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Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection
Published on: May 31, 2024
iNICU - Integrated Neonatal Care Unit: Capturing Neonatal Journey in an Intelligent Data Way
Harpreet Singh1,2, Gautam Yadav3, Raghuram Mallaiah4
1Academy of Scientific and Innovative Research, New Delhi, India. harpreet_singh@oxyent.com.
Insights
The integrated Neonatology Intensive Care Unit (iNICU) system uses IoT and Big Data to improve care for preterm infants. This technology aims to reduce neonatal mortality by enhancing monitoring and clinical decision-making.
Area of Science:
- Biomedical Engineering
- Clinical Informatics
- Data Science
Background:
- The neonatal period is critical, with high mortality rates, especially for preterm infants.
- Lack of trained staff and infrastructure hinders quality neonatal care.
- Manual data recording and drug dosage calculations are time-consuming and prone to errors.
Purpose of the Study:
- To develop an integrated system (iNICU) for real-time monitoring and data analysis in Neonatal Intensive Care Units (NICUs).
- To leverage IoT and Big Data technologies to improve the quality of care and reduce neonatal mortality.
- To provide translational research informatics support for clinicians.
Main Methods:
- IoT integration of biomedical devices (monitors, ventilators) using Beaglebone and Intel Edison.
- Cloud-based infrastructure (IBM Softlayer) hosting a Java web application mapping NICU workflow.
- Real-time data capture (vital signs, lab results, PACS) streamed via Apache Kafka to Apache Cassandra NoSQL database.
- Integration of clinical data (feed intake, urine output) in PostgreSQL.
- Application of a clinical rule-based engine (Drools) and deep learning models (R, PMML) for data analysis.
Main Results:
- Established India's first Big Data hub for neonates, capturing millions of data points daily.
- Enabled real-time monitoring of vital parameters and clinical data.
- Facilitated longitudinal data collection for evaluating intervention efficacy.
- Developed a system capable of handling both structured and unstructured data.
Conclusions:
- The iNICU solution enhances care efficiency and addresses skill gaps in neonatal care.
- It enables remote monitoring, crucial for rural healthcare access.
- The system aids in early disease detection and aims to significantly reduce neonatal mortality.
Abstract:
Neonatal period represents first 28 days of life, which is the most vulnerable time for a child's survival especially for the preterm babies. High neonatal mortality is a prominent and persistent problem across the globe. Non-availability of trained staff and infrastructure are the major recognized hurdles in the quality care of these neonates. Hourly progress growth charts and reports are still maintained manually by nurses along with continuous calculation of drug dosage and nutrition as per the changing weight of the baby. iNICU (integrated Neonatology Intensive Care Unit) leverages Beaglebone and Intel Edison based IoT integration with biomedical devices in NICU i.e. monitor, ventilator and blood gas machine. iNICU is hosted on IBM Softlayer based cloud computing infrastructure and map NICU workflow in Java based responsive web application to provide translational research informatics support to the clinicians. iNICU captures real time vital parameters i.e. respiration rate, heart rate, lab data and PACS amounting for millions of data points per day per child. Stream of data is sent to Apache Kafka layer which stores the same in Apache Cassandra NoSQL. iNICU also captures clinical data like feed intake, urine output, and daily assessment of child in PostgreSQL database. It acts as first Big Data hub (of both structured and unstructured data) of neonates across India offering temporal (longitudinal) data of their stay in NICU and allow clinicians in evaluating efficacy of their interventions. iNICU leverages drools based clinical rule based engine and deep learning based big data analytical model coded in R and PMML. iNICU solution aims to improve care time, fills skill gap, enable remote monitoring of neonates in rural regions, assists in identifying the early onset of disease, and reduction in neonatal mortality.
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