A novel indirect method for deriving reference intervals through iterative data cleaning guided by self-organizing maps of multi-test patterns (SOM-clean)

SOM-clean represents a practical and robust parametric tool for estimating RIs indirectly from routine laboratory data employing a novel multivariate-based data cleaning scheme.

Authors

Institutions

Publication Details

Journal
Computer Methods and Programs in Biomedicine
Published
2026-02-05
DOI
https://doi.org/10.1016/j.cmpb.2026.109279
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A novel indirect method for deriving reference intervals through iterative data cleaning guided by self-organizing maps of multi-test patterns (SOM-clean)

Anwar Borai, Kiyoshi Ichihara, Teppei Yamashita
Computer Methods and Programs in Biomedicine
Fault Detection and Control Systems
article

A novel indirect method for deriving reference intervals through iterative data cleaning guided by self-organizing maps of multi-test patterns (SOM-clean)

Anwar Borai, Kiyoshi Ichihara, Teppei Yamashita
article en

Abstract

SOM-clean represents a practical and robust parametric tool for estimating RIs indirectly from routine laboratory data employing a novel multivariate-based data cleaning scheme.

Computer Methods and Programs in BiomedicineVol. 278
Tokai University (JP), Yamaguchi University (JP), King Saud bin Abdulaziz University for Health Sciences (SA)
Industry, innovation and infrastructure
Openalex Percentile: Top 66%
Fault Detection and Control Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.