ArticleFrontiers in digital health2021
Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU.
Article in Frontiers in digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed.
- Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review.Health data science · 2026Review
- Real-time prediction of intensive care unit patient acuity and therapy requirements using state-space modelling.Nature communications · 2025Article
- Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study.Journal of medical Internet research · 2025Article
- Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis.Health data science · 2025Review
- Using Domain Adaptation and Inductive Transfer Learning to Improve Patient Outcome Prediction in the Intensive Care Unit: Retrospective Observational Study.Journal of medical Internet research · 2024Observational
- APRICOT-Mamba: Acuity Prediction in Intensive Care Unit (ICU): Development and Validation of a Stability, Transitions, and Life-Sustaining Therapies Prediction Model.Research square · 2024Article
- Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST).Journal of machine learning research : JMLR · 2024Article
- Wearable sensors in patient acuity assessment in critical care.Frontiers in neurology · 2024Article
- Building an automated, machine learning-enabled platform for predicting post-operative complications.Physiological measurement · 2023Article
- Dynamic predictions of postoperative complications from explainable, uncertainty-aware, and multi-task deep neural networks.Scientific reports · 2023Article
- Transfer learning for non-image data in clinical research: A scoping review.PLOS digital health · 2022Article
- Gamification for Machine Learning in Surgical Patient Engagement.Frontiers in surgery · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Accurate prediction and monitoring of patient health in the intensive care unit can inform shared decisions regarding appropriateness of care delivery, risk-reduction strategies, and intensive care resource use. Traditionally, algorithmic solutions for patient outcome prediction rely solely on data available from electronic health records (EHR). In this pilot study, we explore the benefits of augmenting existing EHR data with novel measurements from wrist-worn activity sensors as part of a clinical environment known as the Intelligent ICU. We implemented temporal deep learning models based on two distinct sources of patient data: (1) routinely measured vital signs from electronic health records, and (2) activity data collected from wearable sensors. As a proxy for illness severity, our models predicted whether patients leaving the intensive care unit would be successfully or unsuccessfully discharged from the hospital. We overcome the challenge of small sample size in our prospective cohort by applying deep transfer learning using EHR data from a much larger cohort of traditional ICU patients. Our experiments quantify added utility of non-traditional measurements for predicting patient health, especially when applying a transfer learning procedure to small novel Intelligent ICU cohorts of critically ill patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.