Adopting Trustworthy AI for Sleep Disorder Prediction: Deep Time Series Analysis with Temporal Attention Mechanism and Counterfactual Explanations

Pegah Ahadian; Qiang Guan; Sherry Wang; Wei Xu

REVIEW 5 major objections 5 minor 15 references

Adopting Trustworthy AI for Sleep Disorder Prediction: Deep Time Series Analysis with Temporal Attention Mechanism and Counterfactual Explanations

T0 review · 5 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper claims a time-series pipeline of LSTM, TCN, and TFT with SHAP and counterfactual explanations predicts sleep disorders at 85.62% testing accuracy while identifying stress, sleep quality, and physical activity as the key…

desk verdict Standard deep-learning-plus-XAI application whose temporal structure is never actually defined; the reported accuracies are internally inconsistent and rest on 22 test patients, so the empirical claims are not yet supported. read the letter →

arxiv 2412.18971 v1 pith:DW3O2OAA submitted 2024-12-25 cs.LG

Pegah Ahadian , Wei Xu , Sherry Wang , Qiang Guan This is my paper · ORCID

classification cs.LG

keywords sleepdisorderpredictiondeeptimeseriesLSTMtemporalconvolutionalnetworkfusiontransformerSHAPcounterfactualexplanationexplainableAI

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to show that pairing three deep time-series models — LSTM, TCN, and Temporal Fusion Transformer — with SHAP-based attribution, temporal attention, and counterfactual examples yields sleep-disorder predictions that are both accurate and transparent enough for clinicians to trust. The reported numbers are a training accuracy around 90–94%, validation accuracy near 89–90%, and a testing accuracy of 85.62% on a 22-patient holdout. The authors argue that the models consistently single out stress level, quality of sleep, and physical activity level as the dominant predictors, and that a counterfactual such as lowering stress from 8 to 4 can flip a disorder prediction to no disorder. If true, this would give healthcare practitioners a concrete, interpretable route from longitudinal lifestyle data to early intervention.

What carries the argument

The load-bearing mechanism is the combination of three sequence learners — LSTM with its gated cell state, TCN with dilated causal convolutions, and TFT with attention — plus the interpretability layer: SHAP values assign each feature a contribution to the output, a temporal attention mechanism computes per-time-step weights whose weighted sum forms the context vector $c_t = \sum_t a_t h_t$, and counterfactual explanations perturb input features while holding others fixed. The paper uses the attention scores and SHAP attributions to identify which time periods and features matter, and uses counterfactuals to turn those attributions into intervention statements.

What would settle it

Inspect the constructed sequences: count how many distinct time steps each of the 400 patients contributes and check whether augmentation preserves chronological order. If most patients appear as one static row, or if the augmented rows are shuffled or repeated snapshots, then the model's "temporal" attention and counterfactual explanations are not actually using time, and the central claim would reduce to a cross-sectional prediction result.

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Extended reading notes

Core claim

The central claim is that the XTSA pipeline, which feeds derived sleep-health features into LSTM, TCN, and TFT and explains the outputs with SHAP, a temporal attention context vector, and counterfactual edits, predicts sleep disorders (insomnia and sleep apnea) at the reported accuracies while exposing the decision process. On the paper's own terms, the discovery is that model predictions are driven primarily by a small set of actionable features — stress level, sleep quality, physical activity, with occupation also appearing in SHAP plots — and that the models' behavior is regular enough that a single feature change (stress 8 to 4) reliably moves a patient from predicted disorder to no disorder.

Load-bearing premise

The load-bearing premise is that the augmented 400-patient training set is a real ordered time series, not just a collection of static or shuffled patient snapshots; if the temporal order is not genuine, the temporal attention and temporal counterfactual explanations have no time dimension to explain.

Editorial extensions

If this is right

If the reported accuracy holds, a model using only lifestyle and physiological measurements could screen for insomnia and sleep apnea before a formal sleep study.
Stress level, sleep quality, and physical activity emerge as the features clinicians should monitor and target.
Counterfactual outputs such as "stress 8 to 4 removes the disorder prediction" provide concrete thresholds for intervention design.
The agreement between LSTM and TCN on feature importance suggests the drivers are not an artifact of one architecture.
TFT's slightly lower validation accuracy but favorable loss suggests attention-based fusion is a viable alternative for clinical time series.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

