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Klassificering av transkriberade telefonsamtal med Support Vector Machines för ökad effektivitet inom vården
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Classification of transcribed telephone calls with support vector machines for increased efficiency in healthcare (English)
Abstract [sv]

Patientnämndens förvaltning i Stockholm tar årligen emot tusentals samtal som önskar framföra klagomål på vården i Region Stockholm. Syftet med arbetet är att undersöka hur en NLP-robot för klassificering av inkomna klagomål skulle kunna bidra till en ökad effektivitet av verksamheten.

Klassificeringen av klagomålen har utförts med hjälp av en metod baserad på Support Vector Machines. För att optimera modellens korrekthet undersöktes hur längden av ordvektorerna påverkar korrektheten. Modellen gav en slutgiltig korrekthet 53,10 %. Detta resultat analyserades sedan med målsättningen att identifiera potentiella förbättringsmöjligheter hos modellen. För framtida arbeten kan det därför vara intressant att undersöka hur antalet samtal, antalet personer som spelar in samtal och klassfördelningen i datamängden påverkar korrektheten.

För att undersöka hur effektiviteten hos Patientnämndens förvaltning i Stockholm skulle påverkas av implementeringen av en NLP-robot användes en SWOT-analys. Denna analys visade på tydliga fördelar med automatisering av klagomålshanteringen, men att en sådan implementation måste ske med försiktighet där det säkerställs att tillgången på kompetens är tillräcklig för att förebygga potentiella hot.

Abstract [en]

Every year Patientnämnden recieves thousands of phone calls from patients wishing to make complaints about the health care in Stockholm. The aim of this work is to investigate how an NLP-robot for classification of recieved phone calls would contribute to an increased efficiency of the operation.

The classification of the complaints has been made using a method based on Support Vector Machines. In order to optimize the accuracy of the model the impact of the length of the word vector has been investigated. The final result was an accuracy of 53.10%. The result was analyzed with the goal to identify potential opportunities of improvement of the model. For future work it could be interesting to investigate in how the number of calls, the number of people recording the calls and the distribution between the classes affect the accuracy

A SWOT-analysis was performed in order to investigate in how the efficiency of Patientnämnden would be affected by the implementation of an NLP-robot. The analysis showed apparent benefits of automation of complaint management, but also that such an implementation must be done with great caution in order to be able to ensure that the available competence is high enough to prevent potential threats.

Place, publisher, year, edition, pages
2019. , p. 12
Series
TRITA-EECS-EX ; 2019:285
Keywords [en]
NLP, Text Classification, SVM, Operational efficiency, ASR, Customer Service
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-262043OAI: oai:DiVA.org:kth-262043DiVA, id: diva2:1360706
Examiners
Available from: 2019-11-07 Created: 2019-10-14 Last updated: 2019-11-07Bibliographically approved

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