Abstract
Surgery to treat elderly hip fracture patients may cause complications that can lead to early mortality. An early warning system for complications could provoke clinicians to monitor high-risk patients more carefully and address potential complications early, or inform the patient. In this work, we develop a multimodal deep-learning model for post-operative mortality prediction using pre-operative and per-operative data from elderly hip fracture patients. Specifically, we include static patient data, hip and chest images before surgery in pre-operative data, vital signals, and medications administered during surgery in per-operative data. We extract features from image modalities using ResNet and from vital signals using LSTM. Explainable model outcomes are essential for clinical applicability, therefore we compute Shapley values to explain the predictions of our multimodal black box model. We find that i) Shapley values can be used to estimate the relative contribution of each modality both locally and globally, and ii) a modified version of the chain rule can be used to propagate Shapley values through a sequence of models supporting interpretable local explanations. Our findings imply that a multimodal combination of black box models can be explained by propagating Shapley values through the model sequence.
Original language | English |
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Title of host publication | Explainable Artificial Intelligence |
Subtitle of host publication | Second World Conference, xAI 2024. Valletta, Malta, July 17-19, 2024. Proceedings, Part IV |
Editors | Luca Longo, Sebastian Lapuschkin, Christin Seifert |
Place of Publication | Cham, Switzerland |
Publisher | Springer Nature |
Pages | 84-101 |
Number of pages | 18 |
ISBN (Electronic) | 978-3-031-63803-9 |
ISBN (Print) | 978-3-031-63802-2 |
DOIs | |
Publication status | Published - 10 Jul 2024 |
Event | The 2nd World Conference on eXplainable Artificial Intelligence - Valetta, Malta Duration: 17 Jul 2024 → 19 Jul 2024 https://xaiworldconference.com/2024/ |
Publication series
Name | Communications in Computer and Information Science |
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Publisher | Springer |
Volume | 2156 |
ISSN (Print) | 1865-0929 |
ISSN (Electronic) | 1865-0937 |
Conference
Conference | The 2nd World Conference on eXplainable Artificial Intelligence |
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Country/Territory | Malta |
City | Valetta |
Period | 17/07/24 → 19/07/24 |
Internet address |
Keywords
- 2024 OA procedure