MANGO: Multimodal Acuity traNsformer for intelliGent ICU Outcomes
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913624504664064 |
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| author | Zhang, Jiaqing Contreras, Miguel Bandyopadhyay, Sabyasachi Davidson, Andrea Sena, Jessica Ren, Yuanfang Guan, Ziyuan Ozrazgat-Baslanti, Tezcan Loftus, Tyler J. Nerella, Subhash Bihorac, Azra Rashidi, Parisa |
| author_facet | Zhang, Jiaqing Contreras, Miguel Bandyopadhyay, Sabyasachi Davidson, Andrea Sena, Jessica Ren, Yuanfang Guan, Ziyuan Ozrazgat-Baslanti, Tezcan Loftus, Tyler J. Nerella, Subhash Bihorac, Azra Rashidi, Parisa |
| contents | Estimation of patient acuity in the Intensive Care Unit (ICU) is vital to ensure timely and appropriate interventions. Advances in artificial intelligence (AI) technologies have significantly improved the accuracy of acuity predictions. However, prior studies using machine learning for acuity prediction have predominantly relied on electronic health records (EHR) data, often overlooking other critical aspects of ICU stay, such as patient mobility, environmental factors, and facial cues indicating pain or agitation. To address this gap, we present MANGO: the Multimodal Acuity traNsformer for intelliGent ICU Outcomes, designed to enhance the prediction of patient acuity states, transitions, and the need for life-sustaining therapy. We collected a multimodal dataset ICU-Multimodal, incorporating four key modalities, EHR data, wearable sensor data, video of patient's facial cues, and ambient sensor data, which we utilized to train MANGO. The MANGO model employs a multimodal feature fusion network powered by Transformer masked self-attention method, enabling it to capture and learn complex interactions across these diverse data modalities even when some modalities are absent. Our results demonstrated that integrating multiple modalities significantly improved the model's ability to predict acuity status, transitions, and the need for life-sustaining therapy. The best-performing models achieved an area under the receiver operating characteristic curve (AUROC) of 0.76 (95% CI: 0.72-0.79) for predicting transitions in acuity status and the need for life-sustaining therapy, while 0.82 (95% CI: 0.69-0.89) for acuity status prediction... |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_17832 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MANGO: Multimodal Acuity traNsformer for intelliGent ICU Outcomes Zhang, Jiaqing Contreras, Miguel Bandyopadhyay, Sabyasachi Davidson, Andrea Sena, Jessica Ren, Yuanfang Guan, Ziyuan Ozrazgat-Baslanti, Tezcan Loftus, Tyler J. Nerella, Subhash Bihorac, Azra Rashidi, Parisa Signal Processing Artificial Intelligence Machine Learning Estimation of patient acuity in the Intensive Care Unit (ICU) is vital to ensure timely and appropriate interventions. Advances in artificial intelligence (AI) technologies have significantly improved the accuracy of acuity predictions. However, prior studies using machine learning for acuity prediction have predominantly relied on electronic health records (EHR) data, often overlooking other critical aspects of ICU stay, such as patient mobility, environmental factors, and facial cues indicating pain or agitation. To address this gap, we present MANGO: the Multimodal Acuity traNsformer for intelliGent ICU Outcomes, designed to enhance the prediction of patient acuity states, transitions, and the need for life-sustaining therapy. We collected a multimodal dataset ICU-Multimodal, incorporating four key modalities, EHR data, wearable sensor data, video of patient's facial cues, and ambient sensor data, which we utilized to train MANGO. The MANGO model employs a multimodal feature fusion network powered by Transformer masked self-attention method, enabling it to capture and learn complex interactions across these diverse data modalities even when some modalities are absent. Our results demonstrated that integrating multiple modalities significantly improved the model's ability to predict acuity status, transitions, and the need for life-sustaining therapy. The best-performing models achieved an area under the receiver operating characteristic curve (AUROC) of 0.76 (95% CI: 0.72-0.79) for predicting transitions in acuity status and the need for life-sustaining therapy, while 0.82 (95% CI: 0.69-0.89) for acuity status prediction... |
| title | MANGO: Multimodal Acuity traNsformer for intelliGent ICU Outcomes |
| topic | Signal Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.17832 |