Comparing Machine Learning Algorithms for Medical Time-Series Data
Paper i proceeding, 2024

Medical software becomes increasingly advanced and more mission-critical. Machine learning is one of the methods which is used in medical software to tackle a diversity of patient data, problems with data quality and providing the ability to process increasingly large amounts of data from medical procedures. However, one of the challenges is the lack of comparisons of algorithms in-situ, during medical procedures. This paper explores the potential of performing real-time comparisons of algorithms for early stroke detection during carotid endarterectomy. SimSAX, DTW (dynamic time warping), and Pearson correlation were compared based on the real-time data against medical specialists in clinical evaluations. The analysis confirmed the general feasibility of the app- roach, though the algorithms were inadequate in extracting significant information from specific signals. Interviews with physicians revealed a positive outlook toward the system's potential, advocating for further investigation. Despite their limitations, the algorithms and the prototype application provides a promising foundation for future development of new methods for detecting stroke.

Machine learning

SimSAX

stroke

dynamic time warping

Författare

Alex Helmersson

Student vid Chalmers

Faton Hoti

Student vid Chalmers

Sebastian Levander

Student vid Chalmers

Aliasgar Shereef

Student vid Chalmers

Emil Svensson

Student vid Chalmers

Ali El-Merhi

Göteborgs universitet

Sahlgrenska universitetssjukhuset

Richard Vithal

Göteborgs universitet

Sahlgrenska universitetssjukhuset

Jaquette Liljencrantz

Sahlgrenska universitetssjukhuset

Göteborgs universitet

Linda Block

Göteborgs universitet

Sahlgrenska universitetssjukhuset

Helena Odenstedt Hergès

Göteborgs universitet

Sahlgrenska universitetssjukhuset

Miroslaw Staron

Chalmers, Data- och informationsteknik, Software Engineering

Göteborgs universitet

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 14483 LNCS 200-207
9783031492655 (ISBN)

24th International Conference on Product-Focused Software Process Improvement, PROFES 2023
Dornbirn, Austria,

Ämneskategorier

Medicinsk laboratorie- och mätteknik

Sannolikhetsteori och statistik

Datavetenskap (datalogi)

DOI

10.1007/978-3-031-49266-2_14

Mer information

Senast uppdaterat

2024-07-30