Artificial Intelligence

Neo-Bedside Monitoring Device for Integrated Neonatal Intensive Care Unit (iNICU)

Neonatal intensive care units generate massive amounts of physiological data from bedside monitors, ventilators, infusion pumps, blood gas analyzers, and other medical devices. Traditionally, these data are manually documented at regular intervals, leading to transcription errors and the loss of valuable high-resolution clinical information.

This paper introduces Neo, an affordable bedside Internet of Things (IoT) device that integrates heterogeneous NICU medical devices into the cloud-based iNICU platform. Neo continuously acquires real-time physiological signals, synchronizes data from multiple vendors, automatically generates nursing charts, and provides clinicians with unified patient trends through web and tablet interfaces.

The platform combines embedded hardware, cloud infrastructure, and machine learning to support early prediction of neonatal morbidity using continuous physiological signals such as heart rate, respiratory rate, and oxygen saturation. Validation using data collected from 92 preterm infants demonstrated the feasibility of real-time clinical analytics for supporting early intervention and improving neonatal care.

Neo established one of the earliest vendor-agnostic bedside device integration platforms specifically designed for neonatal intensive care, laying the foundation for AI-assisted neonatal monitoring systems.

Development of Data Dictionary for Neonatal Intensive Care Unit: Advancement Towards a Better Critical Care Unit

Critical care units generate enormous volumes of clinical, laboratory, and medical device data, yet most electronic medical record systems store this information in proprietary formats that limit interoperability and clinical research. This paper introduces a comprehensive, open-source neonatal intensive care unit (NICU) data dictionary designed to standardize clinical workflows, patient documentation, quality indicators, and medical device integration.

The proposed framework contains 1,555 standardized clinical fields covering patient admission, assessments, nutrition, medications, laboratory investigations, procedures, physiological monitoring, discharge summaries, and quality indicators. The system integrates bedside medical devices, laboratory systems, and electronic medical records while maintaining standardized definitions based on international neonatal guidelines.

The framework was prospectively evaluated across two Level III NICUs involving 344 neonates and demonstrated high data completeness, accuracy, validity, and timeliness while identifying important practice variations associated with neonatal outcomes. The standardized data model establishes a foundation for interoperable neonatal databases, multicenter research, clinical benchmarking, and future artificial intelligence applications in neonatal intensive care.

Deep Learning to Quantify Care Manipulation Activities in Neonatal Intensive Care Units

Premature infants admitted to Neonatal Intensive Care Units (NICUs) experience numerous routine caregiving procedures every day, many of which contribute to cumulative physiological stress that may adversely affect neurodevelopment. Existing approaches for measuring neonatal stress rely on manual documentation using the Neonatal Infant Stressor Scale (NISS), making continuous monitoring impractical in busy clinical environments.

This study developed a multimodal deep learning system that analyzes bedside video recordings together with synchronized physiological signals to automatically detect neonatal care manipulation activities, quantify their duration and frequency, estimate immediate physiological responses, and calculate stress exposure. The framework was trained and evaluated using 289 hours of synchronized video and physiological recordings collected during 330 clinical sessions from 27 neonates across two NICUs. Using advanced computer vision models and temporal activity localization techniques, the system achieved activity quantification statistically equivalent to expert human annotation within clinically acceptable error margins while accurately identifying activities associated with significant physiological changes.

The proposed framework represents an important step toward continuous, objective, AI-powered neonatal stress monitoring capable of supporting clinicians with standardized documentation, automated Neonatal Infant Stressor Scale (NISS) estimation, and real-time decision support in intensive care environments.

Development and Validation of High Definition Phenotype-Based Mortality Prediction in Critical Care Units

Accurate prediction of neonatal mortality remains one of the most challenging problems in critical care. This study developed a High Definition Phenotype (HDP) framework that integrates electronic medical records, laboratory results, bedside physiological monitoring, medications, nutrition, and derived physiological features into a unified time-series representation of each patient.

Clinical data from 1,546 neonatal admissions across eight Level III NICUs were collected, with 757 patients containing complete fixed, intermittent, and continuous datasets for model development. Two predictive approaches—a Logistic Regression Model (LRM) and a Long Short-Term Memory (LSTM) deep learning model—were developed and compared with established neonatal severity scores including CRIB, CRIB-II, SNAP-II, and SNAPPE-II.

