Abstract: Probabilistic Prognostic Estimates of Survival in Metastatic
  Cancer Patients
  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.
