Abstract: Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients

TL;DR

This paper introduces PPES-Met, a deep learning model that predicts 3-month survival in metastatic cancer patients using clinical notes, achieving high accuracy and offering interpretability to aid physicians.

Contribution

The study presents a novel neural network framework combining semantic mapping and temporal modeling for survival prediction from unstructured clinical notes.

Findings

Achieved AUC of 0.89 in survival prediction

Successfully integrated semantic data mapping with neural embeddings

Provided an interactive tool for model interpretability

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.