Abstract: Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients
This work addresses personalized treatment decisions for metastatic cancer patients, though it is incremental as it builds on existing methods for temporal data analysis.
The authors tackled the problem of estimating short-term survival in metastatic cancer patients by analyzing clinical notes with a deep learning model, achieving an AUC of 0.89 on a validation set of 1,818 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.