Support-Induced Diagnostic Probing for Multichannel Medical Time Series

Multi-channel medical time-series classification requires combining diagnostic context (usually given by labelled examples) with the information contained in a new recording. We propose Support-Induced Diagnostic Probing (SIDP), a class-conditioned retrieval architecture that converts positive-vs-negative support differences into diagnostic probes. Cross-channel attention first transforms a recording into a fixed-size set of latent representations. Support-generated probes then retrieve and score diagnosis-specific information from these representations. Once trained on a dataset, SIDP classifies new recordings from a small labelled support context without retraining or updating its parameters. We evaluate on PTB-XL, a large public ECG benchmark, where the task is to predict five diagnostic categories that can co-occur within a recording. Using the standard patient-disjoint test split, SIDP achieves **0.8450** macro-AUROC and **0.6215** macro-F1 across 2,158 test records from 1,877 patients. Our method improves macro-F1 over pooled-descriptor MLP and support-conditioned MLP baselines by **7.50** and **9.97** percentage points, respectively, with bootstrap confidence intervals that exclude zero. We further perform ablation experiments on the PTB dataset to isolate architectural contributions when identical channel descriptors are provided. Here, SIDP reaches **0.8603** window macro-F1 and **0.9661** patient macro-F1. Cross-channel attention improves window macro-F1 by **14.13** points over a pooling baseline with the same inputs, and support-generated probes improve macro-F1 by **2.14** points over fixed learned queries. An analysis of the attention weights suggests that both attention stages adapt to each recording. These results suggest that support-induced probing is an effective way to retrieve diagnostic information from multi-channel recordings. The source code for our architecture is available at https://anonymous.4open.science/r/sidp-85DD