dorsal/arxiv
View SchemaTemporal Fusion Nexus: A task-agnostic multi-modal embedding model for clinical narratives and irregular time series in post-kidney transplant care
| Authors | Aditya Kumar, Simon Rauch, Mario Cypko, Marcel Naik, Matthieu-P Schapranow, Aadil Rashid, Fabian Halleck, Bilgin Osmanodja, Roland Roller, Lars Pape, Klemens Budde, Mario Schiffer, Oliver Amft |
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| Categories | |
| ArXiv ID | 2601.08503vv1 |
| URL | https://arxiv.org/abs/2601.08503 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
We introduce Temporal Fusion Nexus (TFN), a multi-modal and task-agnostic embedding model to integrate irregular time series and unstructured clinical narratives. We analysed TFN in post-kidney transplant (KTx) care, with a retrospective cohort of 3382 patients, on three key outcomes: graft loss, graft rejection, and mortality. Compared to state-of-the-art model in post KTx care, TFN achieved higher performance for graft loss (AUC 0.96 vs. 0.94) and graft rejection (AUC 0.84 vs. 0.74). In mortality prediction, TFN yielded an AUC of 0.86. TFN outperformed unimodal baselines (approx 10% AUC improvement over time series only baseline, approx 5% AUC improvement over time series with static patient data). Integrating clinical text improved performance across all tasks. Disentanglement metrics confirmed robust and interpretable latent factors in the embedding space, and SHAP-based attributions confirmed alignment with clinical reasoning. TFN has potential application in clinical tasks beyond KTx, where heterogeneous data sources, irregular longitudinal data, and rich narrative documentation are available.
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"date_created": "2026-02-17T05:53:15.227000Z",
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"abstract": "We introduce Temporal Fusion Nexus (TFN), a multi-modal and task-agnostic embedding model to integrate irregular time series and unstructured clinical narratives. We analysed TFN in post-kidney transplant (KTx) care, with a retrospective cohort of 3382 patients, on three key outcomes: graft loss, graft rejection, and mortality. Compared to state-of-the-art model in post KTx care, TFN achieved higher performance for graft loss (AUC 0.96 vs. 0.94) and graft rejection (AUC 0.84 vs. 0.74). In mortality prediction, TFN yielded an AUC of 0.86. TFN outperformed unimodal baselines (approx 10% AUC improvement over time series only baseline, approx 5% AUC improvement over time series with static patient data). Integrating clinical text improved performance across all tasks. Disentanglement metrics confirmed robust and interpretable latent factors in the embedding space, and SHAP-based attributions confirmed alignment with clinical reasoning. TFN has potential application in clinical tasks beyond KTx, where heterogeneous data sources, irregular longitudinal data, and rich narrative documentation are available.",
"arxiv_id": "2601.08503",
"authors": [
"Aditya Kumar",
"Simon Rauch",
"Mario Cypko",
"Marcel Naik",
"Matthieu-P Schapranow",
"Aadil Rashid",
"Fabian Halleck",
"Bilgin Osmanodja",
"Roland Roller",
"Lars Pape",
"Klemens Budde",
"Mario Schiffer",
"Oliver Amft"
],
"categories": [
"cs.LG",
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Temporal Fusion Nexus: A task-agnostic multi-modal embedding model for clinical narratives and irregular time series in post-kidney transplant care",
"url": "https://arxiv.org/abs/2601.08503",
"version": "v1"
},
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