Work

A Concept for Mining Transitive Sequential Patterns from Pancreatic Cancer Patient Journeys

Year: 2024

Type: book-chapter

Abstract: Pancreatic cancer, renowned for its aggressive nature and poor prognosis, necessitates the optimization of treatment strategies. The sequence of procedures in clinical trials is critical, such as evaluating the potential benefits of preoperative chemo-radio-therapy for pancreatic cancer. Nevertheless, we might not be aware of other temporal sequences which have an effect on therapy response or the general outcome. Extracting transitive sequential patterns from patients' medical trajectories allows researchers to identify temporal characteristics for complex diseases. We illustrate how such sequential patterns can be discovered and might be utilized in pancreatic cancer research as well as patient care.

Source: Studies in health technology and informatics

DOI: 10.3233/shti240862

Authors: Jonas Hügel, Jan Janosch Schneider, Daniel Tran Ortega, Ella Maria Jentsch, Sophia Rheinländer, Nils Hendrik Beyer, Hossein Estiri, Christoph Ammer-Hermenau, Elisabeth Hessmann, Alexander Otto König, Volker Ellenrieder, Ulrich Sax

Institutions: Universitätsmedizin Göttingen, Campus-Institut Data Science (CIDAS), University of Göttingen, Harvard University, Massachusetts General Hospital

Language: English

Topic: Data Mining Algorithms and Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4402279211

FWCI: 0.732

Cites:

Cited by: 1

Related to: 10