗法对躯体症状和睡眠障碍症状取得了明显的改善效果。对药物组和针刺组之间各症状因子治疗前后的减分数进行组间T检验的结果表明,针刺疗法对躯体症状和睡眠障碍症状的治疗效果显著优于药物疗法。
在采用针刺治疗的过程中,患者没有出现副作用的表现;而服用百优解治疗的过程中,大多数患者都有不同程度副作用表现和临床反应,百优解的不同副作用在用药的前2周内表现最明显,随后逐渐有所减轻。从副反应量表评分的比较可知,在整个治疗过程中,针刺疗法与药物疗法相比,都存在着副作用少的显著优势,这种优势随着疗程的进行而愈加明显。
2.影响抑郁症疗效的因素复杂多样。在临床试验中观察发病年龄、病情严重程度、病程这3种因素与疗效的量化关系,结果如下:
药物组中,各年龄段的总有效率并无显著差异,但显著疗效在31-40岁和51-60组出现的比率较大。针刺组中,18-30岁组的临床总疗效(91.67%)显著优于其他年龄组;显著疗效出现在31-40岁和41-50岁组的比率大于其他两组;51-60岁组的临床总疗效(66.67%)和显著疗效率(0%)均低于其他年龄组。通过两组之间量表减分数的比较,针刺组51-60岁患者的量表减分数显著高于药物组51-60岁患者,表明针刺疗法对51-60岁组的临床疗效优于药物疗法。
药物组和针刺组的数据均显示,轻度抑郁患者的临床总疗效略优于中度抑郁患者,而中度抑郁组产生显著疗效的比率高于轻度抑郁组。两组之间量表减分数比较在轻度抑郁患者和中度抑郁患者中均未见显著性差异。
药物组中,病程3个月以下的患者临床总疗效优于病程较长的患者,但其疗效大多表现为患者抑郁症状的轻微改善,而显著疗效在病程较长的患者中出现的比率高于病程较短的患者;针刺组中,病程较短的患者临床总疗效优于病程较长的患者,并且产生显著疗效的比率随着病程的增长而呈现下降趋势。两组之间量表减分数比较在不同病程的患者中均未见显著性差异。
3.预测模型基于3层BP神经网络,输入层节点数与选取的影响因素个数相同,输出层节点数有2个,分别对应HAMD减分率和SDS减分率。
从网络训练的结果分析可知,网络对训练样本的识别效果较好,训练误差达到预期目标(均方误差mse=0.0010),对HAMD减分率和SDS减分率的预测拟合度较好(可决系数分别为0.9695、0.9751),且拟合精度较高,说明网络模型具有较好的学习“记忆”能力。
分析网络模型对未知样本的识别能力。按照处理方式一,与网络训练结果相比,网络测试的拟合程度比训练结果的拟合程度差,可决系数减小,并且网络模型的预测误差较大,对HAMD减分率的平均绝对误差为0.2123,均方误差为0.0679,对SDS减分率的平均绝对误差为0.1676,均方误差为0.0420。预测值与实际值存在显著的线性关系。按照处理方式二,5次实验均表明网络测试的拟合程度比训练结果的拟合程度差,网络模型对测试样本的预测误差大于对已知样本的预测误差。同时在测试组中可决系数出现有较大的波动,主要是因为验证组总体样本较少。
考虑治疗前HAMD各症状因子积分对疗效的影响,将其作为预测因子加入预测模型,选取临床试验一中HAMD减分率在25%-50%的42个病例资料作为样本数据,选用19-30-2网络结构,其他网络参数不变,通过使用训练样本自身仿真观察训练情况,使用留一个出来(leave one out)的方式处理样本以观察网络测试结果。结果表明,网络的学习“记忆”能力较好,对HAMD减分率和SDS减分率的预测拟合度较好(可决系数分别为0.8716,0.9539),且拟合精度较高。同时对未知样本的识别能力仍然不及对已知样本的识别,网络误差较大,对HAMD减分率的平均绝对误差为0.1736,均方误差为0.0445,对SDS减分率的平均绝对误差为0.1601,均方误差为0.0368;但预测误差与10-25-2结构模型的误差相比稍有减小,预测效果获得改善。
结论
1.药物疗法和针刺疗法对抑郁症均取得了明显的临床疗效,明显改善了抑郁症状;药物疗法和针刺疗法的疗效数据统计结果不存在有统计学意义的明显差异,但在临床实际中,两种不同疗法的疗效存在区别,并且两种疗法对各类独立症状的治疗作用也不尽相同;针刺疗法未产生负面效应。
2.影响抑郁症临床疗效的因素复杂多样。由试验结果可知,不同发病年龄、不同病情严重程度、不同病程的患者对药物疗法和针刺疗法均表现出了不同的敏感度。
3.建立的神经网络预测模型具有较好的学习记忆能力,而推理能力不及学习能力。通过增加样本量、改善数据预处理、改进网络模型等途径,有望为临床提供可靠性良好的预测方法。
It has been verified by clinical practice that the therapeutic effects for depression relate to many effect factors and the complex nonlinear interaction between them. While Artificial Neural Network (ANN) is a nonlinear system that simulates the information processing method of the human brain, with high ability to deal with nonlinear problems, and adapts to the modeling for such problems as with complex information, dark background knowledge, or indefinite inference rules. In this study, the back propagation (BP) neural network is used to estimate effectiveness for depression.
This study contains 3 continued linked parts : clinical trials, effect factors analysis and forecast modeling for effectiveness.
The objective are respectively that:
1. To study and analysis comparatively the clinical phenomenon of therapeutic effect of drug and acupuncture for treatment of depression;
2. To review and analysis the influence factors of clinical effectiveness for treatment of depression;
3. To modeling a forecast model aimed to forecast the quantitative effectiveness which will appear at the end of treatment course.
Correspondingly, the methods are shown below:
1. Two groups of the patients are chosen: the control group– 30 patients, received Prozac on 20 mg per day, during 6 weeks; and the acupuncture group– 30 patients, received acupuncture 30 minutes per time, 3 times a week, during 6 weeks.
2. Review the effect factors of clinical effectiveness for treatment of depression from document material, and analysis what influence the factors of age, severity degree, and disease course have set on the effectiveness.
3. Build a forecast model based on neural network method, with the quantitative values of relevant factors of effect as input variables of the network, while the changes in scales scores between pre and post treatment are set as output variables. The collected 110 cases are divided into training samples and testing samples and the neural network is trained and tested.
It can be concluded from the results that:
1. Notable positive effect and reduction in depressive symptoms are shown in both drug group and acupuncture group; Significant difference is not indicated between the effectiveness of drug and acupuncture. However, the therapeutic effects of these two therapies are difference;No negative effects of acupuncture is found.
2. There are many effect factors of clinical effectiveness for treatment of depression between which the complex interactions also influence the effectiveness. Concluded from the clinical data in our trial, the different therapeutic effects are established in different groups compartmentalized by onset age、severity degree、disease course.
3. The neural network model in this research has got rather ideal results of estimation in identification for the known samples, however, its predicting ability for unknown samples has not achieved such ideal results as that for known samples. The forecast model based on neural network is expected to be a helpful method for clinical research and practice with the work of expanding the sample size and improving the neural network model.
引文
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