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L-VISP: LSTM Visualization for Interpretable Symptom Prediction in Patient Cohorts
Journal article   Open access   Peer reviewed

L-VISP: LSTM Visualization for Interpretable Symptom Prediction in Patient Cohorts

C. Floricel, Y. Wang, A. Wentzel, C. D. Fuller, G. E. Marai, M. E. Papka and G. Canahuate
Computer graphics forum, e70314
03/19/2026
DOI: 10.1111/cgf.70314
Appears in  UI Libraries Support Open Access
url
https://doi.org/10.1111/cgf.70314View
Published (Version of record) Open Access

Abstract

Symptom modelling in head and neck cancer is challenged by the complexity of heterogeneous patient data, leading to an interest in deep learning approaches. Although Long Short-Term Memory Networks (LSTMs) have shown great results in patient risk prediction, their low interpretability requires data modellers to collaborate with clinical experts to validate the results. We present L-VISP, a human-machine solution that uses visual analytics for LSTM modelling in clinical research. L-VISP uses custom visual encodings to make multiple LSTM variants interpretable, supporting a full range of analysis, from understanding model operations and evaluating performance to interpreting results in a clinical context. We evaluate L-VISP with data modellers and a clinical oncologist and present the takeaways from this multidisciplinary collaboration.
 medical XAI LSTM modeling multidisciplinary visual analytics temporal visualization UIOWA OA Agreement

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