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开发和多站点外部验证双相情感障碍的可概括风险预测模型
Colin G Walsh1, Michael A Ripperger2, Yirui Hu3
1Vanderbilt University Medical Center Health System, Nashville, TN, USA. Colin.walsh@vumc.org.
Translational psychiatry
|January 25, 2024
概括
使用电子健康记录 (EHR) 开发双相情感障碍风险的预测模型对早期识别和精准医学有很大的前景. 这些可概括的模型可以改善心理健康资源分配,减少误诊.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 计算医学是一种计算医学.
- 医疗信息学 医疗信息学
背景情况:
- 双极性障碍显著导致残疾,过早死亡和自杀.
- 早期识别双相情感障碍风险对于有针对性的干预和有效的资源配置至关重要.
- 现有的预测模型往往缺乏跨不同人群的概括性.
研究的目的:
- 开发和验证双极性障碍风险的可通用预测模型.
- 评估使用电子健康记录 (EHR) 数据在不同地点进行预测的可行性.
- 为了比较各种机器学习算法的性能,用于双相情感障碍风险预测.
主要方法:
- 在PsycheMERGE网络中进行了一项观察性病例控制研究.
- 预测模型是使用随机森林,梯度增强机,惩罚回归和堆叠组合来开发的.
- 利用了来自三个不同学术医疗中心的数据,其中包括人口统计数据,诊断码和药物.
主要成果:
- 这项研究包括超过350万名患者记录,其中有12533例双相情感障碍病例.
- 堆叠组合模型显示出最佳性能,AUC在0.82-0.87.7之间.
- 在所有研究地点,高风险量体显示出超过5%的积极预测值.
结论:
- 对双相情感障碍风险的可通用预测模型可以在不同的医疗保健环境中开发.
- 机器学习,特别是集体方法,显示出精准医学在心理健康领域的巨大潜力.
- 这些模型将通过PsycheMERGE网络传播,可以帮助早期识别和资源分配.
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Bipolar disorder is a chronic mental health condition marked by significant mood fluctuations, including episodes of mania and depression. Elevated energy levels, heightened mood or irritability, impulsive behavior, reduced sleep needs, rapid speech, racing thoughts, inflated self-esteem, and distractibility characterize mania. Individuals with bipolar disorder often alternate between depressive and manic states, with periods of emotional stability lasting an average of six months to a year.
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Mania, a psychological condition characterized by elevated mood, increased energy, and reduced sleep need, is part of the bipolar disorder cycle. The exact cause of mania isn't entirely known, but it is thought to be a combination of genetic, environmental, and neurological factors. Bipolar disorder involves alternating manic and depressive episodes. Mood stabilizers like lithium, antipsychotics, and anticonvulsants help manage these episodes. Lithium carbonate is particularly effective as...
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Diagnostic and Statistical Manual of Mental Disorders (DSM)
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