This inference goes beyond the paper: the temporal claims are testable only if each patient contributes multiple ordered observations; if the augmentation produces shuffled or duplicated static rows, the attention and counterfactual explanations reduce to cross-sectional feature effects rather than time-dependent ones.
This inference connects to neighboring work: the same SHAP-plus-counterfactual wrapper could be applied to other longitudinal health outcomes, with the counterfactual step serving as a cheap way to generate candidate interventions for clinical trials.
A testable extension: reporting per-patient time-step counts and the augmentation scheme would let a reader verify whether the 85.62% testing accuracy reflects temporal generalization or simple feature memorization.
Another inference: the appearance of occupation alongside physiology in SHAP plots suggests the model may be picking up socioeconomic proxies, so a controlled experiment removing demographic features would reveal how much of the predictive signal is genuinely behavioral.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The one-line take: this is a standard deep-learning-plus-XAI application with an undefined temporal axis, and the reported numbers don't yet support the claims. The reader's summary matches my reading, and the stress-test note is on target: the load-bearing issue is not the small test set or the contradictory accuracies, but the absence of any specification of what the time steps are. The dataset section reports cross-sectional statistics (mean sleep duration, stress range), and the only mention of time series construction is a hand-wave about "data augmentation employed to extend the data for the training step." No sequence length, no sampling interval, no statement about whether each patient contributes multiple rows or one row. If each patient is a static snapshot, then LSTM, TCN, and TFT are being applied to non-sequential data, and the "temporal attention" weights and counterfactual "temporal" explanations are not explanations of temporal dynamics at all. The paper gives no way to rule this out.

Credit where it's earned: the paper is honest about its own earlier misreport, the citations to the standard methods (LSTM, TCN, TFT, SHAP, Wachter counterfactuals) are appropriate, and the high-level description of the explainability pipeline is correct. The application is relevant and the motivation is sensible. For a reader who wants a template for wiring SHAP and counterfactuals onto a sleep-screening model, this could be a starting point.

But the evaluation is currently not reliable. The test set is 22 patients with no error bars, no cross-validation, and no baseline comparison. The result paragraph contains contradictory accuracy assignments. And even if the temporal structure were clarified, the paper would need repeated runs and a proper comparison against a non-temporal baseline to justify the use of sequence models. These are addressable, because the dataset is public and the methods are standard, but they are not cosmetic.

Who is this for? A reader interested in a quick application case study of XAI in sleep health might skim it. But I would not cite it in its current form, and I would not bring it to our reading group as an example of sound evaluation. I would send it to a serious referee only if the authors are given a clear revision path requiring them to specify the temporal construction, correct the metrics, add baselines and error bars. As is, the central temporal claim is unfounded. My verdict: engage conditionally, with the temporal definition as the gate.

Recommendation: peer-review it, but only with the explicit expectation of major revision focused on the temporal structure.

Referee Report

5 major / 5 minor

Summary. The manuscript presents an applied pipeline, XTSA, that combines LSTM, TCN, and TFT with SHAP, a temporal attention mechanism, and counterfactual explanations for sleep-disorder prediction. Using a derived feature set from the PhysioNet 'Multilevel Monitoring of Activity and Sleep in Healthy People' dataset, the authors train on 400 patients and test on 22, reporting validation accuracies around 89.33-90.00% and a testing accuracy of 85.62%. They claim stress level, sleep quality, physical activity, and occupation are the dominant predictors, and a counterfactual example asserts that reducing stress from 8 to 4 changes a predicted disorder to no disorder. The paper's central conclusion is that this integrated approach produces accurate, interpretable, and trustworthy predictions for sleep disorders.

Significance. The potential significance is a practical demonstration of temporal XAI for sleep screening, using a public dataset and standard models; such a demonstration could be useful to clinical AI audiences. The contribution is not a new method or a theoretical derivation, and the paper contains no code or machine-checked artifacts. The significance is currently conditional: until the temporal data construction, augmentation, and evaluation are specified and corrected, the central claim is unverifiable. On the positive side, the paper explicitly attempts to combine model-level and explanation-level outputs (SHAP, attention, counterfactuals) in one pipeline, which is a worthwhile goal. The empirical claims, however, require substantial rework before they can be credited.

major comments (5)

