Manifold-Informed Neural Networks for Low-Label ECG Electrolyte Inference

Key points

• Physics-informed neural networks (PINNs) supervise on the residual of a known differential operator.1
• Many high-stakes inference problems admit no such operator: clinical signals, behavioral measurements, materials properties.1
• The construction is a structural parallel to PINNs (squared operator residual plus label data, in place of operator residual plus boundary data); the $s{=}0$ limit recovers the homogeneous Laplace equation $LGH=0$ (classical manifold regularization solves the same equation but minimizes the Dirichlet energy $\operatorname{tr}(H^\topLGH)$, a different penalty form).1
• We evaluate MINN on three-class classification of potassium, calcium, and magnesium from 12-lead ECGs in MIMIC-IV-ECG, against twelve baselines spanning supervised and tabular methods, ECG foundation models, and manifold/graph approaches.1

Sources (1)

• [1]Manifold-Informed Neural Networks for Low-Label ECG Electrolyte Inference
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 02:56 PM

  We introduce the Manifold-Informed Neural Network (MINN), a PINN-parallel paradigm that replaces the physics operator residual with a manifold operator residual: a discrete Poisson equation $L_G H = s_φ(x, m)$ on a $k$-nearest-neighbor graph in the learned latent space, with a learned, task-aware source.

  Physics-informed neural networks (PINNs) supervise on the residual of a known differential operator.

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