Get started
Work

Lightweight LLM summarization with LSTM for early ICU deterioration prediction

Year: 2026
Type: article
Abstract: Early identification of clinical deterioration remains crucial for ICU patient outcomes, yet conventional prediction models rely exclusively on numerical vital signs, ignoring contextual information in clinical narratives. To develop a computationally efficient multimodal system that integrates lightweight large language model (LLM)-derived structured clinical summaries with temporal vital-sign data for simultaneous prediction of in-hospital mortality, vasopressor-dependent shock, and mechanical ventilation requirements. We analyzed 50,318 ICU stays from the MIMIC-IV v2.2 databases (raw: 73,181 stays; analytic cohort: 50,318 stays from approximately 36,800 unique patients) after applying exclusion criteria. Stays were partitioned at the patient level into training (≈ 68%), validation (≈ 14%), and test (≈ 18%) sets. Clinical notes from the first 24 h were transformed into structured embeddings using template-guided Flan-T5-Large to minimize hallucination. These embeddings were fused with hourly vital signs and laboratory time series in a multi-task LSTM architecture. Class imbalance was addressed with focal loss for the sequential model and synthetic oversampling for tabular baselines. Primary outcomes were in-hospital mortality (11.3%), vasopressor-dependent shock (18.7%), and mechanical ventilation initiation (22.4%) within 48 h following the 24-hour prediction window. The multimodal model achieved AUROCs of 0.876 (95% CI: 0.868–0.883) for mortality, 0.842 (95% CI: 0.835–0.849) for shock, and 0.831 (95% CI: 0.824–0.838) for ventilation improvements of 0.047, 0.038, and 0.032 over LSTM-only baselines, respectively (all p < 0.001). Larger gains were observed in AUPRC (0.063, 0.054, and 0.048). The model showed good calibration (ECE 0.043–0.051) and identified 71.3% of patients with low SOFA scores who died, compared with near-zero sensitivity for traditional scores. Template-guided generation was associated with a lower observed hallucination rate in the reviewed sample (3.2% versus 14.7% for unconstrained generation). The system supports inference in 12–18 ms on a single 24 GB GPU. Constrained lightweight LLM summarization, when combined with temporal modeling, meaningfully enhances early prediction of mortality, shock, and ventilation requirements while remaining feasible for real-world clinical deployment.
DOI: 10.1038/s41598-026-64618-1
Language: English
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7172329716
FWCI:
Cites:
Cited by:
Related to: