Abstract
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.
Highlights
There is an increasing need in the hospital setting for methods to predict the mortality risk and decompensation likelihood of patients [1]
We have shown how the energy landscape framework developed for molecular and condensed matter can be applied to cost functions that support multiple minima in machine learning [32,33]
An extensive set of tests was first run for the 33 individual medical data items using neural network fits over various time ranges, with 3, 4, 5 and 6 hidden nodes and λ = 10−5
Summary
There is an increasing need in the hospital setting for methods to predict the mortality risk and decompensation likelihood of patients [1]. Identification of patient deterioration can assist provider organizations to properly manage and treat patients in a timely manner, to improve outcomes and allocate limited operational and financial resources efficiently [2,3]. With EHRs, patient health information is all centralized in one place, increasing ease of analysis with computational tools
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