A Deep Learning Algorithm Based on CNN-LSTM Framework for Predicting Cancer Drug Sales Volume

Junghua Lin; Nanxi Wang; Yilin Yao; Yinghan Li

REVIEW 4 major objections 6 minor 3 cited by

A Deep Learning Algorithm Based on CNN-LSTM Framework for Predicting Cancer Drug Sales Volume

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A CNN-LSTM hybrid forecasts quarterly cancer drug sales with lower error than CNN, LSTM, or RNN on the same Egyptian dataset.

desk verdict A routine CNN-LSTM application to 40 quarters of one drug's sales; the reported accuracy is unverifiable because the train/test split is never defined. read the letter →

arxiv 2506.21927 v1 pith:ZHDQL3LT submitted 2025-06-27 cs.CE

Yinghan Li , Yilin Yao , Junghua Lin , Nanxi Wang This is my paper · ORCID

classification cs.CE

keywords CNN-LSTMtimeseriesforecastingcancerdrugsalesvolumepredictionpharmaceuticalsupplychaindeeplearningMSERMSE

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

This paper tries to establish that a hybrid deep learning architecture, CNN-LSTM, forecasts quarterly sales of a specific cancer drug in Egypt more accurately than CNN, LSTM, or RNN models applied to the same data. The authors assemble 40 quarterly records from 2015 to 2024, with features such as price, drug type, company, effectiveness, and sales volume. On their test results, CNN-LSTM reaches an MSE of 1.150 and RMSE of 1.072, compared with an MSE of 3.526 for CNN and 1.956 for LSTM. The practical motivation is that better sales forecasts help pharmaceutical companies plan production, manage inventory, and support healthcare policy decisions.

What carries the argument

The central object is the CNN-LSTM hybrid network: two one-dimensional convolutional layers (kernel sizes 3 and 5, with 64 and 128 filters, batch normalization, ReLU, and max-pooling) extract local temporal features from the multi-channel sales series, and two stacked LSTM layers (128 hidden units each, dropout 0.3, stateful with reset) capture long-term dependencies. A fully connected dense layer produces the next quarter's sales volume regression output. The machinery's role is to fuse short-window patterns with long-range trends so the model can track both seasonal fluctuations and gradual growth in a volatile series.

What would settle it

Re-run the comparison with a stated split of the 40 quarters (for example, the first 32 as training and the last 8 as held-out test) across multiple random seeds; if CNN-LSTM does not beat LSTM on the held-out test MSE, the central forecasting claim fails.

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Extended reading notes

Core claim

The central claim is that combining a convolutional front end with a long short-term memory back end captures both local temporal patterns and long-term dependencies in cancer drug sales data, and that this combination outperforms each component alone. In the reported comparison, CNN-LSTM achieves MSE 1.150 and RMSE 1.072 on the test set, beating CNN (3.526, 1.878), LSTM (1.956, 1.399), and RNN (2.026, 1.423). The author's interpretation is that CNN extracts local features such as price fluctuations and sales peaks while LSTM models seasonal and inter-quarter trends, and that the fused representation yields the best forecast. The paper claims this supports data-driven decision-making for pharmaceutical production, inventory, and healthcare policy.

Load-bearing premise

The load-bearing premise is that the quoted MSE and RMSE are out-of-sample test errors, yet the paper never specifies the train/test split, random seed, or cross-validation for the 40 quarterly observations.

Editorial extensions

If this is right

On the same 40-quarter Egypt dataset, CNN-LSTM lowers MSE by about 2.38 relative to CNN and by about 0.81 relative to LSTM.
The trained pipeline can produce next-quarter sales volume forecasts for this drug, which can feed directly into production planning and inventory management.
The reported alignment of predicted and actual sales curves implies the model captures seasonal fluctuations and long-term growth trends in this series.
The multidimensional feature set (price, effectiveness, company, drug form, user evaluation) can be reused as inputs for the same architecture on similar pharmaceutical sales data.

