Machine Learning 资讯 | 基于深度学习识别姑息治疗患者

2018-02-27 11:18:49来源:oschina作者:RiboseYim人点击

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“Hiring a Machine Learning engineer or Data Scientist in Silicon Valley is becoming like hiring a professional athlete. That’s how demanding it is” — The New York Times




基于深度学习识别姑息治疗患者

Stanford ML Group 建立了一个使用深度学习算法的程序,根据电子健康记录(Electronic Health Record ,EHR,包括病历、心电图、医疗影像等信息)数据确定在未来3-12个月高风险死亡的住院患者。这些病人的预警信息将发送给姑息治疗小组,这有助于姑息护理小组尽早介入、提供服务。


姑息治疗(Palliative Care ,在日本、中国台湾翻译为舒缓医学)起源于 hospice运动,最早起源于公元四世纪。根据世界卫生组织的定义,姑息治疗强调控制疼痛及患者有关症状,并对心理、社会和精神问题予以重视,目的是为病人和家属赢得最好的生活质量。




预测模型是一个 18 层的深度神经网络,输入参数为一个病人的 EHR 数据,输出为未来 3-12 个月死亡的概率。训练数据采用斯坦福医院 EHR 数据库中的历史数据,包含超过 200 万名患者的数据。EHR 数据包括患者过去 12 个月的诊断结论、治疗程序、处方和相关细节(经过脱敏和技术处理,以替代码的形式表示),所有数据被转换成 13654 维的特征向量。训练好的模型 AUROC 评分达到 0.93 ,交叉验证的平均精度为0.69 分。


对于机器学习系统来说,使用户可以根据预测结果采取行动,需要提供预测结果的详细解释,这点对于建立用户信心至关重要。Stanford 的程序可以自动生成一个报告,在病人的 EHR 数据中高亮突出对于预测结果具有重要影响因子的条目。


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智能预测 Prediction

Mybridge AI在 20000 篇关于创建机器学习应用的文章中挑选了前 50 名。从有实践经验的数据科学家那里学习是一个好方法,我们可以的分享中获得构建、运营机器学习应用的经验教训。50 篇文章大致可以分为 15 个主题,如下所示:


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