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Arho Virkki

dblp:59/6544 · also Arho Veli Virkki · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2018
0000-0001-7731-5869ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 77% Bioinformatics and computational biology · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics › clinical prediction
clinical prognosis
0.312018
ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts · Bioinform. 2018
Bioinformatics and computational biology › cancer genomics
prostate cancer
0.112018
ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts · Bioinform. 2018

Methods — techniques the papers use, named apart from their topics

prognostic modeling · 0.3
YearPublicationVenuePosition
2018 ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts
abstract
Motivation: Prognostic models are widely used in clinical decision-making, such as risk stratification and tailoring treatment strategies, with the aim to improve patient outcomes while reducing overall healthcare costs. While prognostic models have been adopted into clinical use, benchmarking their performance has been difficult due to lack of open clinical datasets. The recent DREAM 9.5 Prostate Cancer Challenge carried out an extensive benchmarking of prognostic models for metastatic Castration-Resistant Prostate Cancer (mCRPC), based on multiple cohorts of open clinical trial data. Results: We make available an open-source implementation of the top-performing model, ePCR, along with an extended toolbox for its further re-use and development, and demonstrate how to best apply the implemented model to real-world data cohorts of advanced prostate cancer patients. Availability and implementation: The open-source R-package ePCR and its reference documentation are available at the Central R Archive Network (CRAN): https://CRAN.R-project.org/package=ePCR. R-vignette provides step-by-step examples for the ePCR usage. Supplementary information: Supplementary data are available at Bioinformatics online.
Teemu D. Laajala, Mika Murtojärvi, Arho Virkki, Tero Aittokallio
Bioinform.3
2008 Overnight features of transcutaneous carbon dioxide measurement as predictors of metabolic status
Arho Virkki, Olli Polo, Tarja Saaresranta, Anne Laapotti-Salo, Mats Gyllenberg, Tero Aittokallio
Artif. Intell. Medicine1