Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients
This addresses under-diagnosis of treatment side-effects for cancer patients, but is incremental as it applies an existing method to a specific domain.
The paper tackles predicting pain and tiredness levels for cancer patients post-diagnosis using patient data, achieving a weighted average deviation of 3.52 for pain and 2.27 for tiredness.
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.