| _version_ | 1866902170597588992 |
|---|---|
| author | Yip, Simon |
| author_facet | Yip, Simon |
| contents | <p>Dual-architecture machine learning system for early detection of clinical deterioration in intensive care unit (ICU) patients. The system compares gradient-boosted decision trees (LightGBM) with temporal convolutional networks (TCN) to model complementary aspects of physiological risk using routinely collected clinical observations. Three NEWS2-derived deterioration outcomes are considered: maximum risk level attained during the ICU stay (<code>max_risk</code>), median sustained risk level across the stay (<code>median_risk</code>), and the proportion of time spent in a high-risk state (<code>pct_time_high</code>).</p> <p>Models are trained and evaluated using the PhysioNet MIMIC-IV Clinical Demo v2.2 dataset via two distinct feature-engineering pipelines. The TCN operates on high-resolution timestamp-level temporal features (96-hour windows, 171 features) to capture short-term physiological instability, while the LightGBM model uses patient-level aggregated tabular features (40 features) to characterise longer-term exposure to risk. Comparative evaluation indicates complementary performance profiles: LightGBM exhibits superior calibration and regression fidelity for sustained risk estimation, while TCNs show stronger sensitivity and discrimination for acute deterioration events. Performance is assessed using ROC-AUC, Brier score, and R², alongside interpretability analyses based on SHAP values and saliency methods.</p> <p>The end-to-end pipeline includes clinically validated NEWS2 preprocessing (including CO₂ retainer logic, Glasgow Coma Scale mapping, and supplemental oxygen protocols), comprehensive feature engineering, model training with hyperparameter optimisation, robust metric evaluation, and a command-line inference interface supporting batch prediction and per-patient lookup. Overall, the system demonstrates physiologically plausible predictive behaviour, clinically meaningful interpretability, and a reproducible workflow suitable for extension to full clinical datasets or downstream deployment contexts.</p> <p>This work is intended for research and educational use.</p> <table> <tbody> <tr> <td><strong>Target Outcome</strong></td> <td><strong>Best-Performing Model</strong></td> <td><strong>Key Metric(s) </strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td><code>max_risk </code></td> <td>TCN</td> <td>ROC-AUC = 0.923; </td> <td>Strong acute deterioration detection</td> </tr> <tr> <td><code>median_risk</code></td> <td>LightGBM</td> <td>ROC-AUC = 0.872; Brier Score = 0.065</td> <td>Superior sustained risk calibration</td> </tr> <tr> <td><code>pct_time_high</code> </td> <td>LightGBM</td> <td>R² = 0.793; RMSE = 0.038</td> <td>Higher fidelity estimation of high-risk exposure </td> </tr> </tbody> </table> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18487174 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Time-Series ICU Patient Deterioration Predictor Yip, Simon Early Warning Score ICU Critical Care Patient Deterioration Machine Learning LightGBM Temporal Convolutional Network Time Series Analysis Clinical Prediction NEWS2 Score MIMIC-IV Neural Network Artificial Intelligence Health Informatics <p>Dual-architecture machine learning system for early detection of clinical deterioration in intensive care unit (ICU) patients. The system compares gradient-boosted decision trees (LightGBM) with temporal convolutional networks (TCN) to model complementary aspects of physiological risk using routinely collected clinical observations. Three NEWS2-derived deterioration outcomes are considered: maximum risk level attained during the ICU stay (<code>max_risk</code>), median sustained risk level across the stay (<code>median_risk</code>), and the proportion of time spent in a high-risk state (<code>pct_time_high</code>).</p> <p>Models are trained and evaluated using the PhysioNet MIMIC-IV Clinical Demo v2.2 dataset via two distinct feature-engineering pipelines. The TCN operates on high-resolution timestamp-level temporal features (96-hour windows, 171 features) to capture short-term physiological instability, while the LightGBM model uses patient-level aggregated tabular features (40 features) to characterise longer-term exposure to risk. Comparative evaluation indicates complementary performance profiles: LightGBM exhibits superior calibration and regression fidelity for sustained risk estimation, while TCNs show stronger sensitivity and discrimination for acute deterioration events. Performance is assessed using ROC-AUC, Brier score, and R², alongside interpretability analyses based on SHAP values and saliency methods.</p> <p>The end-to-end pipeline includes clinically validated NEWS2 preprocessing (including CO₂ retainer logic, Glasgow Coma Scale mapping, and supplemental oxygen protocols), comprehensive feature engineering, model training with hyperparameter optimisation, robust metric evaluation, and a command-line inference interface supporting batch prediction and per-patient lookup. Overall, the system demonstrates physiologically plausible predictive behaviour, clinically meaningful interpretability, and a reproducible workflow suitable for extension to full clinical datasets or downstream deployment contexts.</p> <p>This work is intended for research and educational use.</p> <table> <tbody> <tr> <td><strong>Target Outcome</strong></td> <td><strong>Best-Performing Model</strong></td> <td><strong>Key Metric(s) </strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td><code>max_risk </code></td> <td>TCN</td> <td>ROC-AUC = 0.923; </td> <td>Strong acute deterioration detection</td> </tr> <tr> <td><code>median_risk</code></td> <td>LightGBM</td> <td>ROC-AUC = 0.872; Brier Score = 0.065</td> <td>Superior sustained risk calibration</td> </tr> <tr> <td><code>pct_time_high</code> </td> <td>LightGBM</td> <td>R² = 0.793; RMSE = 0.038</td> <td>Higher fidelity estimation of high-risk exposure </td> </tr> </tbody> </table> <p> </p> |
| title | Time-Series ICU Patient Deterioration Predictor |
| topic | Early Warning Score ICU Critical Care Patient Deterioration Machine Learning LightGBM Temporal Convolutional Network Time Series Analysis Clinical Prediction NEWS2 Score MIMIC-IV Neural Network Artificial Intelligence Health Informatics |
| url | https://doi.org/10.5281/zenodo.18487174 |