mlsanityData to 7 Oct 2026

Temporal-spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit

arXiv 2306.01970 ↗First posted 3 Jun 2023cs.LG cs.AI cs.CVcited by 3

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Abstract (arXiv)

In recent years, medical information technology has made it possible for electronic health record (EHR) to store fairly complete clinical data. This has brought health care into the era of "big data". However, medical data are often sparse and strongly correlated, which means that medical problems cannot be solved effectively. With the rapid development of deep learning in recent years, it has provided opportunities for the use of big data in healthcare. In this paper, we propose a temporal-saptial correlation attention network (TSCAN) to handle some clinical characteristic prediction problems, such as predicting death, predicting length of stay, detecting physiologic decline, and classifying phenotypes. Based on the design of the attention mechanism model, our approach can effectively remove irrelevant items in clinical data and irrelevant nodes in time according to different tasks, so as to obtain more accurate prediction results. Our method can also find key clinical indicators of important outcomes that can be used to improve treatment options. Our experiments use information from the Medical Information Mart for Intensive Care (MIMIC-IV) database, which is open to the public. Finally, we have achieved significant performance benefits of 2.0% (metric) compared to other SOTA prediction methods. We achieved a staggering 90.7% on mortality rate, 45.1% on length of stay. The source code can be find: urlhttps://github.com/yuyuheintju/TSCAN.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 24 of its 61 references that are in the map. First posted on arXiv 3 Jun 2023.

Builds on

24 of 61 references.

References · 24

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12 more
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  12. 2021Multi-Task Prediction of Clinical Outcomes in the Intensive Care Unit using Flexible Multimodal Transformerscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.

  1. 2024Spatial Semantic Recurrent Mining for Referring Image Segmentationcited by 1
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  3. 2026PGP-Clinical-TimeKAN: Prior-Guided Joint Probabilistic Forecasting of Clinical Trajectoriescited by 0