Reading between the lines

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

Because the paper does not report the train/test split, the quoted test errors should be treated as conditional on an unspecified split, and a proper validation design is required before the model ranking is accepted.
The single-drug, single-region, 40-quarter scope leaves the architecture's generality untested; applying the same pipeline to multi-drug or multi-region data would be a direct way to check it.
The paper's own limitation list suggests a concrete extension: adding external shock variables such as policy changes, competing products, and public health events could reduce the deviations seen during sudden market shifts.
An ablation test that replaces the CNN block with dense layers of comparable size before the LSTM would isolate how much of the reported gain actually comes from convolutional feature extraction.

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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

Colleague,

The paper is a routine CNN-LSTM application to 40 quarterly sales records of one cancer drug in Egypt. The reported test-set MSE of 1.150 and RMSE of 1.072 are the entire basis for the forecasting claim, but the paper never says how the 40 quarters were split into train and test. Those numbers could just as easily be in-sample fits. That alone sinks the central claim.

To its credit, the paper does a few things right. The literature review touches relevant work on pharmaceutical sales forecasting with LSTM and XGBoost, and the model description is reasonably concrete: two 1D CNN layers with kernel sizes 3 and 5 and 64/128 filters, followed by two LSTM layers with 128 units and dropout 0.3. It also compares against CNN, LSTM, and RNN baselines, and it admits limitations—no external variables, single region, need for multi-region validation. So the authors are not oblivious to the bigger picture.

But the soft spots are load-bearing. First, the missing train/test split. With 40 quarterly observations and a model that likely has tens of thousands of parameters, overfitting is almost guaranteed. Second, there are no error bars or repeated runs; the baselines show LSTM at MSE 1.956, which is far better than CNN but still worse than the hybrid—yet without variance or significance testing, that ordering could be noise. Third, the data are proprietary and not made available, so nothing is independently checkable. Fourth, the paper does not say which variables are used as inputs versus the target. It lists drug type, company, price, effectiveness, and user reviews; we don't know how these are encoded or whether the model saw them at all. Finally, the dataset itself is questionable—web-scraped from 'several reputable Egyptian pharmaceutical websites' with variables like 'Effectiveness' and 'UserEvaluate' that have no clear measurement scale.

In short, the paper is a straightforward application of a standard architecture to a tiny dataset, and the evaluation is too under-specified to support the headline result. It could be a useful teaching example of how not to report time-series experiments, but it is not a contribution to forecasting methodology or to cancer drug sales analysis.

Recommendation: desk reject. If the authors later provide code, data, and a clear train/test protocol, a re-submission could be worth a second look, but not as it stands.

Regards,

Referee Report

4 major / 6 minor

Summary. The paper proposes a CNN-LSTM hybrid model for forecasting quarterly sales volume of a single cancer drug in Egypt over 2015-2024 (40 quarters). The authors report an MSE of 1.150 and RMSE of 1.072, which they attribute to a test set, and compare these numbers with those of CNN, LSTM, and RNN baselines. The central claim is that the hybrid architecture outperforms the single models and provides a reliable data-driven tool for pharmaceutical forecasting.

Significance. If the reported metrics were genuinely out-of-sample and reproducible, the study would provide a useful empirical data point on hybrid deep learning for pharmaceutical demand forecasting on small, real-world data. The paper's stated contributions---a multidimensional quarterly dataset, a CNN-LSTM architecture, and a benchmark against single models---are plausible but currently unverifiable. The manuscript provides no dataset, no code, no error bars, and no explicit train/test protocol. The significance is therefore prospective rather than demonstrated: the architecture choice is reasonable, but the evidence does not currently support the forecasting claim.

major comments (4)

