Evidence map›Paper›PMID 40121476›Full record

ArticleInternational journal of health geographics2025

Designing a clustering algorithm for optimizing health station locations.

Pasi Fränti, Sami Sieranoja, Tiina Laatikainen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of health geographics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

3 authors.

Pasi FräntiMachine Learning Group, School of Computing, University of Eastern Finland, P.O. Box 111, 80101, Joensuu, Finland. pasi.franti@uef.fi.
Sami SieranojaMachine Learning Group, School of Computing, University of Eastern Finland, P.O. Box 111, 80101, Joensuu, Finland.
Tiina LaatikainenInstitute of Public Health and Clinical Nutrition, University of Eastern Finland, P.O. Box 1627, 70211, Kuopio, Finland.

Funding

Academy of Finland 336325Academy of Finland 336330
6 · The paper itself

Abstract

In this paper, we define the optimization of health station locations as a clustering problem. We design a robust algorithm for the problem using a pre-calculated overhead graph for fast distance calculations and apply a robust clustering algorithm called random swap to provide accurate optimization results. We study the effect of three cost functions (Euclidean distance, squared Euclidean distance, travel cost) using real patient locations in North Karelia, Finland. We compare the optimization results with the existing health station locations. We found that the algorithm optimized the locations beyond administrative borders and strongly utilized the transport network. The results can provide additional insight for the decision-makers.

Indexed as

AlgorithmsCluster AnalysisClustering AlgorithmsFinlandHumansClusteringFacility locationHealth care optimizationMaximum coverageRandom swap

Identifiers

PMID40121476
PMCPMC11929219

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.