Readmission Risk Prediction

Predicting the Probability of Readmission After Cancer Surgery with LSTM

Hospital readmissions cost the US healthcare system billions of dollars annually, are associated with high mortality rates, and are a source of stress and suffering for patients and family members. Traditional approaches to readmission risk stratification rely on static administrative and medical record data and generally classify all surgical oncology patients at high-risk. However, different factors related to daily behavior and activities may contribute to or signal increased or decreased risk of readmission. Our research utilizes mobile sensing and deep learning to measure daily readmission risk in cancer patients after discharge. Using data from mobile and Fitbit devices of 49 patients collected over 90 days after discharge from the hospital, we build a probabilistic model in an LSTM structure to infer the risk progression trajectory in each patient. Our results show that using only sensor data, the model can predict the risk progression trajectory aligned with the ground truth data.

Related Publications

Prediction of Hospital Readmission from Longitudinal Mobile Data Streams [PDF]

Chen Qian, Patraporn Leelaprachakul, Matthew Landers, Carissa Low, Anind K Dey, Afsaneh Doryab

Sensors 2021

Digital Biomarkers of Perioperative Patient-Reported Symptom Burden in Pancreatic Surgery Patients [PDF]

Carissa A Low, Meng Li, Julio Vega, Krina C Durica, Denzil Ferreira, Vernissia Tam, Melissa Hogg, Herbert Zeh, Afsaneh Doryab, Anind K Dey

JMIR 2021

A Deep Learning Framework for Prediction of Readmission Risk After Cancer Surgery From Mobile Data Streams [PDF]

A Doryab

Proceedings of Deep Learning for Wellbeing Applications Leveraging Mobile Devices and Edge Computing, June 2020

Afsaneh Doryab, Anind K Dey, Grace Kao, Carissa Low

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, March 2019