[§5, Table 1 (Comparison of experimental results)] The central claim that the CNN-LSTM model 'performs well on the test set' is unsupported because the paper never defines the train/test split. Section 3 describes the data as 40 quarterly observations and mentions preprocessing, but no partition ratio, chronological cutoff, random seed, or cross-validation procedure is reported. With only 40 data points, the quoted MSE of 1.150 and RMSE of 1.072 are equally consistent with in-sample fitting, a single favorable run, or a leaky preprocessing pipeline. This is load-bearing because the Abstract, Section 5, and the Conclusions all rest on these two numbers. The authors must specify how the test set was constructed and demonstrate that the reported errors are out-of-sample.
[§4 (CNN-LSTM Model Introduction)] The model capacity is very large relative to the sample size: two 1D-CNN layers with 64 and 128 filters, two LSTM layers with 128 hidden units, and stateful training. With roughly 40 quarterly observations, the effective training sample after any plausible split is maybe 25-30 points. The paper reports only a dropout rate of 0.3 and gives no information about epochs, early stopping, optimizer, learning rate, batch size, or how many runs were averaged. Without this information and without a proper train/test separation, the statement in the Conclusions that the model shows 'strong generalization and stability' is not supported.
[Table 1 (Comparison of experimental results)] The comparison with CNN, LSTM, and RNN baselines is uninterpretable. No architectural details, training configurations, or evaluation protocols are given for the baselines beyond generic descriptions. The table lists a single MSE and RMSE per model with no error bars, no repeated runs, and no significance test. On a 40-point series, run-to-run variability can easily change these numbers by a large margin; the text's claim of 'significant improvement' is therefore not justified by the evidence presented.
[§3 (Data Introduction)] Reproducibility is not possible from the manuscript: the dataset is not made available, the scraping sources are not listed in enough detail to re-acquire the data, and no code is provided. In addition, the preprocessing description says 'unit normalization and time alignment were performed on numeric fields like price and sales volume' before any mention of splitting; if normalizing statistics are computed on the full series, test information leaks into the training process. The authors must state that normalization parameters are fitted only on the training partition, and if that is already the case, they should say so explicitly.

minor comments (6)

[Throughout] The paper contains two different tables both labelled 'Table 1' (the variable description table and the model comparison table); the second should be renumbered and the in-text references corrected.
[Figure 1] The percentages in the regional distribution do not sum to 100% (four regions at 20% each plus Europe at 19.9% totals 99.9%); please correct the numbers or the description.
[§5 (heading numbering)] There are two section 5 headings: 'Model result analysis' and 'Conclusions'; the Conclusions should be renumbered as a separate section.
[Figure 3] The forecast curve would be far more informative with labeled axes, units, a legend distinguishing actual and predicted values, and ideally a shaded confidence interval or at least the residual series; the present figure supports only a qualitative reading.
[Table 1 variables] The variable 'Date' is described as 'the timestamp when the data was scraped', yet the data are quarterly sales records spanning 2015-2024; please clarify whether the timestamp refers to the calendar quarter of sales or to the time of collection, as these are materially different for time-series modeling.
[References] Reference [15] contains a corrupted author entry ('** Y'), and several references lack complete page ranges or DOIs; please correct these in a final copyediting pass.

Circularity Check

1 steps flagged · score 6.0 of 10

The central forecast claim is partially circular: the paper reports 'test set' MSE/RMSE without ever defining a train/test split, and describes the forecast as covering all 40 quarters, so the reported 'prediction' is indistinguishable from in-sample fit.

fitted input called prediction [Section 5 (Model result analysis and Conclusions), Table 1; Section 3 (Data Introduction)]
"The forecast curve in Figure 3 illustrates the predicted quarterly sales volume of a cancer drug in Egypt from 2015 to 2024 using the CNN -LSTM model. ... Evaluation on the test set yielded a Mean Squared Error (MSE) of 1.150 and a Root Mean Squared Error (RMSE) of 1.072."

