From simulations to surrogates: Neural networks enhancing burn wound healing predictions
Bibliographic record
Abstract
Burn injuries trigger substantial inflammation, complicating wound healing and potentially leading to severe systemic complications. Understanding the immune response to burns is crucial for improving treatment. Although agent-based models (ABMs) are valuable for studying these interactions, they are computationally demanding. This paper explores the integration of neural networks (NNs) as surrogate models to approximate and forecast ABM simulation results in predicting cytokine concentrations over time and space. We present the development of a baseline ABM using the CompuCell3D software, simulating the innate immune response and generating extensive cytokine concentration data. This data is processed and prepared for neural network training, involving data cleaning, transformation into suitable formats, and a time-series-aware train-test split. We then implement and assess various neural network architectures. Each model is designed to capture the temporal and spatial dynamics of cytokine concentrations, with adjusted model architectures (kernels, number of layers, neurons per layer) to better suit this problem. The models are evaluated using Mean Squared Error, R-squared, and Mean Absolute Percentage Error. In this paper, we assess how different NN architectures (convolutional neural networks (CNNs), long short-term memory (LSTM) neural networks, attention mechanisms, and physics-informed neural networks (PINNs)) predict the concentration of cytokines in this biological system. We find that STA-LSTM generally performs best across statistical metrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".