Deep multi-modal intermediate fusion of clinical record and time series data in mortality prediction Article Swipe
Ke Niu
,
Ke Zhang
,
Xueping Peng
,
Yijie Pan
,
Naian Xiao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fmolb.2023.1136071
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fmolb.2023.1136071
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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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fmolb.2023.1136071
- https://www.frontiersin.org/articles/10.3389/fmolb.2023.1136071/pdf
- OA Status
- gold
- Cited By
- 29
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4323567514
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4323567514Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fmolb.2023.1136071Digital Object Identifier
- Title
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Deep multi-modal intermediate fusion of clinical record and time series data in mortality predictionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-08Full publication date if available
- Authors
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Ke Niu, Ke Zhang, Xueping Peng, Yijie Pan, Naian XiaoList of authors in order
- Landing page
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https://doi.org/10.3389/fmolb.2023.1136071Publisher landing page
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https://www.frontiersin.org/articles/10.3389/fmolb.2023.1136071/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/articles/10.3389/fmolb.2023.1136071/pdfDirect OA link when available
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Computer science, Modal, Baseline (sea), Artificial intelligence, Data mining, Series (stratigraphy), Time series, Embedding, Machine learning, Biology, Oceanography, Chemistry, Polymer chemistry, Paleontology, GeologyTop concepts (fields/topics) attached by OpenAlex
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29Total citation count in OpenAlex
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2025: 10, 2024: 18, 2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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