Prediction of Cancer Symptom Trajectory Using Longitudinal Electronic Health Record Data and Long Short-Term Memory Neural Network.
Sena ChaeW Nick StreetNaveenkumar RamarajuStéphanie Gilbertson WhitePublished in: JCO clinical cancer informatics (2024)
We can successfully predict patients' symptom trajectories with a prediction model, built with sparse assessment data, using routinely collected nursing documentation. The results of this project can be applied to better individualize symptom management to support cancer patients' quality of life.
Keyphrases
- electronic health record
- neural network
- clinical decision support
- patient reported
- end stage renal disease
- adverse drug
- ejection fraction
- newly diagnosed
- quality improvement
- papillary thyroid
- mental health
- peritoneal dialysis
- depressive symptoms
- squamous cell carcinoma
- cross sectional
- machine learning
- working memory
- big data
- squamous cell
- patient reported outcomes
- childhood cancer