Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients

TL;DR

This study develops an interpretable model to predict symptom progression, specifically pain and tiredness, in cancer patients using real-world data, aiming to improve symptom management and treatment outcomes.

Contribution

Introduces a decision tree-based predictive model for symptom evolution in cancer patients, addressing class imbalance with SMOTE and emphasizing interpretability.

Findings

Previous symptom levels are key predictors.

Prediction deviation for pain is 3.52.

Prediction deviation for tiredness is 2.27.

Abstract

Cancer treatment is an arduous process for patients and causes many side-effects during and post-treatment. The treatment can affect almost all body systems and result in pain, fatigue, sleep disturbances, cognitive impairments, etc. These conditions are often under-diagnosed or under-treated. In this paper, we use patient data to predict the evolution of their symptoms such that treatment-related impairments can be prevented or effects meaningfully ameliorated. The focus of this study is on predicting the pain and tiredness level of a patient post their diagnosis. We implement an interpretable decision tree based model called LightGBM on real-world patient data consisting of 20163 patients. There exists a class imbalance problem in the dataset which we resolve using the oversampling technique of SMOTE. Our empirical results show that the value of the previous level of a symptom is a key indicator for prediction and the weighted average deviation in prediction of pain level is 3.52 and of tiredness level is 2.27.