[Dataset / Research Methods] The paper's central claim of temporal modeling is not supported because the construction of the time series is never specified. The Dataset section reports only cross-sectional summary statistics (mean sleep duration 7.13 h, stress 5.39, etc.) and says 'data augmentation employed to extend the data for the training step,' but it never states the number of time steps per patient, the interval between steps, or how steps were derived from the PhysioNet source. It also does not state whether augmentation preserves within-patient temporal order or whether the train/test split is at the patient level. The model equations in Research Methods are generic, and no input tensor shape or sequence length is given. If the augmented rows are static snapshots or shuffled fragments, then LSTM/TCN/TFT have no temporal dependencies to exploit, the temporal attention weights in Fig. 3 select among constructed time steps with no real temporal meaning, and the temporal counterfactual explanations reduce to static feature edits. This must be resolved before the empirical claims can be evaluated.
[Experimental Result] The reported accuracy figures are contradictory and prevent assessment. The text first says the LSTM model achieved a training accuracy of 93.72% and a validation accuracy initially reported as 90.00%, then says 'we confirmed that training accuracy is indeed 90.00%,' and later refers to the LSTM performance as 'initially misstated'; it says TFT validation accuracy was 'accurately recorded at 89.33%' after an 'earlier misreport.' Table 1, which is referenced, is not present. Please provide a corrected per-model table with training, validation, and test metrics, and clarify which numbers apply to LSTM, TCN, and TFT.
[Experimental Result] The evaluation is not a valid benchmark for the central claim. Only 22 test patients are used, no cross-validation or repeated runs are reported, no confidence intervals or error bars are given, and the class distribution of the test set is not stated; the 85.62% testing accuracy is therefore not statistically anchored. No comparison to simpler baselines (e.g., logistic regression, gradient boosting, or a non-temporal MLP) is presented, so the necessity of deep temporal models is not established. An ablation that removes the attention mechanism or destroys temporal order is also missing. The authors should report per-model metrics, uncertainty estimates, baselines, and ablation results.
[Temporal Attention and Counterfactual Explanations] The interpretability claims are not supported as robust findings. The statement that 'reducing stress levels from 8 to 4 while maintaining a high quality of sleep score shifts the prediction from disorder to no disorder' describes one model counterfactual, but the paper does not report how often this flip occurs, for which patients, or whether it is stable under retraining or data perturbations. Similarly, SHAP values describe correlations learned by the model and cannot by themselves establish that stress, sleep quality, or physical activity are the 'key determinants' of sleep disorders. Please revise the language to distinguish model explanations from clinical causal evidence, or provide sensitivity analyses that support the stronger claims.
[Abstract / Experimental Result] TFT is barely evaluated despite being a headline component of the proposed approach. The experiments focus on LSTM and TCN; the only TFT result is a validation accuracy of 89.33%, with no training/testing metrics, SHAP analysis, attention visualization, or counterfactual examples. Either include full TFT results or adjust the abstract and conclusion to cover only the models actually evaluated.

minor comments (5)

[Research Methods] The equations for the LSTM gates, TCN output, TFT variable selection, and attention scores are not included in the text after their introductory sentences; without these definitions the methods are not reproducible. Please render all equations and disambiguate the notation, since c_t is used for both the LSTM cell state and the attention context vector.
[Related Work] Reference [8] (Sano et al., 2018) is cited as the source of SHAP, but that paper is not a SHAP reference; please cite the original SHAP paper (Lundberg and Lee, 2017) and retain [8] only for the wearable-sensor sleep study.
[Figures] Figure 1 caption refers to 'Color of points' but does not provide a color legend; Figure 2 refers to 'right and left' panels that are not labeled; Figure 3 is described only qualitatively. Please add legends, panel labels, and quantitative summaries of the attention and counterfactual results.
[Conclusion] The abstract and title emphasize LSTM, TCN, and TFT, but the Conclusion summarizes only LSTM and TCN results; please reconcile the scope and either report TFT results or limit the claims accordingly.
[Dataset] The abstract calls the dataset 'large,' but only 400 training patients and 22 test patients are used; please state the exact dataset size, the augmentation count, the label definition (binary presence/absence versus multi-class insomnia/apnea), and the class distribution.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: standard supervised-learning benchmark; reported accuracies are held-out evaluations and explainability outputs are post-hoc model descriptions.

full rationale

The paper trains LSTM, TCN, and TFT models on a training set and reports validation/testing accuracies; these predictions are evaluated on held-out data rather than being constructed from the target labels. SHAP values, temporal attention weights, and counterfactual examples are all computed from the fitted models and therefore describe those models' learned behavior; this is an interpretive limitation, not a circular derivation. The single self-citation (reference [4]) appears only as a general pointer to XAI as a research area and is not load-bearing for any of the paper's central claims. The open question about how the temporal sequences were constructed from the source data is a data-preprocessing transparency issue, not a circularity issue, because no equation or fitted parameter in the paper reduces the reported predictions or explanations back to the input data by definition. No step in the manuscript claims a result that is equivalent to its own input, so the circularity score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

No new particles, mediators, or theoretical entities are introduced; the only additions are standard XAI artifacts (SHAP values, attention scores, counterfactual scenarios), which are computed from the model rather than postulated.

free parameters (3)

