Machine learning approaches for predicting the onset time of the adverse drug events in oncology

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00209805%3A_____%2F22%3A00079086" target="_blank" >RIV/00209805:_____/22:00079086 - isvavai.cz</a>

Výsledek na webu

<a href="https://reader.elsevier.com/reader/sd/pii/S2666827022000615?token=7A25AA0104C63BDC67F85977B7275D0E9EF303A34FDEC06DD9EEABC342922179FF035F4CE8C1209A1603EE8488F310B0&originRegion=eu-west-1&originCreation=20221110122750" target="_blank" >https://reader.elsevier.com/reader/sd/pii/S2666827022000615?token=7A25AA0104C63BDC67F85977B7275D0E9EF303A34FDEC06DD9EEABC342922179FF035F4CE8C1209A1603EE8488F310B0&originRegion=eu-west-1&originCreation=20221110122750</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.mlwa.2022.100367" target="_blank" >10.1016/j.mlwa.2022.100367</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine learning approaches for predicting the onset time of the adverse drug events in oncology

Popis výsledku v původním jazyce

Predicting the onset time of adverse drug events can substantially lessen the negative impact on the prognosis of cancer patients who are often subject of aggressive and highly toxic treatment regimens. However, the laboratory verification of each patient case to study the mechanics of adverse drug events requires costly, time-intensive research. Thus, to alleviate the efforts required to tackle this problem, using computational models is highly desirable. To provide a suite of such applicable models, we used openly available adverse drug event data resources called FAERS and explored various machine learning paradigms to assess their performance in predicting adverse effect onset days (since the beginning of the treatment). Among various machine learning approaches, we observed that the graph-based embedding model, particularly ComplEx, performed better than other, more traditional machine learning approaches. The embedding learned from the ComplEX trained with k-NN regression for the downstream predictive task obtained the lowest root mean square error, which we consider very promising for further research.

Název v anglickém jazyce

Machine learning approaches for predicting the onset time of the adverse drug events in oncology

Popis výsledku anglicky

Predicting the onset time of adverse drug events can substantially lessen the negative impact on the prognosis of cancer patients who are often subject of aggressive and highly toxic treatment regimens. However, the laboratory verification of each patient case to study the mechanics of adverse drug events requires costly, time-intensive research. Thus, to alleviate the efforts required to tackle this problem, using computational models is highly desirable. To provide a suite of such applicable models, we used openly available adverse drug event data resources called FAERS and explored various machine learning paradigms to assess their performance in predicting adverse effect onset days (since the beginning of the treatment). Among various machine learning approaches, we observed that the graph-based embedding model, particularly ComplEx, performed better than other, more traditional machine learning approaches. The embedding learned from the ComplEX trained with k-NN regression for the downstream predictive task obtained the lowest root mean square error, which we consider very promising for further research.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

20301 - Mechanical engineering

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Machine Learning with Applications

ISSN

2666-8270

e-ISSN

2666-8270

Svazek periodika

9

Číslo periodika v rámci svazku

September 2022

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

14

Strana od-do

100367

Kód UT WoS článku

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EID výsledku v databázi Scopus

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