{"id":"c9ecd536-4d9a-4c31-a172-1305b877b65c","arxiv_id":"2412.18971","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":3.0,"correctness_risk":"high","formal_verification":"none","parameter_count":3,"one_line_summary":"LSTM, TCN, and TFT predict sleep disorders from a 400-patient dataset with reported accuracies between 85 and 93 percent, and SHAP, temporal attention, and counterfactuals are offered as interpretability aids.","lead":"This paper trains three standard deep time series models, LSTM, TCN, and TFT, on a small sleep health dataset and adds SHAP, temporal attention, and counterfactual explanations to their predictions. The authors report accuracies from about 85 to 93 percent and argue that the explanations make the models trustworthy enough for clinical decision support.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"No independent estimate of sequence construction or temporal order exists; the paper's temporal claims rest on an unspecified time-step pipeline. Until the preprocessing/augmentation is specified and validated, temporal attention and counterfactual explanations are unfounded.","rationale":"The paper's stated contribution is a 'Trustworthy AI approach with Explainable Time Series Analysis (XTSA)' in which temporal attention and counterfactual explanations are central. For that contribution to be real, the input must be a sequence indexed by time per patient. The manuscript never provides that indexing. The reader's CONDITIONAL verdict already rests on this exact gap, and my review of the full text finds no passage that fills it: the Dataset section describes aggregate statistics and normalization, not a tensor shape; the Research Methods equations are generic; the experiments report only aggregate accuracy figures (and in one passage explicitly correct an 'earlier misreport'). Thus the most load-bearing concern is not that the models are non-standard—they are standard—but that the temporal structure they are supposed to exploit is unstated and possibly absent. The public data source makes the issue directly checkable, so the right disposition is to keep the CONDITIONAL verdict and attach the concrete reproducibility/validation test. I agree with the reader's weakest_assumption; accordingly no verdict change is needed.","tokens_in":6246,"tokens_out":4295,"duration_ms":41923,"concrete_test":"Download the public PhysioNet dataset [14] and reconstruct the authors' preprocessing. (1) Count rows per subject and timestamps; if any subject has fewer than 2 time points, or if all rows are independent patient-days, the temporal premise fails immediately. (2) Re-run the three models on the original augmented sequences and on a control version where the time axis of each sequence is randomly shuffled, keeping features and labels identical. If validation/testing accuracy does not degrade under shuffling, temporal order is not being used. (3) Compare against a non-temporal baseline (e.g., logistic regression or XGBoost on the same features); if a static model matches or beats the LSTM/TCN/TFT, the claimed temporal advantage and attention-based insights are not supported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim—that LSTM, TCN, and TFT with temporal attention yield accurate and interpretable sleep-disorder predictions—presupposes that the 400-patient training data, after feature derivation and 'data augmentation employed to extend the data for the training step,' is a genuine multivariate time series. The paper never states the number of time steps per patient, the sampling interval, how the steps were extracted from the 'Multilevel Monitoring of Activity and Sleep in Healthy People' source, or whether augmentation preserves within-patient temporal order. The Dataset section reports only cross-sectional summary statistics (mean sleep duration 7.13 h, range; stress 5.39; etc.), and the model equations in Research Methods are generic, with no input tensor shape or sequence length. If the rows are static patient snapshots, or augmentation creates shuffled or repeated fragments, then LSTM/TCN/TFT have no temporal dependencies to exploit; the temporal attention weights in Fig. 3 select among fabricated time steps, and counterfactual explanations like 'lower stress from 8 to 4' reduce to static feature edits with no time dimension. This is not a dispute about model choice but about whether the stated object of explanation exists at all. The result paragraph's contradictory accuracy assignments (e.g., the passage that first gives LSTM training accuracy as 93.72% and later 'confirms' 90.00%) compound the uncertainty, but the unresolved temporal construction is the load-bearing issue.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","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.","tokens_in":6497,"tokens_out":9531,"duration_ms":93011,"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":[{"comment":"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.","section":"Dataset / Research Methods"},{"comment":"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.","section":"Experimental Result"},{"comment":"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.","section":"Experimental Result"},{"comment":"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.","section":"Temporal Attention and Counterfactual Explanations"},{"comment":"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.","section":"Abstract / Experimental Result"}],"minor_comments":[{"comment":"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.","section":"Research Methods"},{"comment":"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.","section":"Related Work"},{"comment":"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.","section":"Figures"},{"comment":"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.","section":"Conclusion"},{"comment":"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.","section":"Dataset"}],"recommendation":"major_revision","confidential_remarks":"The reader's stress-test concern about the unspecified temporal sequence construction is valid and is the most serious issue: it affects the interpretation of every model and explanation in the paper. If the authors cannot supply a concrete description of the time-step construction and augmentation and confirm that temporal order is meaningful, the paper should not be published. In its current form the manuscript is closer to a preliminary report than a complete evaluation; the contradictory accuracy statements and absent TFT results reinforce that view. I do not see a fit/prediction circularity problem, as this is a standard supervised-learning benchmark."