Log In Sign Up

Predicting colorectal polyp recurrence using time-to-event analysis of medical records

by   Lia X. Harrington, et al.

Identifying patient characteristics that influence the rate of colorectal polyp recurrence can provide important insights into which patients are at higher risk for recurrence. We used natural language processing to extract polyp morphological characteristics from 953 polyp-presenting patients' electronic medical records. We used subsequent colonoscopy reports to examine how the time to polyp recurrence (731 patients experienced recurrence) is influenced by these characteristics as well as anthropometric features using Kaplan-Meier curves, Cox proportional hazards modeling, and random survival forest models. We found that the rate of recurrence differed significantly by polyp size, number, and location and patient smoking status. Additionally, right-sided colon polyps increased recurrence risk by 30 left-sided polyps. History of tobacco use increased polyp recurrence risk by 20 0.65 and identified several other predictive variables, which can inform development of personalized polyp surveillance plans.


DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network

Medical practitioners use survival models to explore and understand the ...

Suicide Risk Modeling with Uncertain Diagnostic Records

Motivated by the pressing need for suicide prevention through improving ...

A predictive analytics approach for stroke prediction using machine learning and neural networks

The negative impact of stroke in society has led to concerted efforts to...

Self-reporting and screening: Data with current-status and censored observations

We consider survival data that combine three types of observations: unce...