Prediction of Hospital Readmission from Longitudinal Mobile Data Streams.

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

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

Using remotely monitored patient activity patterns after hospital discharge to predict 30 day hospital readmission: a randomized trial.Scientific reports · 2023
Trial
Impact of time from discharge to readmission on outcomes: an observational study from the US National Readmission Database.BMJ open · 2024
Observational

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Chen QianDepartment of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USA.ORCID 0000-0002-4028-4752

Patraporn LeelaprachakulHeinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Matthew LandersDepartment of Computer Science, University of Virginia, Charlottesville, VA 22904, USA.

Carissa LowDepartment of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.

Anind K DeyInformation School, University of Washington, Seattle, WA 98105, USA.

Afsaneh DoryabDepartment of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USA.

Funding

Center for Machine Learning and Health at Carnegie Mellon University through the Pittsburgh Health Data Alliance N/A

6 · The paper itself

Abstract

Hospital readmissions impose an extreme burden on both health systems and patients. Timely management of the postoperative complications that result in readmissions is necessary to mitigate the effects of these events. However, accurately predicting readmissions is very challenging, and current approaches demonstrated a limited ability to forecast which patients are likely to be readmitted. Our research addresses the challenge of daily readmission risk prediction after the hospital discharge via leveraging the abilities of mobile data streams collected from patients devices in a probabilistic deep learning framework. Through extensive experiments on a real-world dataset that includes smartphone and Fitbit device data from 49 patients collected for 60 days after discharge, we demonstrate our framework's ability to closely simulate the readmission risk trajectories for cancer patients.

Indexed as

Patient DischargePatient ReadmissionForecastingHumansPostoperative ComplicationsRisk Factorsdata processingdeep learningfeature extractionmobile and wearable sensingpatient readmission

Identifiers

PMID34833586

DOI10.3390/s21227510

PMCPMC8618459

What OpenQuestion holds

Textmetadata

LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.