IoT and AI for Clinical Decision Support with Hierarchical Attention

 

 

 

Sasikumar  M. S. S.1,* , Ranganayaki V. C.2 , R. Suganthi3, Nalini Subramanian4 , T. Sethukarasi5 ,
T. A. Mohanaprakash6,*

 

1Assistant Professor (Senior Grade), Department of Computer Science & Engineering School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology,  Avadi, Chennai, India

 

2Assitant professor , Department of Computer Science and Engineering, St.Joseph’s Institute of Technology, OMR, Chennai, Tamilnadu, India

 

3Associate Professor, Department of ECE, Panimalar Engineering College, Chennai, Tamilnadu, India

 

4Associate Professor, Department of Information Technology, Rajalakshmi Engineering College, Thandalam, Chennai, Tamil Nadu, India

 

5Professor & Head Department of Computer Science and Engineering, R.M.K Engineering College , Kavaraipettai, Chennai, India

 

6Associate Professor, Department of Computer Science and Engineering, RMK Engineering College , Kavaraipettai , Chennai, India

 

Emails: drsasikumarmss@veltech.edu.in;  ranganayaki.v.c@gmail.com; drrsuganthi@panimalar.ac.in; mrgn.nalini@gmail.com; tsk.cse@rmkec.ac.in; tamohanaprakash@gmail.com

 

Text Box: Abstract The integration of Clinical Informatics (NI) and Artificial Intelligence (AI) promises to transform healthcare by improving clinical decisions, optimizing workflows, and personalizing patient care. However, most current systems fail to incorporate contextual reasoning, real-time adaptation, or ethical sensitivity, leading to fragmented support and increased cognitive burden on clinicians. To address these limitations, we propose NI-AIH—a hybrid clinical-AI framework built on a Context-Enriched Hierarchical Attention Network (CE-HAN). This deep architecture employs dual-attention mechanisms to interpret structured and unstructured clinical data—including EHR entries, nursing notes, and real-time IoT sensor feeds—capturing temporal patterns and contextual cues essential to patient status. The NI-AIH framework consists of four core components: a Clinical Context Engine (CCE) that uses CE-HAN for semantic modeling; a Predictive Care Optimizer (PCO) that applies risk-stratified deep ensembles; an Adaptive Interaction Layer (AIL) that enables seamless nurse–AI collaboration; and an Ethical Decision Integrator (EDI) that uses fuzzy logic to ensure real-time ethical alignment. In a trial deployment within a smart geriatric care unit, NI-AIH demonstrated a 23% improvement in early sepsis detection (p<0.01), a 31% reduction in clinician cognitive load (measured via NASA-TLX survey), and a 19% increase in workflow efficiency compared to conventional rule-based systems. By uniting clinical precision with ethical and context-aware intelligence, NI-AIH establishes a new paradigm for compassionate and effective AI-assisted healthcare.

 

Received: January 08, 2025 Revised: March 06, 2025 Accepted: May 28, 2025

 

Keywords: Clinical Decision Support Systems; Hierarchical Attention Network; Predictive Analytics; IoT in Healthcare; Ethical AI