Machine learning landscapes and predictions for patient outcomes.
Machine learning landscapes and predictions for patient outcomes.
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DOI：
10.1098/rsos.170175
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发表时间：
2017-07
影响因子：
3.5
通讯作者：
Wales DJ
中科院分区：
文献类型：
--
作者：
Das R;Wales DJ
为解释和探索分子科学中的能量景观而开发的理论和计算工具应用于由神经网络的局部最小值定义的景观。这些机器学习场景对应于训练数据的拟合，其中输入是患者数据库的生命体征和实验室测量结果，目标是预测临床结果。在此贡献中，我们测试了通过拟合单个测量值，然后拟合 2 到 10 个不同患者医疗数据项的组合获得的预测。分析了所讨论的 48 小时期间不同时间间隔内的测量结果的影响，发现最新的值是最重要的。我们还比较了神经网络获得的结果作为隐藏节点数量的函数，以及正则化参数的不同值。将预测与替代凸拟合函数进行比较，观察到很强的相关性。这些结果对随机选择进行训练和测试的患者的依赖性随着可用数据库的大小而系统性地降低。本研究中神经网络拟合定义的机器学习景观具有单漏斗特征，这可能解释了为什么获得全局最小解或与此最佳参数化行为类似的拟合相对简单。
The theory and computational tools developed to interpret and explore energy landscapes in molecular science are applied to the landscapes defined by local minima for neural networks. These machine learning landscapes correspond to fits of training data, where the inputs are vital signs and laboratory measurements for a database of patients, and the objective is to predict a clinical outcome. In this contribution, we test the predictions obtained by fitting to single measurements, and then to combinations of between 2 and 10 different patient medical data items. The effect of including measurements over different time intervals from the 48 h period in question is analysed, and the most recent values are found to be the most important. We also compare results obtained for neural networks as a function of the number of hidden nodes, and for different values of a regularization parameter. The predictions are compared with an alternative convex fitting function, and a strong correlation is observed. The dependence of these results on the patients randomly selected for training and testing decreases systematically with the size of the database available. The machine learning landscapes defined by neural network fits in this investigation have single-funnel character, which probably explains why it is relatively straightforward to obtain the global minimum solution, or a fit that behaves similarly to this optimal parameterization.
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影响因子：
3.2
作者：
Desautels T;Calvert J;Hoffman J;Jay M;Kerem Y;Shieh L;Shimabukuro D;Chettipally U;Feldman MD;Barton C;Wales DJ;Das R
通讯作者：
Das R
影响因子：
2
作者：
GOLDFARB, D
通讯作者：
GOLDFARB, D
影响因子：
4.4
作者：
Becker, OM;Karplus, M
通讯作者：
Karplus, M
影响因子：
2.4
作者：
Das, Ritankar;Wales, David J.
通讯作者：
Wales, David J.
影响因子：
2.7
作者：
LIU, DC;NOCEDAL, J
通讯作者：
NOCEDAL, J