Learned model weights and architecture hyperparameters = not reported
The accuracy claims depend entirely on the trained parameters of LSTM, TCN, and TFT, but no hyperparameters, layer counts, or training details are given.
Temporal sequence construction and augmentation parameters = not reported
The paper says augmentation was used and features were derived, but does not specify time step counts, window sizes, or augmentation strategy, all of which affect the temporal claims.
Train/test split sizes = 400 training, 22 testing
A manually chosen split with no cross-validation; the small test set makes reported accuracy estimates unstable.

assumptions (4)

standard math Standard LSTM, TCN, and TFT model definitions and training procedures as described in the cited literature.
The paper invokes these architectures without proving them and relies on their standard implementation.
domain assumption The derived features and augmented rows form a valid time series with meaningful temporal order.
This is the weakest assumption; the paper never shows how time steps were constructed, so temporal attention and counterfactuals may be meaningless.
domain assumption Imputation and normalization of missing data do not distort the label distribution or temporal structure.
Preprocessing is described in one sentence, so its effects on the 400/22 split are unknown.
domain assumption SHAP values and counterfactual examples faithfully represent the model's decision process and can guide clinical intervention.
The paper treats model-based attributions as actionable insights, though they are correlations from the model, not causal effects.

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Cite this review

Pith. "Pith review of Adopting Trustworthy AI for Sleep Disorder Prediction: Deep Time Series Analysis with Temporal Attention Mechanism and Counterfactual Explanations." pith.science (2026). https://pith.science/paper/DW3O2OAA

@misc{pith2026241218971,
  author       = {Pith},
  title        = {Pith review of: Adopting Trustworthy AI for Sleep Disorder Prediction: Deep Time Series Analysis with Temporal Attention Mechanism and Counterfactual Explanations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DW3O2OAA}},
  note         = {Machine review of arXiv:2412.18971}
}

read the original abstract

Sleep disorders have a major impact on both lifestyle and health. Effective sleep disorder prediction from lifestyle and physiological data can provide essential details for early intervention. This research utilizes three deep time series models and facilitates them with explainability approaches for sleep disorder prediction. Specifically, our approach adopts Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM) for time series data analysis, and Temporal Fusion Transformer model (TFT). Meanwhile, the temporal attention mechanism and counterfactual explanation with SHapley Additive exPlanations (SHAP) approach are employed to ensure dependable, accurate, and interpretable predictions. Finally, using a large dataset of sleep health measures, our evaluation demonstrates the effect of our method in predicting sleep disorders.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

15 extracted references · 13 canonical work pages

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

Explainable ai for time series classification: a review, taxonomy and research directions,

A. Theissler, F . Spinnato, U. Schlegel, and R. Guidotti, “Explainable ai for time series classification: a review, taxonomy and research directions, ” Ieee Access, vol. 10, pp. 100 700– 100 724, 2022

work page 2022

[2] [2]

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K. S. Han, L. Kim, and I. Shim, “Stress and sleep disorder, ” Experi-mental neurobiology, vol. 21, no. 4, p. 141, 2012

work page 2012

[3] [3]

Metrics for explainable ai: Challenges and prospects,

R. R. Hoffman, S. T. Mueller, G. Klein, and J. Litman, “Metrics for explainable ai: Challenges and prospects, ” arXiv preprint arXiv:1812.04608, 2018

arXiv 2018

[4] [4]

Ai trustworthy challenges in drug discovery,

P . Ahadian and Q. Guan, “Ai trustworthy challenges in drug discovery, ”in International Workshop on Trustworthy Artificial Intelligence for Healthcare. Springer, 2024, pp. 1–12

work page 2024

[5] [5]

Multimodal ambulatory sleep detection using lstm recurrent neural networks,

A. Sano, W. Chen, D. Lopez -Martinez, S. Taylor, and R. W. Picard, “Multimodal ambulatory sleep detection using lstm recurrent neural networks, ” IEEE journal of biomedical and health informatics, vol. 23, no. 4, pp. 1607–1617, 2018

work page 2018

[6] [6]

An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,

S. Bai, J. Z. Kolter, and V. Koltun, “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling, ” arXiv preprint arXiv:1803.01271, 2018

arXiv 2018

[7] [7]

Temporal fusion transformers for interpretable multi-horizon time series forecasting,

B. Lim, S. ¨O. Arık, N. Loeff, and T. Pfister, “Temporal fusion transformers for interpretable multi-horizon time series forecasting, ” International Journal of Forecasting, vol. 37, no. 4, pp. 1748–1764, 2021

work page 2021

[8] [8]

A. Sano, S. Taylor, A. W. McHill, A. J. Phillips, L. K. Barger, E. Klerman, and R. Picard, “Identifying objective physiological markers and modifiable behaviors for self-reported stress and mental health status using wearable sensors and mobile phones: observational study, ” Journal of medical Internet research, vol. 20, no. 6, p. e210, 2018

work page 2018