Organizations: Department of Computer Engineering, SRC.,Islamic Azad university, Tehran, Iran · School of Computer Engineering, Iran university of Science and technology, Tehran, Iran
Accurate and interpretable mortality risk prediction in intensive care units (ICUs) remains a critical challenge due to the irregular temporal structure of electronic health records (EHRs), the complexity of longitudinal disease trajectories, and the lack of clinically grounded explanations in many data-driven models. To address these challenges, we propose TA-RNN-Medical-Hybrid, a time-aware and knowledge-enriched deep learning framework that jointly models longitudinal clinical sequences and irregular temporal dynamics through explicit continuous-time encoding, along with standardized medical concept representations. The proposed framework extends time-aware recurrent modeling by integrating explicit continuous-time embeddings that operate independently of visit indexing, SNOMED-based disease representations, and a hierarchical dual-level attention mechanism that captures both visit-level temporal importance and feature/concept-level clinical relevance. This design enables accurate mortality risk estimation while providing transparent and clinically meaningful explanations aligned with established medical knowledge. We evaluate the proposed approach on the MIMIC-III critical care dataset and compare it against strong time-aware and sequential baselines. Experimental results demonstrate that TA-RNN-Medical-Hybrid consistently improves predictive performance in terms of AUC, accuracy, and recall-oriented F2-score. Moreover, qualitative analysis shows that the model effectively decomposes mortality risk across time and clinical concepts, yielding interpretable insights into disease severity, chronicity, and temporal progression. Overall, the proposed framework bridges the gap between predictive accuracy and clinical interpretability, offering a scalable and transparent solution for high-stakes ICU decision support systems.
Figures & tables
Method
Temporal Modeling
Irregular Time Handling
Attention Mechanism
External Medical Knowledge
Interpretability Level
Clinical Applicability
Traditional ML Models
Limited
No
No
No
Feature-level
Moderate
Deep Learning (MLP/CNN)
Limited
No
No
No
Low
Moderate
RNN / GRU
Sequential
Partial
No
No
Low
Moderate
Transformer-based Models
Long-range
Partial
Yes
No
Visit/Modality-level
Moderate
TA-RNN Al Olaimat and Bozdag (2024)
Time-aware sequential
Yes
Visit-level
No
Visit-level
High
GRU-D Che et al. (2018)
✓
✓
No
✗
Hidden-state
Table 1: Comparison of representative ICU mortality prediction models
Figure 1: Pipeline for constructing the ICD-to-SNOMED embedding matrix. ICD codes are normalized and mapped to SNOMED concepts, followed by semantic text embedding using BioClinicalBERT and structural embedding via SNOMED relational graphs.
Figure 2: Overall architecture of the proposed TA-RNN-Medical-Hybrid framework. Diagnosis codes are first mapped to SNOMED-based embeddings and aggregated at the visit level. Explicit temporal embeddings are incorporated to handle irregular visit intervals. The resulting sequence is processed by a BiGRU and multi-head self-attention module, followed by a dual-level attention mechanism (visit + disease-level) and demographic fusion… to generate the final mortality risk prediction.
Figure 3: Summary of model interpretation outputs. The figure illustrates mortality risk stratification, visit-level attention distribution, and ranked disease-level contributions for representative ICU patients.
Figure 4: Temporal disease progression patterns derived from disease-level attribution scores. Increasing, decreasing, and stable contribution trends illustrate how individual diseases influence mortality risk over time.
Figure 5: Example of the proposed clinical interpretation dashboard generated by TA-RNN-Medical-Hybrid. The dashboard integrates predicted mortality risk, visit-level temporal importance, SNOMED-based feature-level attribution, and longitudinal feature progression to support transparent clinical decision-making.
Model
Time-aware Modeling
Irregular Sampling
Attention Mechanism
External Medical Knowledge
Interpretability Level
Logistic Regression
✗
✗
✗
✗
Feature-level
Random Forest / XGBoost
✗
✗
✗
✗
Feature-level
RNN / GRU
✓
✗
✗
✗
Hidden-state
BiGRU
✓
✗
✗
✗
Hidden-state
TA-RNN
✓
✓
Visit-level
✗
Visit-level
Transformer-based ICU Models
✓
Partial
Self-attention
✗
Token-level
Table 2: Methodological comparison of TA-RNN-Medical-Hybrid with representative ICU mortality prediction models.
Model
Accuracy
AUC
F2-score
GRU-D
0.84
0.71
0.88
BEHRT
0.88
0.78
0.92
Med-BERT
0.89
0.80
0.93
TA-RNN
0.86
0.73
0.90
TA-RNN-Medical-Hybrid
0.91
0.82
0.95
Table 3: Performance comparison on ICU mortality prediction.
Model Variant
Accuracy
AUC
F2-score
TA-RNN (Baseline)
0.86
0.73
0.90
TA-RNN-Medical-Hybrid (Full Model)
0.91
0.82
0.95
w/o structured feature embeddings
0.88
0.75
0.91
w/o temporal attention
0.89
0.79
0.91
w/o contextual feature attention
0.89
0.80
0.92
w/o continuous-time encoding
0.89
0.80
0.92
Table 4: Component-wise ablation analysis of TA-RNN-Medical-Hybrid (feature-based).
Model
Accuracy
AUC
F2-score
TA-RNN-Medical-Hybrid
0.91 ± 0.01
0.82 ± 0.02
0.95 ± 0.01
Table 5: Performance of TA-RNN-Medical-Hybrid across 5 random seeds (Mean ± Std).