Both AI models consistently outperformed traditional severity scores, with the LSTM achieving an AUC of up to 0.96, while also providing continuously updated mortality risk predictions throughout hospitalization. The framework demonstrates the potential of integrating multimodal clinical data into real-time bedside decision support systems capable of detecting patient deterioration earlier than conventional approaches.

Designing a Bedside System for Predicting Length of Stay in a Neonatal Intensive Care Unit

Length of stay (LOS) is an important indicator of neonatal outcomes, healthcare resource utilization, and hospital costs. This study developed an artificial intelligence–based prediction model that estimates NICU length of stay using routinely collected electronic medical record data. Unlike previous approaches, the model incorporated antenatal and perinatal characteristics, deviations from ASPEN nutrition guidelines, deviations from NeoFax medication recommendations, and major neonatal clinical diagnoses. Data from 836 neonatal admissions across two tertiary NICUs were used to develop gestation-specific predictive models and validated using an independent cohort of 211 patients. The resulting bedside interface enables clinicians to visualize patient-specific risk factors contributing to prolonged hospitalization, supporting family counseling, clinical decision-making, and operational planning while demonstrating the feasibility of real-time AI-assisted predictive analytics within neonatal intensive care.

Predicting Clinical Outcomes Using Artificial Intelligence and Machine Learning in Neonatal Intensive Care Units: A Systematic Review

Artificial intelligence (AI) and machine learning (ML) are rapidly transforming neonatal intensive care by enabling predictive analytics from large volumes of clinical data. This systematic review evaluated 68 published studies examining AI and ML applications for predicting neonatal morbidities, mortality, and length of stay in NICUs. The review analyzed prediction models, data sources, machine learning techniques, and performance metrics while identifying current limitations and future opportunities. The authors propose that integrating multimodal data—including electronic medical records, physiologic monitoring, medical imaging, laboratory results, and environmental sensing—can enable next-generation clinical decision support systems that improve neonatal outcomes through personalized, real-time predictive analytics.

An Ontology and Rule-Based Clinical Decision Support System for Personalized Nutrition Recommendations in the Neonatal Intensive Care Unit

Premature infants require individualized nutritional management to support optimal growth and neurodevelopment, yet adherence to neonatal nutrition guidelines remains challenging in busy intensive care units.

This research introduces the Nutrition Recommendation Ontology (NRO), an ontology- and rule-based clinical decision support system that integrates electronic medical record data with standardized neonatal nutrition guidelines. The system models neonatal clinical information using semantic technologies, knowledge graphs, and logical reasoning to generate personalized recommendations for enteral and parenteral nutrition while automatically identifying deviations from established guidelines.

The framework was developed using retrospective data from 601 NICU patients representing 8,460 patient-days of clinical care. The ontology consists of 121 classes, 366 axioms, and 157 semantic rules, enabling automated reasoning based on gestational age, day of life, clinical conditions, risk factors, and neonatal morbidities.

Validation by an expert panel of neonatologists demonstrated 98% agreement between the system-generated recommendations and expert clinical decisions, highlighting the promise of semantic artificial intelligence for improving nutritional management and supporting evidence-based decision making in neonatal intensive care units.

Edge-Hosted LLM-Assisted NICU Discharge Summary Generation: Field-Level Evaluation Using a Clinician-Defined Rubric

Objective
To evaluate an edge-hosted large language model framework capable of generating neonatal intensive care discharge summaries while preserving patient privacy.

Methods
MORPHEUS was evaluated using 401 NICU admissions. Performance was measured using a clinician-defined rubric covering 72 clinical documentation fields, including completeness, clarity, coherence, actionability, and factual accuracy.

Key Findings

• Higher completeness than clinician-authored summaries
• Improved documentation consistency
• Privacy-preserving edge deployment without cloud processing
• Demonstrated feasibility for real-world NICU workflows

Impact

This work demonstrates how edge AI can reduce clinician documentation burden while maintaining high-quality discharge summaries and protecting sensitive patient information.