Evidence map›Paper›PMID 28748430›Full record

ArticleJournal of medical systems2017

iNICU - Integrated Neonatal Care Unit: Capturing Neonatal Journey in an Intelligent Data Way.

Harpreet Singh, Gautam Yadav, Raghuram Mallaiah, Preetha Joshi, Vinay Joshi, Ravneet Kaur, Suneyna Bansal, Samir K Brahmachari

Abstract read
In one paragraph

Article in Journal of medical systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  15. Clinician-Driven Design ofIEEE journal of translational engineering in health and medicine · 2018
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Harpreet SinghAcademy of Scientific and Innovative Research, New Delhi, India. harpreet_singh@oxyent.com.ORCID http://orcid.org/0000-0002-5000-534X
Gautam YadavKalawati Hospital, Shiv Chowk, Rewari, Haryana, India.
Raghuram MallaiahFortis Le Femme, Greater Kailash -2, New Delhi, India.
Preetha JoshiKokilaben Dhirubhai Ambani Hospital, Mumbai, India.
Vinay JoshiKokilaben Dhirubhai Ambani Hospital, Mumbai, India.
Ravneet KaurOxyent Medical Private Limited, 801 DLF Tower B, New Delhi, 110025, India.
Suneyna BansalOxyent Medical Private Limited, 801 DLF Tower B, New Delhi, 110025, India.
Samir K BrahmachariAcademy of Scientific and Innovative Research, New Delhi, India. skb@igib.res.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Intensive Care Units, NeonatalHumansIndiaInfant, NewbornInfant, PrematureRural PopulationWorkflowDeep learningEMR/hisIoT cloudNeonate healthNeonatology intensive care unitPACS

Identifiers

PMID28748430
PMCPMC5529490

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.