Disorder-Aware ML Models

Updated 3 January 2026

Disorder-aware machine learning models are systems that explicitly incorporate anomalies and heterogeneity to improve sensitivity and precision.
They employ techniques such as signature representation, discrete feature engineering, and attention pooling to capture disorder across diverse domains.
Applications range from psychiatric diagnostics to quantum device characterization, yielding enhanced classification accuracy and practical insights.

Disorder-aware machine learning models are specifically designed to incorporate, exploit, or infer disorder—conceptualized as deviations, heterogeneity, or anomalies in measured signals, features, or latent representations—directly into their modeling and inference pipelines. This class of models spans biomedical, physical, and engineering domains where disorder is not an incidental nuisance but rather a source of diagnostic, prognostic, or structural information. Across psychiatric, genetic, neurological, hematological, sensor-based, and quantum device contexts, recent studies demonstrate that disorder-aware design yields substantial gains in classification accuracy, interpretability, sensitivity, and generalizability.

1. Formalization and Data Representation of Disorder

Disorder-aware modeling requires either explicit encoding of disorder-relevant features or implicit extraction of disorder structure from raw or processed data. Key methodologies include:

Pathwise Signature Representation in Psychiatry: For mood disorders, patient self-reports are embedded as time-parametrized paths $X : [0,T] \to \mathbb{R}^7$ (six mood Likert items plus time), normalized on fixed-length overlapping windows (e.g., 20 reports). The truncated signature $S^{\le N}(X)$ captures iterated integrals over these paths, encoding both net changes and cross-ordering of features such as anxiety leading irritability (Arribas et al., 2017).
Discrete Feature Engineering in Genomics: Early genetic disorder diagnosis leverages engineered count/binary features (maternal age ≥ 40, any inherited gene, high WBC count, heart/respiratory issue OR flags), filtered and ranked via chi-squared dependence on outcome class to isolate mechanistically disorder-relevant signals. Disorder subclasses (Leigh, mitochondrial myopathy, Tay-Sachs, etc.) are treated as separate targets (Siddik et al., 2024).
Instance-level Disorder in MIL: In hematological applications, cell images are organized as bags $B = \{I_1,\dots,I_N\}$ with bag-level labels. Gaussian mixture models fitted to negative-instance embeddings $z_n$ provide a healthy-cell baseline, with Mahalanobis anomaly scores $d_n$ quantifying deviation from normality. Rare, out-of-distribution morphologies are flagged regardless of training coverage (Kazeminia et al., 2022).
Latent Representation in Dynamical Systems: In device and change-point studies, disorder maps into latent parameters: e.g., phase offsets $\varphi_i$ in coupled oscillator networks or random field components in nanowire quantum systems. Physical models provide forward simulators, and neural nets are trained as surrogates or inverse solvers on disorder-parametrized data (Huang et al., 14 Apr 2025, Taylor et al., 2023, Craig et al., 2021).
Feature Selection and Age-Stratified Data: Disorders whose manifestations evolve (ASD, movement) utilize stratified datasets (Toddler, Child, Adult) and unsupervised filter methods (Relief F, L1 sparsity, manifold projection) to highlight discriminative traits for each subgroup (Hossain et al., 2020, Tang et al., 2023).

2. Model Architecture and Training Protocols

Disorder-aware models cover a spectrum from kernel methods and ensembles to deep neural networks with domain-specific enhancements:

Multinomial Logistic Regression on Signature Features: For mood disorder triage, the softmax classifier $\hat y = \mathrm{softmax}(W \phi_N(R))$ optimizes regularized cross-entropy, directly leveraging high-order signature terms for cohort distinction (Arribas et al., 2017).
Gradient Boosting and Kernel SVM: Early genetic disorder classification achieves maximal accuracy (CatBoost 0.77, SVM 0.80) by tuning depth, leaf regularization, and kernel bandwidth. Class weights and oversampling correct for subclass imbalance (Siddik et al., 2024).
Anomaly-Aware Pooling in MIL: Hybrid pooling layers compute $\ell_n = W_D d_n + W_A a_n + b$ logits from anomaly and attention scores, normalized by softmax. Warm-starting with single-instance classifier loss improves early optimization of small sets (Kazeminia et al., 2022).
Surrogate and Inverse Physical Networks: Device disorder inversion is performed by CNNs trained to map transport signatures to disorder parameters. Uniqueness is validated by reconstructing physical observables and topological invariants using recovered parameters (Taylor et al., 2023, Craig et al., 2021).
Diamond-Like Feature Learning: Movement disorder monitoring uses stacked sparse autoencoders (embedding raw features at every stage), followed by L1-reduced and manifold-projected ensemble SVM classifiers, balancing overfitting risk, non-Gaussianity, and interpretability (Tang et al., 2023).
Attention and LSTM Pooling for Dynamic Disorder: fMRI dynamics are captured through overlapping window CNN encoders augmented by bi-LSTM and attention pooling, inferring both subject-level and spatio-temporal disorder patterns. The MILC self-supervised objective maximizes InfoNCE contrast between local windows and global context (Mahmood et al., 2020).