Section 3 describes data preprocessing but never specifies any train/test split, split ratio, random seed, or cross-validation; Section 5 then reports a 'test set' MSE of 1.150 and RMSE of 1.072 without defining the test set. The forecast curve is explicitly described as covering the full 2015-2024 span, i.e., all 40 quarters of the dataset, and Table 1's errors are given with no statement of which quarters were held out. With no held-out set defined, a model of this capacity (2 CNN layers with 64/128 filters, 2 LSTM layers of 128 units) can trivially fit 30-40 samples, so the reported metrics are indistinguishable from in-sample fit. The 'performs well on the test set' claim therefore reduces, by the paper's own construction, to 'the model fits the data it was trained on.'

full rationale

The paper is a standard CNN-LSTM application with external references; no load-bearing self-citation is present (reference [11] is background and not identifiable as an author citation, and in any case is not load-bearing), and no uniqueness theorem or ansatz is imported from the authors' prior work. The architecture and metrics are standard. The circularity concern is confined to the evaluation protocol: the central claim, that CNN-LSTM 'performs well on the test set' with MSE 1.150 and RMSE 1.072, rests entirely on a test set that the manuscript never defines. Section 3 gives only the total span (40 quarters from 2015 to 2024) and the preprocessing steps; Section 5 presents the forecast curve as covering the entire span and reports errors without stating any split. Under the paper's own construction, 'prediction on the test set' is therefore indistinguishable from the fitted output on the training data, so the headline result may be a fit renamed as a prediction. This matches the 'fitted input called prediction' pattern and warrants a score of 6: the central forecasting claim partially reduces to a fit by construction, even though a methodological fix (reporting a proper held-out split) would resolve it. The other circularity patterns (self-definitional, self-citation load-bearing, uniqueness imported from authors, ansatz smuggled in via citation, renaming a known result) are not present.

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

The central claim rests on a handful of manual design choices and an undescribed train/test split, while the data and code are not released.

free parameters (4)

CNN kernel sizes and filter counts = kernels 3 and 5; 64 and 128 filters
Manual architectural choices with no ablation; performance comparison depends on them.
LSTM hidden units and dropout = 128 units; dropout 0.3
Manual choices; no sensitivity analysis is shown.
Train/test split = not specified
Metrics depend entirely on this partition; absence makes the reported errors uninterpretable.
Training hyperparameters = not reported
Optimizer, learning rate, epochs, and batch size are absent, so the fit is not reproducible.

assumptions (4)

domain assumption The 40 quarterly records of one drug are sufficient to train a deep network with hundreds of parameters.
No statistical justification is given; high risk of overfitting on a tiny sample.
domain assumption Scraped web data accurately reflect true drug sales volumes.
No source URLs, scraping dates, or validation against official statistics are provided.
domain assumption No comparison to statistical baselines is needed to establish forecasting value.
The paper claims improvement over neural variants only; simple methods like seasonal naive or ARIMA are absent.
domain assumption The test set exists and is held out.
The paper says 'test set' but never defines it; this is a load-bearing premise.

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Cite this review

Pith. "Pith review of A Deep Learning Algorithm Based on CNN-LSTM Framework for Predicting Cancer Drug Sales Volume." pith.science (2026). https://pith.science/paper/ZHDQL3LT

@misc{pith2026250621927,
  author       = {Pith},
  title        = {Pith review of: A Deep Learning Algorithm Based on CNN-LSTM Framework for Predicting Cancer Drug Sales Volume},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZHDQL3LT}},
  note         = {Machine review of arXiv:2506.21927}
}

