Evidence map›Paper›PMID 20703618›Full record

ArticleJournal of medical systems2010

A newborn screening system based on service-oriented architecture embedded support vector machine.

Kai-Ping Hsu, Sung-Huai Hsieh, Sheau-Ling Hsieh, Po-Hsun Cheng, Yung-Ching Weng, Jang-Hung Wu, Feipei Lai

Abstract read
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In one paragraph

Article in Journal of medical systems, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 17% of its field
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

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
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

7 authors at 4 institutions in 1 country.

Kai-Ping HsuDepartment of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
Sung-Huai Hsieh
Sheau-Ling Hsieh
Po-Hsun Cheng
Yung-Ching Weng
Jang-Hung Wu
Feipei Lai
National Taiwan University · TWNational Taiwan University Hospital · TWNational Kaohsiung Normal University · TWNational Yang Ming Chiao Tung University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The clinical symptoms of metabolic disorders are rarely apparent during the neonatal period, and if they are not treated earlier, irreversible damages, such as mental retardation or even death, may occur. Therefore, the practice of newborn screening is essential to prevent permanent disabilities in newborns. In the paper, we design, implement a newborn screening system using Support Vector Machine (SVM) classifications. By evaluating metabolic substances data collected from tandem mass spectrometry (MS/MS), we can interpret and determine whether a newborn has a metabolic disorder. In addition, National Taiwan University Hospital Information System (NTUHIS) has been developed and implemented to integrate heterogeneous platforms, protocols, databases as well as applications. To expedite adapting the diversities, we deploy Service-Oriented Architecture (SOA) concepts to the newborn screening system based on web services. The system can be embedded seamlessly into NTUHIS.

Indexed as

Artificial IntelligenceHospital Information SystemsComputer Communication NetworksData MiningHumansInfant, NewbornMetabolism, Inborn ErrorsNeonatal ScreeningSystems IntegrationTaiwanTandem Mass SpectrometryWorkflow

Identifiers

PMID20703618
OpenAlexW2085866348

What OpenQuestion holds

Textmetadata
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.