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L-VISP: LSTM Visualization for Interpretable Symptom Prediction in Patient Cohorts
Authors: Floricel, C., Wang, Y., Wentzel, A., Fuller, C. D., Marai, G. E., Papka, M. E., Canahuate, G.
Publication: Computer Graphics Forum, Eurographics Symptom modeling 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 modelers to collaborate with clinical experts to validate the results. We present L-VISP, a human–machine solution that uses visual analytics for LSTM modeling 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 modelers and a clinical oncologist and present the takeaways from this multidisciplinary collaboration. Keywords: medical XAI, LSTM modeling, multidisciplinary visual analytics, temporal visualization CCS Concepts: Human-centered computing - Scientific visualization; Computing methodologies - Machine learning; Applied computing - Life and medical sciences Date: March 1, 2026 Document: View PDF |
