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Analysis of Emergency Medical Transport Datasets using Machine Learning
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Analys av ambulanstransport medelst maskininlärning (Swedish)
Abstract [en]

The selection of hospital once an ambulance has picked up its patient is today decided by the ambulance staff. This report describes a supervised machinelearning approach for predicting hospital selection. This is a multi-classclassification problem. The performance of random forest, logistic regression and neural network were compared to each other and to a baseline, namely the one rule-algorithm. The algorithms were applied to real world data from SOS-alarm, the company that operate Sweden’s emergency call services. Performance was measured with accuracy and f1-score. Random Forest got the best result followed by neural network. Logistic regression exhibited slightly inferior results but still performed far better than the baseline. The results point toward machine learning being a suitable method for learning the problem of hospital selection.

Abstract [sv]

Beslutet om till vilket sjukhus en ambulans ska köra patienten till bestäms idag av ambulanspersonalen. Den här rapporten beskriver användandet av övervakad maskininlärning för att förutsåga detta beslut. Resultaten från algoritmerna slumpmässig skog, logistisk regression och neurala nätvärk jämförs med varanda och mot ett basvärde. Basvärdet erhölls med algorithmen en-regel. Algoritmerna applicerades på verklig data från SOS-alarm, Sveriges operatör för larmsamtal. Resultaten mättes med noggrannhet och f1-poäng. Slumpmässigskog visade bäst resultat följt av neurala nätverk. Logistisk regression uppvisade något sämre resultat men var fortfarande betydligt bättre än basvärdet. Resultaten pekar mot att det är lämpligt att använda maskininlärning för att lära sig att ta beslut om val av sjukhus.

Place, publisher, year, edition, pages
2017. , p. 37
Keyword [en]
machine learning; supervised learning; multiclass classification; EMS
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-215162OAI: oai:DiVA.org:kth-215162DiVA, id: diva2:1146717
External cooperation
Carmenta
Subject / course
Computer Science
Educational program
Master of Science - Engineeering Physics
Supervisors
Examiners
Available from: 2017-10-16 Created: 2017-10-03 Last updated: 2018-01-13Bibliographically approved

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Analysis of Emergency Medical Transport Datasets using Machine Learning(2243 kB)21 downloads
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