read the original abstract

This study explores the application potential of a deep learning model based on the CNN-LSTM framework in forecasting the sales volume of cancer drugs, with a focus on modeling complex time series data. As advancements in medical technology and cancer treatment continue, the demand for oncology medications is steadily increasing. Accurate forecasting of cancer drug sales plays a critical role in optimizing production planning, supply chain management, and healthcare policy formulation. The dataset used in this research comprises quarterly sales records of a specific cancer drug in Egypt from 2015 to 2024, including multidimensional information such as date, drug type, pharmaceutical company, price, sales volume, effectiveness, and drug classification. To improve prediction accuracy, a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is employed. The CNN component is responsible for extracting local temporal features from the sales data, while the LSTM component captures long-term dependencies and trends. Model performance is evaluated using two widely adopted metrics: Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The results demonstrate that the CNN-LSTM model performs well on the test set, achieving an MSE of 1.150 and an RMSE of 1.072, indicating its effectiveness in handling nonlinear and volatile sales data. This research provides theoretical and technical support for data-driven decision-making in pharmaceutical marketing and healthcare resource planning.

Figures

Figures reproduced from arXiv: 2506.21927 by the authors.

**Figure 2.** The structure of LSTM [14] Finally, the high-level sequential representations produced by the LSTM layers are passed to a fully connected dense layer for regression output, yielding the predicted sales volume for the next quarter. This design enables the LSTM module to effectively model nonlinear dynamics along the time dimension, while also integrating the local structural features extracted by CNN. Experimental re… view at source ↗

Discussion (0). Continue with ORCID to comment.

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Reference graph

Works this paper leans on

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

The growing role of precision and personalized medicine for cancer treatment[J]

Krzyszczyk P, Acevedo A, Davidoff E J, et al. The growing role of precision and personalized medicine for cancer treatment[J]. Technology, 2018, 6(03n04): 79-100

work page 2018

[2] [2]

Comparison of sales income and research and development costs for FDA -approved cancer drugs sold by originator drug companies[J]

Tay-Teo K, Ilbawi A, Hill S R. Comparison of sales income and research and development costs for FDA -approved cancer drugs sold by originator drug companies[J]. JAMA network open, 2019, 2(1): e186875-e186875

work page 2019

[3] [3]

Breath analysis system with convolutional neural network (CNN) for early detection of lung cancer[J]

Lee B, Lee J, Lee J O, et al. Breath analysis system with convolutional neural network (CNN) for early detection of lung cancer[J]. Sensors and Actuators B: Chemical, 2024, 409: 135578

work page 2024

[4] [4]

Comparative study of long short -term memory (LSTM), bidirectional LSTM, and traditional machine learning approaches for energy consumption prediction[J]

Alizadegan H, Rashidi Malki B, Radmehr A, et al. Comparative study of long short -term memory (LSTM), bidirectional LSTM, and traditional machine learning approaches for energy consumption prediction[J]. Energy Exploration & Exploitation, 2025, 43(1): 281-301

work page 2025

[5] [5]

A review of ridge parameter selection: minimization of the mean squared error vs

García García C, Salmeron Gomez R, García Pérez J. A review of ridge parameter selection: minimization of the mean squared error vs. mitigation of multicollinearity[J]. Communications in Statistics-Simulation and Computation, 2024, 53(8): 3686-3698

work page 2024

[6] [6]

Applying Machine Learning and Statistical Forecasting Methods for Enhancing Pharmaceutical Sales Predictions[J]

Fourkiotis K P, Tsadiras A. Applying Machine Learning and Statistical Forecasting Methods for Enhancing Pharmaceutical Sales Predictions[J]. Forecasting, 2024, 6(1): 170-186

work page 2024

[7] [7]

Drug sales forecasting in the pharmaceutical market using deep neural network algorithms[J]

Qassrawi N, Azzeh M, Hijjawi M. Drug sales forecasting in the pharmaceutical market using deep neural network algorithms[J]. International Journal of Systematic Innovation, 2024, 8(3): 63-83

work page 2024

[8] [8]

Predicting drug shortages using pharmacy data and machine learning[J]

Pall R, Gauthier Y, Auer S, et al. Predicting drug shortages using pharmacy data and machine learning[J]. Health care management science, 2023, 26(3): 395-411

work page 2023