A pilot study in using deep learning to predict limited life expectancy in women with recurrent cervical cancer - PubMed (original) (raw)

A pilot study in using deep learning to predict limited life expectancy in women with recurrent cervical cancer

Koji Matsuo et al. Am J Obstet Gynecol. 2017 Dec.

No abstract available

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Conflict of interest statement

The authors report no conflict of interest.

Figures

FIGURE 1

FIGURE 1. Partial dependency plots for 3 month survival prediction

Deep-learning model was used for the analysis. The x-axis represents the interval change of each (z-score normalization) clinicolaboratory factor from the first recurrence. Normalization was used to eliminate the effects of different feature ranges. The y-axis represents the interval change in survival chance (partial dependency). Partial dependency plots are graphical visualizations of the marginal effect of a given variable (or multiple variables) on an outcome. Intuitively, we can interpret the partial dependence as the expected outcome as a function of the target variables. The y-axis values are negative because the expected outcome decreases for our target variables. The lower the partial dependence value, the less chance of 3 month survival. Similar results were also seen in 6 month survival predictors. The tick marks on the x-axis represent the deciles of the feature values in the training data.

FIGURE 2

FIGURE 2. First decision tree model for 3 month survival prediction

First decision tree for 3 month survival prediction was obtained by mimicking the performance of deep neural networks. In the decisio- tree plots, the thresholds on the feature nodes are for normalized features. Samples include the number of samples to that node. The value of a node is the prediction score of a sample from the corresponding decision rules.

References

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