QuanTiMedAI: Quantum-Enhanced Time-Series Model guided by Agentic AI for Cardiac Arrest Mortality Prediction

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Mutasim Fuad Sarker, Adiba Rahman Namira, Wafa Binte Alam, Md Adnan Arefeen, Mahzabeen Emu, Sumaiya Tabassum Nimi
• Venue: arXiv: cs.AI
• Published: 2026-08-06
• Collected: 2026-08-07
• URL: http://arxiv.org/abs/2608.06294v1
• PDF: https://arxiv.org/pdf/2608.06294v1
• DOI: UNKNOWN

Plain-Language Brief

The researchers present QuanTiMedAI, a quantum-agentic framework designed for predicting cardiac arrest mortality rates. This system combines an agentic large language model for clinically informed feature discovery with a compact quantum recurrent network that considers the temporal progression of physiological deterioration and recovery throughout a patient's ICU stay. The study claims that this approach outperforms conventional methods, achieving competitive predictive performance while using significantly fewer parameters.

What the Document Says

• The study introduces QuanTiMedAI, a quantum-agentic framework for cardiac arrest mortality prediction.
• The system combines an agentic large language model and a compact quantum recurrent network.
• The agentic large language model is used for clinically informed feature discovery.
• The quantum recurrent network is designed for temporality aware mortality prediction.
• Findings show that the agentic LLM-guided feature selection outperforms conventional methods.
• QuanTiMedAI's quantum architecture achieves competitive predictive performance with fewer parameters.
• On a MIMIC-IV cohort, QuanTiMedAI attains an AUROC of 0.852 using 605 parameters, improving approximately 2.9% over the current state-of-the-art baseline.
• A structured ablation study validates the contribution of each architectural design choice.

Key Concepts Mentioned

• Cardiac arrest
• Electronic health record data
• Mortality prediction
• Quantum-agentic framework
• Agentic large language model
• Compact quantum recurrent network
• Temporal progression
• Physiological deterioration and recovery
• ICU stay
• AUROC
• Parameter count
• Feature selection
• Conventional methods
• Ablation study

Claims Made by the Document

• QuanTiMedAI outperforms conventional feature selection approaches.
• QuanTiMedAI achieves competitive predictive performance with fewer parameters.
• QuanTiMedAI improves approximately 2.9% over the current state-of-the-art baseline.

Evidence Used

• Results from a study on a MIMIC-IV cohort of cardiac arrest patients.
• A structured ablation study comparing QuanTiMedAI with conventional methods.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Cardiac arrest remains one of the most lethal conditions encountered in intensive care units. Despite the growing availability of electronic health record data, existing mortality prediction studies in this population largely depend on static summaries derived from early admission. Such approaches ignore the temporal progression of physiological deterioration and recovery that unfolds throughout a patient's ICU stay. To address this limitation, we introduce QuanTiMedAI, a quantum-agentic framework developed for cardiac arrest mortality prediction using agentic AI guided quantum enhancement time series model. The proposed system combines an agentic large language model (LLM) for clinically informed feature discovery with a compact quantum recurrent network for temporality aware mortality prediction. Our findings demonstrate that agentic LLM-guided feature selection consistently outperforms conventional feature selection approaches, and the proposed quantum architecture achieves competitive predictive performance through nonlinear feature enhancement while keeping the number of parameters very low. Through extensive experimentation on a MIMIC-IV cohort of cardiac arrest patients, QuanTiMedAI's quantum-enhanced architecture attains an AUROC of 0.852 using only 605 parameters, an improvement of approximately 2.9% over a current state-of-the-art baseline for this task. A structured ablation study systematically validates the contribution of each architectural design choice. These results show that quantum-enhanced sequential modeling can exceed classical recurrent networks while using substantially fewer parameters.