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基于随机森林法的精神分裂症患者病情复发的预测

  • 卢国强,尤志军,俞秋峰,汪晓晖,李雪芳,朱佳娟

* 通信作者: 卢国强, 单位:上海市青浦区精神卫生中心Shanghai Qingpu mental health center) 201721

摘要


目的:通过随机森林法分析精神分裂症门诊患者的病情复发的影响因素,建立精神分裂症患者病情复发的预测模型。

方法: 搜集精神分裂症患者门诊记录、住院信息、社区随访记录等信息,将数据进行结构化处理,利用Python处理分析数据,建立病情复发的预测模型。

结果: 具有统计学意义的影响因素是年就诊次数、刺激事件、自我照顾能力、学习能力(P<0.05)。经过随机森林分析,就诊次数、刺激事件、个人生活料理、学习能力是预测模型中的重要权衡因素。复发预测模型具有46.41%的命中率,准确率84.4%,覆盖率91.23%。

结论: 预测模型能够有效预测精神分裂症患者精神分裂症患者病情复发情况并且提供预警信息。

关键词:关键词:精神分裂症;复发;随机森林;影响因素;预测模型

ABSTRACT


Objective: to establish a relapse model of schizophrenia patients and analyze the influencing relapse factors of schizophrenia patients with random forest method.

Methods: collect the outpatient records, inpatient information, community follow-up notes and other informations of schizophrenic patients, structurally process the data, use Python to analyze the data, establish a prediction model of relapse.

Results: the influencing factors with statistical significance were the number of visits every year, stimulating events, self-care ability and learning ability (P < 0.05). After random forest analysis, the number of visits, stimulation events, personal life care and learning ability are important trade-off factors in the prediction model. The recurrence prediction model has a hit rate of 46.41% ,The accuracy rate was 84.4%, and a coverage rate of 91.23%.

Conclusion  : the prediction model can effectively predict the relapse of schizophrenia and provide early warning information.

Key words: Key words: Schizophrenia; relapse; random forest; influencing factors; predictive model

引用本文 / How to Cite This Article

卢国强,尤志军,俞秋峰,汪晓晖,李雪芳,朱佳娟.基于随机森林法的精神分裂症患者病情复发的预测[J]. 国际精神病学杂志, 2021, 48(4): 631-636

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