Augmented visualization of class-cluster match in patient similarity networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15110%2F25%3A73633234" target="_blank" >RIV/61989592:15110/25:73633234 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27240/25:10260174

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s41109-025-00741-8?utm_source=getftr&utm_medium=getftr&utm_campaign=getftr_pilot&getft_integrator=clarivate" target="_blank" >https://link.springer.com/article/10.1007/s41109-025-00741-8?utm_source=getftr&utm_medium=getftr&utm_campaign=getftr_pilot&getft_integrator=clarivate</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s41109-025-00741-8" target="_blank" >10.1007/s41109-025-00741-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Augmented visualization of class-cluster match in patient similarity networks

Popis výsledku v původním jazyce

Visual representation of data mining results is essential for accurate assessment by domain experts without extensive knowledge of complex relationships in multi-label data. Similarity networks are a commonly used tool for analyzing such data, utilizing data clustering and community detection and visualizing the relationships between individual objects in an understandable form. In clinical data mining, a patient similarity network (PSN) can help clinicians identify patient clusters with representative labels and interpret their relationships. This article demonstrates the use of the Matthews correlation coefficient (MCC) to analyze cluster-class relationships to complement the PSN visualization in several synthetic datasets. We then discuss the limitations of MCC for this application and propose a modification in the form of a rescaled MCC (rMCC). Furthermore, we introduce a novel measure, Connection Purity, that complements rMCC in an informative way. We propose an augmented visualization of patient similarity networks utilizing both measures. We demonstrate this approach on several real-world datasets, showing how clinical intuition may be biased and how our method helps to rectify it.

Název v anglickém jazyce

Augmented visualization of class-cluster match in patient similarity networks

Popis výsledku anglicky

Visual representation of data mining results is essential for accurate assessment by domain experts without extensive knowledge of complex relationships in multi-label data. Similarity networks are a commonly used tool for analyzing such data, utilizing data clustering and community detection and visualizing the relationships between individual objects in an understandable form. In clinical data mining, a patient similarity network (PSN) can help clinicians identify patient clusters with representative labels and interpret their relationships. This article demonstrates the use of the Matthews correlation coefficient (MCC) to analyze cluster-class relationships to complement the PSN visualization in several synthetic datasets. We then discuss the limitations of MCC for this application and propose a modification in the form of a rescaled MCC (rMCC). Furthermore, we introduce a novel measure, Connection Purity, that complements rMCC in an informative way. We propose an augmented visualization of patient similarity networks utilizing both measures. We demonstrate this approach on several real-world datasets, showing how clinical intuition may be biased and how our method helps to rectify it.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Applied Network Science

ISSN

2364-8228

e-ISSN

2364-8228

Svazek periodika

10

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

21

Strana od-do

52

Kód UT WoS článku

001601175600001

EID výsledku v databázi Scopus

2-s2.0-105019792212