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# Deep Learning-Driven Abnormal Vital Sign Monitoring in Critical Care Official repository for the paper: **Exploration of deep learning-driven methods for monitoring abnormal vital signs in critical care** This repository contains the implementation of a deep learning framework for real-time abnormal vital-sign monitoring in ICU settings. The proposed method combines temporal-spectral representation learning with context-aware decision-making to improve reliability, adaptability, and interpretability in critical care monitoring. The framework is composed of two main modules: **ViSpecFormer** and **ClinConDec**. --- ## Overview Real-time detection of abnormal vital signs is essential in critical care. Traditional threshold-based monitoring systems often ignore temporal dependencies and interactions among physiological variables, which can reduce performance in complex ICU environments. This project addresses that limitation with a deep learning framework designed for multivariate physiological time series and patient-specific clinical context. The framework follows a two-stage design: 1. **ViSpecFormer** Learns latent representations from multivariate vital-sign sequences using: - input embedding - spectral transformation - temporal self-attention - context-aware fusion 2. **ClinConDec** Refines abnormality predictions through: - dynamic thresholding - temporal smoothing - cross-feature consistency adjustment - rule-guided interpretation - optional memory-based evidence retrieval This separation makes the system easier to interpret and better suited to ICU abnormality monitoring. --- ## Key Features - Temporal-spectral modeling for multivariate vital-sign data - Context-aware abnormality detection instead of fixed-threshold rules - Improved robustness under heterogeneous clinical conditions and sparse observations - Interpretable decision refinement for ICU monitoring - Designed for near-real-time inference on modern hardware --- ## Method Summary ### ViSpecFormer ViSpecFormer is the signal representation module. It converts multivariate physiological observations into latent representations that preserve both temporal dependencies and frequency-related patterns. It uses discrete cosine transform based spectral filtering to capture low-frequency trends and high-frequency fluctuations, followed by temporal self-attention to model long-range dependencies. Context-aware fusion then combines physiological evidence with clinical context. ### ClinConDec ClinConDec is the decision module. Instead of using a fixed global threshold, it computes a patient-specific, context-dependent decision boundary. It further stabilizes predictions through temporal smoothing and can improve interpretability through rule-guided alignment and memory-based retrieval of similar cases. --- ## Data This study is based on public ICU databases: - **MIMIC-IV v2.2** - **eICU Collaborative Research Database** The experiments use task-specific cohorts derived from these sources after preprocessing, harmonization, eligibility screening, and window-level label generation. The datasets were processed and evaluated independently rather than merged into a joint training set. ### Variables Used Common physiological variables include: - heart rate - respiratory rate - oxygen saturation - blood pressure - temperature Selected contextual clinical variables are also incorporated when available. ### Data Access The raw patient-level data cannot be redistributed directly. Access to MIMIC-IV and eICU-CRD requires credentialed approval through the official providers. --- ## Preprocessing The preprocessing pipeline includes: - temporal alignment - resampling - normalization - missing-value handling - sliding-window generation - window-level label assignment Observation windows are 60 seconds with a stride of 10 seconds, and labels are assigned based on whether a predefined abnormal event occurs within a future prediction horizon. --- ## Experimental Setup - Framework implemented in **PyTorch 2.1** - Training hardware includes **NVIDIA A100 (80GB)** - Mixed-precision training used for efficiency - Five-fold stratified cross-validation - Early stopping based on validation AUC - Repeated experiments with different random seeds ### Default Model Configuration - Embedding dimension: `128` - Hidden dimension: `128` - Attention heads: `8` - Dropout: `0.1` - Temporal smoothing kernel: `K = 2` --- ## Results According to the manuscript, the proposed framework consistently outperforms baseline models such as LSTM, GRU, TCN, GRU-D, InceptionTime, and PatchTST across multiple ICU cohorts and benchmark subsets, especially in AUC and F1 Score. The reported computational profile suggests that the model is compatible with near-real-time ICU monitoring on modern server-class hardware. ## Repository Structure ├── configs/ # Configuration files ├── data/ # Data processing scripts or dataset instructions ├── datasets/ # Dataset loaders ├── models/ # ViSpecFormer, ClinConDec, and related modules ├── trainers/ # Training and evaluation logic ├── utils/ # Utility functions ├── scripts/ # Train/test/preprocess scripts ├── notebooks/ # Optional analysis notebooks ├── checkpoints/ # Saved model weights └── README.md Quick Start 1. Preprocess data python scripts/preprocess.py --config configs/preprocess.yaml 2. Train the model python scripts/train.py --config configs/train.yaml 3. Evaluate the model python scripts/evaluate.py --config configs/eval.yaml 4. Run inference python scripts/infer.py --input path/to/sample.csv --checkpoint checkpoints/best_model.pt Update the commands above to match your actual project structure. Citation If you use this repository in your research, please cite: @article{song2025abnormalvitals, title={Exploration of deep learning-driven methods for monitoring abnormal vital signs in critical care}, author={Song, Bingbing and Li, Qunfeng and Chen, Hong}, journal={PeerJ}, year={2025} } Please replace this BibTeX entry with the final published citation information. Reproducibility To support reproducibility, this project is intended to provide: cohort construction protocol preprocessing scripts variable mapping tables label generation rules split-generation procedure training configuration These materials should allow credentialed users to reproduce the experimental pipeline after obtaining access to the original controlled databases. Limitations Although the method shows promising performance, the manuscript notes that further work is needed on: deployment efficiency in resource-constrained environments generalization across institutions prospective validation in real clinical workflows integration with hospital systems License This project is released under the MIT License unless otherwise specified. Acknowledgments We acknowledge the public resources that made this work possible, including MIMIC-IV and eICU-CRD.

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You are given an integer array nums. A tuple (i, j, k) of 3 distinct indices is good if nums[i] == nums[j] == nums[k]. The distance of a good tuple is abs(i - j) + abs(j - k) + abs(k - i), where abs(x) denotes the absolute value of x. Return an integer denoting the minimum possible distance of a good tuple. If no good tuples exist, return -1.

You are given an integer array nums. A tuple (i, j, k) of 3 distinct indices is good if nums[i] == nums[j] == nums[k]. The distance of a good tuple is abs(i - j) + abs(j - k) + abs(k - i), where abs(x) denotes the absolute value of x. Return an integer denoting the minimum possible distance of a good tuple. If no good tuples exist, return -1.