3. Strategies for Achieving Disorder Awareness

Disorder awareness is instantiated via several mechanisms:

Label Integration: Direct use of clinical/categorical labels enables models to align weights toward disorder-specific motifs (borderline, bipolar, healthy, etc.) without overfitting non-discriminative features (Arribas et al., 2017, Siddik et al., 2024).
Engineered Feature Selection: Clinical, genetic, or symptomatic insights inform feature construction, with selection grounded in statistical dependence or expert knowledge. Feature importance analyses map input variables onto disorder classes/subclasses (Siddik et al., 2024, Hossain et al., 2020).
Temporal and Structural Sensitivity: Pathwise modeling, mutual-information objectives, and attention-pooling architectures allow models to focus on transient, disorder-relevant patterns beyond global averages—e.g., mood swings, network state transitions, cell morphology anomalies (Arribas et al., 2017, Mahmood et al., 2020, Kazeminia et al., 2022).
Threshold Calibration and Operating Point Optimization: In multi-label clinical settings, decision thresholds are explicitly chosen per disorder (e.g., minimum recall ≥ 80%) to ensure sensitivity for critical categories, often at the expense of general accuracy (Samanta et al., 27 Dec 2025).
Physics-Informed Constraints and Bayesian Inference: For nanoscale and quantum disorder, machine learning constitutes a differentiable surrogate to a physical forward model (e.g., Poisson, transport simulation), facilitating Hamiltonian Monte Carlo or variational Bayesian inference directly in disorder parameter space (Craig et al., 2021).

4. Evaluation Metrics, Interpretability, and Validation

Disorder-aware model validation relies on metrics and visualizations suited to both signal and mechanistic interpretation:

Confusion Matrix, ROC-AUC, F1: Classification accuracy, recall, and one-vs-rest ROC-AUC are standard, but for disorder-specific applications, per-group confusion matrices delineate model strengths across classes/subclasses (Arribas et al., 2017, Siddik et al., 2024, Samanta et al., 27 Dec 2025).
Feature Importance and Heatmaps: Models employing random forests or gradient boosting report feature importance by Gini or gain, often confirming known disorder biomarkers. Deep architectures employ Grad-CAM, LIME, or saliency backpropagation to attribute decisions to time-frequency bands, spatial networks, or morphological subregions (Zhuang et al., 2022, Mahmood et al., 2020).
Physical/Physiological Plausibility: For device disorder, reconstructed disorder landscapes are tested by matching experimentally obtained observable profiles (conductance, barriers, quantum dot counts) to theoretical predictions from inferred parameters (Taylor et al., 2023, Craig et al., 2021).
Generalization to Unseen Disorders/Subtypes: Out-of-distribution anomaly detection via Mahalanobis scoring and leave-one-disorder-out experiments enable models to flag rare or untrained conditions (Kazeminia et al., 2022).

5. Practical Applications and Clinical/Physical Relevance

Disorder-aware ML systems are being actively translated into real-world triage and diagnostic tools:

Psychiatric Mood Spectroscopy and Emergency Rescue: Signature and behavioral-feature models enable objective reclassification, cohort-sensitive mood swing forecasting, and point-of-care emergency triage for psychiatric disorders (Arribas et al., 2017, Ahammed et al., 20 Aug 2025).
Genetic Disease Early Diagnosis: Thresholded, multidimensional classifiers using perinatal metrics facilitate rapid pediatric screening with class-specific sensitivity optimization (Siddik et al., 2024).
Multi-Disorder EEG and Sleep Analysis: Sensitivity-calibrated tree ensembles and MLPs reliably screen for an array of neurocognitive and neurodevelopmental syndromes, with feature sets recapitulating standard clinical markers (Samanta et al., 27 Dec 2025, Zhuang et al., 2022).
Quantum Device Characterization: Machine learning is used to infer, optimize, and tune disorder in electronic devices, bridging the gap between experimental observables and microscopic physical configuration, thus aiding in the design and prototyping of quantum technologies (Taylor et al., 2023, Craig et al., 2021).
Movement Disorder Monitoring: Ensemble-based diamond-like architectures supply robust, interpretable chronic disease recognition for wearable sensor networks under high-noise, small-sample conditions (Tang et al., 2023).

6. Limitations, Challenges, and Future Directions

Despite their utility, disorder-aware models face defined limitations:

Label Dependence and Subtype Ambiguity: Disorder awareness is contingent on accurate ground-truth labeling. Subthreshold or comorbid cases may defy neat separation, and stratified models may not generalize to mixed presentations (Arribas et al., 2017).
Feature Space Explosion and Overfitting: High-order signature or deep architectures risk dimensionality inflation, especially when training cohorts are limited. Feature reduction, sparsity, and regularization become essential for clinical viability (Arribas et al., 2017, Tang et al., 2023, Mahmood et al., 2020).
Limited Interpretability: Although some works employ heatmaps and feature-ranking, many hybrid or deep architectures lack rigorous attribution mechanisms, hampering clinical trust and mechanistic understanding (Eslami et al., 2020).
Data Volume and Class Imbalance: Rare disorders pose sample-size and imbalance problems, partially mitigated through oversampling, weighting, or anomaly-scoring, but often at the cost of reduced specificity (Siddik et al., 2024, Samanta et al., 27 Dec 2025).
Physics Surrogate Validity: Device and quantum disorder models are restricted to the operating regime of their physical simulators; retraining or extension to non-Gaussian scenarios may require significant model redesign (Craig et al., 2021, Taylor et al., 2023).

Recommended directions include expansion of stratified datasets, prospective clinical and physical validation, incorporation of raw and multimodal sensor inputs, enhancement of interpretability methods, advanced imbalance/bias corrections, and adaptation of disorder-aware strategies to augment emergent large language and fusion models in medical/physical triage scenarios.

For selected technical exemplars and further reading, refer to the cited arXiv manuscripts (Arribas et al., 2017, Siddik et al., 2024, Kazeminia et al., 2022, Romanenkova et al., 2021, Huang et al., 14 Apr 2025, Eslami et al., 2020, Samanta et al., 27 Dec 2025, Taylor et al., 2023, Zhuang et al., 2022, Craig et al., 2021, Mahmood et al., 2020, Hossain et al., 2020, Tang et al., 2023).