- Title
- Death comes but why: A multi-task memory-fused prediction for accurate and explainable illness severity in ICUs
- Creator
- Chen, Weitong; Zhang, Wei Emma; Yue, Lin
- Relation
- World Wide Web Vol. 26, p. 4025-4045
- Publisher Link
- http://dx.doi.org/10.1007/s11280-023-01211-w
- Publisher
- Springer
- Resource Type
- journal article
- Date
- 2023
- Description
- Predicting the severity of an illness is crucial in intensive care units (ICUs) if a patient‘s life is to be saved. The existing prediction methods often fail to provide sufficient evidence for time-critical decisions required in dynamic and changing ICU environments. In this research, a new method called MM-RNN (multi-task memory-fused recurrent neural network) was developed to predict the severity of illnesses in intensive care units (ICUs). MM-RNN aims to address this issue by not only predicting illness severity but also generating an evidence-based explanation of how the prediction was made. The architecture of MM-RNN consists of task-specific phased LSTMs and a delta memory network that captures asynchronous feature correlations within and between multiple organ systems. The multi-task nature of MM-RNN allows it to provide an evidence-based explanation of its predictions, along with illness severity scores and a heatmap of the patient’s changing condition. The results of comparison with state-of-the-art methods on real-world clinical data show that MM-RNN delivers more accurate predictions of illness severity with the added benefit of providing evidence-based justifications.
- Subject
- personalized healthcare; illness severity prediction; explainable prediction; time series
- Identifier
- http://hdl.handle.net/1959.13/1495967
- Identifier
- uon:54092
- Identifier
- ISSN:1386-145X
- Language
- eng
- Reviewed
- Hits: 266
- Visitors: 263
- Downloads: 0
| Thumbnail | File | Description | Size | Format |
|---|