kth.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Application of Process Mining for Modelling Small Cell Lung Cancer Prognosis
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.ORCID iD: 0000-0002-3398-2296
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.ORCID iD: 0000-0003-1126-3781
Department of Oncology-Pathology, Karolinska Institutet and the Thoracic Oncology Center, Karolinska University Hospital, Stockholm, Sweden.
Department of Oncology-Pathology, Karolinska Institutet and the Thoracic Oncology Center, Karolinska University Hospital, Stockholm, Sweden.
Show others and affiliations
2023 (English)In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 302, p. 18-22Article in journal (Refereed) Published
Abstract [en]

Process mining is a relatively new method that connects data science and process modelling. In the past years a series of applications with health care production data have been presented in process discovery, conformance check and system enhancement. In this paper we apply process mining on clinical oncological data with the purpose of studying survival outcomes and chemotherapy treatment decision in a real-world cohort of small cell lung cancer patients treated at Karolinska University Hospital (Stockholm, Sweden). The results highlighted the potential role of process mining in oncology to study prognosis and survival outcomes with longitudinal models directly extracted from clinical data derived from healthcare.

Place, publisher, year, edition, pages
IOS Press , 2023. Vol. 302, p. 18-22
Keywords [en]
oncology, Process mining, Real-world Data, small cell lung cancer, treatment decision
National Category
Cancer and Oncology
Research subject
Applied and Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-329927DOI: 10.3233/SHTI230056ISI: 001071432900004PubMedID: 37203601Scopus ID: 2-s2.0-85159759671OAI: oai:DiVA.org:kth-329927DiVA, id: diva2:1774554
Conference
The 33rd Medical Informatics Europe Conference, MIE2023, Gothenburg, Sweden. May 22-25, 2023.
Note

QC 20230628

Available from: 2023-06-26 Created: 2023-06-26 Last updated: 2023-11-07Bibliographically approved

Open Access in DiVA

fulltext(880 kB)38 downloads
File information
File name FULLTEXT01.pdfFile size 880 kBChecksum SHA-512
141d70fc995f938fc87d4f5c321767b3680e0c1ab0c98a1bbde9c6356e08cf9d8838d52535dea72f979e04a0e7a88da02cc3c9336ab638922394166c34ee80dc
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMedScopusIOS Press eBooks - Application of Process Mining for Modelling Small Cell Lung Cancer Prognosis

Authority records

Marzano, LucaMeijer, SebastiaanRaghothama, JayanthDarwich, Adam S.

Search in DiVA

By author/editor
Marzano, LucaMeijer, SebastiaanRaghothama, JayanthDarwich, Adam S.
By organisation
Health Informatics and Logistics
In the same journal
Studies in Health Technology and Informatics
Cancer and Oncology

Search outside of DiVA

GoogleGoogle Scholar
Total: 38 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 274 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf