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arXiv · 1801.03058

Abstract: Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients

Imon Banerjee·Michael Francis Gensheimer·Douglas J. Wood·Solomon Henry·Daniel Chang·Daniel L. Rubin
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Abstract

We propose a deep learning model - Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term life expectancy (3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence. In a single framework, we integrated semantic data mapping and neural embedding technique to produce a text processing method that extracts relevant information from heterogeneous types of clinical notes in an unsupervised manner, and we designed a recurrent neural network to model the temporal dependency of the patient visits. The model was trained on a large dataset (10,293 patients) and validated on a separated dataset (1818 patients). Our method achieved an area under the ROC curve (AUC) of 0.89. To provide explain-ability, we developed an interactive graphical tool that may improve physician understanding of the basis for the model's predictions. The high accuracy and explain-ability of the PPES-Met model may enable our model to be used as a decision support tool to personalize metastatic cancer treatment and provide valuable assistance to the physicians.

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Imon Banerjee, Michael Francis Gensheimer, Douglas J. Wood, Solomon Henry, Daniel Chang, Daniel L. Rubin. 2018-01-09. Abstract: Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients. https://arxiv.org/abs/1801.03058

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