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","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.\n\nCredit 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.\n\nBut 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.\n\nWho 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.\n\nRecommendation: peer-review it, but only with the explicit expectation of major revision focused on the temporal structure.","headline":"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.","tokens_in":7047,"tokens_out":2285,"would_cite":false,"duration_ms":25944,"reading_group":"maybe","serious_thinker":"unclear","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"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…","keywords":["sleep disorder prediction","deep time series","LSTM","temporal convolutional network","temporal fusion transformer","SHAP","counterfactual explanation","explainable AI"],"falsifier":"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.","tokens_in":6022,"feed_emoji":"😴","tokens_out":8407,"duration_ms":74114,"temperature":0.7,"pith_summary":"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.","feed_headline":"Sleep-disorder AI hits 85.6% and shows why","feed_subtitle":"The interpretable pipeline points to stress, sleep quality, and activity as the modifiable drivers behind its predictions.","key_machinery":"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.","core_discovery":"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.","pith_inferences":["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."],"forward_implications":["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."],"supporting_citations":[{"why":"Supplies the \"Multilevel Monitoring of Activity and Sleep in Healthy People\" dataset from which all demographic, lifestyle, and physiological features are derived.","marker":"[14]"},{"why":"Cited as the prior LSTM-based sleep-detection result that motivates using recurrent networks on physiological and lifestyle data.","marker":"[5]"},{"why":"Provides the empirical basis for temporal convolutional networks as an efficient sequence-modeling alternative with long-range dependencies.","marker":"[6]"},{"why":"Defines the Temporal Fusion Transformer architecture used here for interpretable multi-horizon time-series modeling.","marker":"[7]"},{"why":"Cited as the source of the SHAP-based feature attribution approach that turns each prediction into per-feature contributions.","marker":"[8]"},{"why":"Supplies the counterfactual-explanation method used to show how changing input features such as stress alters the predicted outcome.","marker":"[15]"},{"why":"Provides the LSTM cell and gate formulation that the temporal attention mechanism is attached to.","marker":"[12]"},{"why":"Supplies the multi-stage temporal convolutional network architecture underlying the TCN implementation.","marker":"[13]"}],"fun_headline_variants":["Sleep-disorder AI: 85.6% accuracy with clear reasons","AI predicts insomnia and apnea, pinpoints stress and sleep quality","Counterfactual AI explains sleep-disorder predictions","LSTM, TCN, TFT: interpretable sleep-disorder forecasting","Stress and sleep quality drive AI's sleep-disorder calls"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"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.","fun_headline_variants_meta":{"raw":{"variants":["Sleep-disorder AI: 85.6% accuracy with clear reasons","AI predicts insomnia and apnea, pinpoints stress and sleep quality","Counterfactual AI explains sleep-disorder predictions","LSTM, TCN, TFT: interpretable sleep-disorder forecasting","Stress and sleep quality drive AI's sleep-disorder calls"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000714,"raw_usage":{"total_tokens":3144,"prompt_tokens":815,"completion_tokens":2329,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":431,"completion_tokens_details":{"reasoning_tokens":2242}},"tokens_in":431,"tokens_out":2329,"duration_ms":15238,"temperature":1.0,"reasoning_tokens":2242,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T00:57:35.896417+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"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.","supporting_citations":[{"cited_title":"Multilevel monitoring of activity and sleep in healthy people,","cited_arxiv_id":null,"evidence_quote":"Supplies the \"Multilevel Monitoring of Activity and Sleep in Healthy People\" dataset from which all demographic, lifestyle, and physiological features are derived."},{"cited_title":"Multimodal ambulatory sleep detection using lstm recurrent neural networks,","cited_arxiv_id":null,"evidence_quote":"Cited as the prior LSTM-based sleep-detection result that motivates using recurrent networks on physiological and lifestyle data."},{"cited_title":"Temporal fusion transformers for interpretable multi-horizon time series forecasting,","cited_arxiv_id":null,"evidence_quote":"Defines the Temporal Fusion Transformer architecture used here for interpretable multi-horizon time-series modeling."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Cited as the source of the SHAP-based feature attribution approach that turns each prediction into per-feature contributions."},{"cited_title":"Counterfactual explanations without opening the black box: Automated decisions and the gdpr,","cited_arxiv_id":null,"evidence_quote":"Supplies the counterfactual-explanation method used to show how changing input features such as stress alters the predicted outcome."},{"cited_title":"A review of recurrent neural networks: Lstm cells and network architectures,","cited_arxiv_id":null,"evidence_quote":"Provides the LSTM cell and gate formulation that the temporal attention mechanism is attached to."},{"cited_title":"Ms-tcn: Multi -stage temporal convolutional network for action segmentation,","cited_arxiv_id":null,"evidence_quote":"Supplies the multi-stage temporal convolutional network architecture underlying the TCN implementation."}],"review_version":1}