“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
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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。
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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。
分类
- 图像处理 Image Manipulation
- 风格转换 Style Transfer
- 图像分类 Image Classification
- 脸部识别 Face Recognition
- 视频稳定化 Video Stabilization
- 目标检测 Object Detection
- 自动驾驶汽车 Self Driving Car
- 智能推荐 Recommendation Al
- 智能游戏 Gaming Al
- 智能下棋 Chess Al
- 智能医学 Medical Al
- 智能演说 Speech Al
- 智能音乐 Music Al
- 自然语言处理 Natural Language Processing
- 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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风格转换 Style Transfer
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人脸识别 Face Recognition
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自动驾驶汽车 Self Driving Car
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智能医学 Medical AI
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- Improving Palliative Care with Deep Learning - Andrew Ng
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智能演说 Speech AI
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- Deep Learning for Siri’s Voice: On-device Deep Mixture Density Networks for Hybrid Unit Selection Synthesis - Apple
智能音乐 Music AI
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自然语言处理 Natural Language Processing
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预测 Prediction
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- How to Predict Stock Prices Easily - Intro to Deep Learning #7
