Cargando…
Prediction of Unplanned Hospital Readmission using Clinical and Longitudinal Wearable Sensor Features
Predictive models have been suggested as potential tools for identifying highest risk patients for hospital readmissions, in order to improve care coordination and ultimately long-term patient outcomes. However, the accuracy of current predictive models for readmission prediction is still moderate a...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120790/ https://www.ncbi.nlm.nih.gov/pubmed/37090626 http://dx.doi.org/10.1101/2023.04.10.23288371 |
_version_ | 1785029243287109632 |
---|---|
author | Yhdego, Haben H. Nayebnazar, Arshia Amrollahi, Fatemeh Boussina, Aaron Shashikumar, Supreeth Wardi, Gabriel Nemati, Shamim |
author_facet | Yhdego, Haben H. Nayebnazar, Arshia Amrollahi, Fatemeh Boussina, Aaron Shashikumar, Supreeth Wardi, Gabriel Nemati, Shamim |
author_sort | Yhdego, Haben H. |
collection | PubMed |
description | Predictive models have been suggested as potential tools for identifying highest risk patients for hospital readmissions, in order to improve care coordination and ultimately long-term patient outcomes. However, the accuracy of current predictive models for readmission prediction is still moderate and further data enrichment is needed to identify at risk patients. This paper describes models to predict 90-day readmission, focusing on testing the predictive performance of wearable sensor features generated using multiscale entropy techniques and clinical features. Our study explores ways to incorporate pre-discharge and post-discharge wearable sensor features to make robust patient predictions. Data were used from participants enrolled in the AllofUs Research program. We extracted the inpatient cohort of patients and integrated clinical data from the electronic health records (EHR) and Fitbit sensor measurements. Entropy features were calculated from the longitudinal wearable sensor data, such as heart rate and mobility-related measurements, in order to characterize time series variability and complexity. Our best performing model acheived an AUC of 83%, and at 80% sensitivity acheived 75% specificity and 57% positive predictive value. Our results indicate that it would be possible to improve the ability to predict unplanned hospital readmissions by considering pre-discharge and post-discharge wearable features. |
format | Online Article Text |
id | pubmed-10120790 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-101207902023-04-22 Prediction of Unplanned Hospital Readmission using Clinical and Longitudinal Wearable Sensor Features Yhdego, Haben H. Nayebnazar, Arshia Amrollahi, Fatemeh Boussina, Aaron Shashikumar, Supreeth Wardi, Gabriel Nemati, Shamim medRxiv Article Predictive models have been suggested as potential tools for identifying highest risk patients for hospital readmissions, in order to improve care coordination and ultimately long-term patient outcomes. However, the accuracy of current predictive models for readmission prediction is still moderate and further data enrichment is needed to identify at risk patients. This paper describes models to predict 90-day readmission, focusing on testing the predictive performance of wearable sensor features generated using multiscale entropy techniques and clinical features. Our study explores ways to incorporate pre-discharge and post-discharge wearable sensor features to make robust patient predictions. Data were used from participants enrolled in the AllofUs Research program. We extracted the inpatient cohort of patients and integrated clinical data from the electronic health records (EHR) and Fitbit sensor measurements. Entropy features were calculated from the longitudinal wearable sensor data, such as heart rate and mobility-related measurements, in order to characterize time series variability and complexity. Our best performing model acheived an AUC of 83%, and at 80% sensitivity acheived 75% specificity and 57% positive predictive value. Our results indicate that it would be possible to improve the ability to predict unplanned hospital readmissions by considering pre-discharge and post-discharge wearable features. Cold Spring Harbor Laboratory 2023-04-11 /pmc/articles/PMC10120790/ /pubmed/37090626 http://dx.doi.org/10.1101/2023.04.10.23288371 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Yhdego, Haben H. Nayebnazar, Arshia Amrollahi, Fatemeh Boussina, Aaron Shashikumar, Supreeth Wardi, Gabriel Nemati, Shamim Prediction of Unplanned Hospital Readmission using Clinical and Longitudinal Wearable Sensor Features |
title | Prediction of Unplanned Hospital Readmission using Clinical and Longitudinal Wearable Sensor Features |
title_full | Prediction of Unplanned Hospital Readmission using Clinical and Longitudinal Wearable Sensor Features |
title_fullStr | Prediction of Unplanned Hospital Readmission using Clinical and Longitudinal Wearable Sensor Features |
title_full_unstemmed | Prediction of Unplanned Hospital Readmission using Clinical and Longitudinal Wearable Sensor Features |
title_short | Prediction of Unplanned Hospital Readmission using Clinical and Longitudinal Wearable Sensor Features |
title_sort | prediction of unplanned hospital readmission using clinical and longitudinal wearable sensor features |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120790/ https://www.ncbi.nlm.nih.gov/pubmed/37090626 http://dx.doi.org/10.1101/2023.04.10.23288371 |
work_keys_str_mv | AT yhdegohabenh predictionofunplannedhospitalreadmissionusingclinicalandlongitudinalwearablesensorfeatures AT nayebnazararshia predictionofunplannedhospitalreadmissionusingclinicalandlongitudinalwearablesensorfeatures AT amrollahifatemeh predictionofunplannedhospitalreadmissionusingclinicalandlongitudinalwearablesensorfeatures AT boussinaaaron predictionofunplannedhospitalreadmissionusingclinicalandlongitudinalwearablesensorfeatures AT shashikumarsupreeth predictionofunplannedhospitalreadmissionusingclinicalandlongitudinalwearablesensorfeatures AT wardigabriel predictionofunplannedhospitalreadmissionusingclinicalandlongitudinalwearablesensorfeatures AT nematishamim predictionofunplannedhospitalreadmissionusingclinicalandlongitudinalwearablesensorfeatures |