ArticleFrontiers in molecular biosciences2023
Deep multi-modal intermediate fusion of clinical record and time series data in mortality prediction.
Article in Frontiers in molecular biosciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- HPCSMN: A Classification Method of Chemotherapy Sensitivity of Hypopharyngeal Cancer Based on Multimodal Network.Interdisciplinary sciences, computational life sciences · 2026Article
- Article
- A data-centric perspective on designing AI foundation models for healthcare.Frontiers in digital health · 2026Article
- Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review.Health data science · 2026Review
- Risk Factor Assessment and Predictive Modeling for Ventilator-Associated Pneumonia: Design and Clinical Implementation of an Artificial Intelligence-Enhanced Early Detection Framework Using Multisource Data Analytics.The clinical respiratory journal · 2025Article
- Advances and prospects of multi-modal ophthalmic artificial intelligence based on deep learning: a review.Eye and vision (London, England) · 2024Review
- Development and Validation of Multimodal Models to Predict the 30-Day Mortality of ICU Patients Based on Clinical Parameters and Chest X-Rays.Journal of imaging informatics in medicine · 2024Article
- Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models.Journal of biomedical informatics · 2024Article
- Forecasting Acute Kidney Injury and Resource Utilization in ICU patients using longitudinal, multimodal models.medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
- Retraction · 2025-06-13Concerns/Issues about Article · Euphemisms for Plagiarism · Investigation by Journal/Publisher · Objections by Author(s) · Plagiarism of/in Article ·
- Retracted
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In intensive care units (ICUs), mortality prediction is performed by combining information from these two sources of ICU patients by monitoring patient health. Respectively, time series data generated from each patient admission to the ICU and clinical records consisting of physician diagnostic summaries. However, existing mortality prediction studies mainly cascade the multimodal features of time series data and clinical records for prediction, ignoring thecross-modal correlation between the underlying features in different modal data. To address theseissues, we propose a multimodal fusion model for mortality prediction that jointly models patients' time-series data as well as clinical records. We apply a fine-tuned Bert model (Bio-Bert) to the patient's clinical record to generate a holistic embedding of the text part, which is then combined with the output of an LSTM model encoding the patient's time-series data to extract valid features. The global contextual information of each modal data is extracted using the improved fusion module to capture the correlation between different modal data. Furthermore, the improved fusion module can be easily added to the fusion features of any unimodal network and utilize existing pre-trained unimodal model weights. We use a real dataset containing 18904 ICU patients to train and evaluate our model, and the research results show that the representations obtained by themodel can achieve better prediction accuracy compared to the baseline.
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