Evidence map›Paper›PMID 35415412›Full record

ArticleJournal of healthcare informatics research2018

Complexity-Based Spatial Hierarchical Clustering for Malaria Prediction.

Peter Haddawy, Myat Su Yin, Tanawan Wisanrakkit, Rootrada Limsupavanich, Promporn Promrat, Saranath Lawpoolsri, Patiwat Sa-Angchai

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of healthcare informatics research, 2018. 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
0.3field-weighted citation impact, top 37% 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, 7 citations in OpenAlex.

  1. Article
  2. 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

7 authors at 2 institutions in 2 countries.

Peter HaddawyFaculty of ICT, Mahidol University, Nakhon Pathom, Thailand.
Myat Su YinFaculty of ICT, Mahidol University, Nakhon Pathom, Thailand.
Tanawan WisanrakkitFaculty of ICT, Mahidol University, Nakhon Pathom, Thailand.
Rootrada LimsupavanichFaculty of ICT, Mahidol University, Nakhon Pathom, Thailand.
Promporn PromratFaculty of ICT, Mahidol University, Nakhon Pathom, Thailand.
Saranath LawpoolsriFaculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Patiwat Sa-AngchaiFaculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Mahidol University · THMahidol Oxford Tropical Medicine Research Unit · TH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Targeted intervention and resource allocation are essential in effective control of infectious diseases, particularly those like malaria that tend to occur in remote areas. Disease prediction models can help support targeted intervention, particularly if they have fine spatial resolution. But, choosing an appropriate resolution is a difficult problem since choice of spatial scale can have a significant impact on accuracy of predictive models. In this paper, we introduce a new approach to spatial clustering for disease prediction we call

Indexed as

Akaike information criterionBayesian information criterionMalaria predictionSpatial clusteringSpatial epidemiology

Identifiers

PMID35415412
PMCPMC8982740
OpenAlexW2888190514

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.