AI-sammanfattade klagomål på hälso- och sjukvård
(2023) EITL05 20231Department of Electrical and Information Technology
- Abstract (Swedish)
- Detta examensarbete utfördes i samarbete med Patientnämnden Skåne, som är en av Region Skånes fristående förvaltningar. Patientnämnden ansvarar bland annat för hantering av patienters klagomål om hälso- och sjukvård som tillhör Region Skåne. Att sammanfatta klagomål är en av uppgifterna inom hanteringen av klagomål. Patientnämnden står inför två utmaningar med denna uppgift. För det första är uppgiften tidskrävande. För det andra varierar kvaliteten på sammanfattningarna beroende på vem som är skribent. Patientnämnden ser artificiell intelligens (AI) som en möjlig lösning till dessa två utmaningar. Syftet med detta examensarbete är att undersöka vilka AI-modeller som kan generera bra sammanfattningar på klagomål.
Under examensarbetet... (More) - Detta examensarbete utfördes i samarbete med Patientnämnden Skåne, som är en av Region Skånes fristående förvaltningar. Patientnämnden ansvarar bland annat för hantering av patienters klagomål om hälso- och sjukvård som tillhör Region Skåne. Att sammanfatta klagomål är en av uppgifterna inom hanteringen av klagomål. Patientnämnden står inför två utmaningar med denna uppgift. För det första är uppgiften tidskrävande. För det andra varierar kvaliteten på sammanfattningarna beroende på vem som är skribent. Patientnämnden ser artificiell intelligens (AI) som en möjlig lösning till dessa två utmaningar. Syftet med detta examensarbete är att undersöka vilka AI-modeller som kan generera bra sammanfattningar på klagomål.
Under examensarbetet definierades kriterierna för en bra sammanfattning. Olika offentligt tillgängliga dataset som kan vara passande för sammanfattning av klagomål utforskades eftersom ett dataset innehållande Patientnämndens klagomål och tillhörande sammanfattningar inte var tillgängligt för detta examensarbete. Utifrån resultaten av utforskningen bedömdes att datasetet cnn_dailymail vara mest lämplig för sammanfattning av klagomål, jämfört med andra tillgängliga dataset. Sedan kartlades olika modeller som kan utföra sammanfattning varefter modellerna BART, PEGASUS, TextRank, T5, Flan-T5 och GPT-Sw3 valdes för vidare undersökning. För varje modell genererades sammanfattningar på fabricerade klagomål. De fabricerade klagomålen var erhållna från Patientnämnden.
Resultaten visade att de genererade sammanfattningarna från Flan-T5 och GPT-Sw3 inte uppnådde kriterierna för en bra sammanfattning. Patientnämnden utvärderade de genererade sammanfattningarna från BART, PEGASUS, TextRank och T5 utifrån kriterierna. Resultatet var att TextRank genererade bäst sammanfattningar på klagomål. (Less) - Abstract
- This thesis was conducted in collaboration with the Patients’ Advisory Committee, which is one of the independent administrations of Region Skåne. Patients’ Advisory Committee is responsible, among other things, for handling patient complaints regarding healthcare services belonging to Region Skåne. Summarizing complaints is one of the tasks within the complaint handling process. The Patients’ Advisory Committee faces two challenges with this task. Firstly, the task is time-consuming. Secondly, the quality of the summaries varies depending on the writer. The Patients’ Advisory Committee sees artificial intelligence (AI) as a possible solution to these two challenges. The purpose of this thesis is to investigate which AI models can generate... (More)
- This thesis was conducted in collaboration with the Patients’ Advisory Committee, which is one of the independent administrations of Region Skåne. Patients’ Advisory Committee is responsible, among other things, for handling patient complaints regarding healthcare services belonging to Region Skåne. Summarizing complaints is one of the tasks within the complaint handling process. The Patients’ Advisory Committee faces two challenges with this task. Firstly, the task is time-consuming. Secondly, the quality of the summaries varies depending on the writer. The Patients’ Advisory Committee sees artificial intelligence (AI) as a possible solution to these two challenges. The purpose of this thesis is to investigate which AI models can generate good summaries of complaints.
During the thesis work, the criteria for a good summary were defined. Various publicly available datasets suitable for summarizing complaints were explored since a dataset containing the complaints and corresponding summaries from the Patients’ Advisory Committee was not available for this thesis. Based on the results of the exploration, it was determined that a Swedish translated version of the dataset cnn_dailymail was the most suitable dataset for summarizing complaints, compared to other available datasets. Subsequently, different models capable of performing summarization were identified, and the models BART, PEGASUS, TextRank, T5, Flan T5, and GPT-Sw3 were selected for further study. For each model, summaries were generated for fabricated complaints. The fabricated complaints were obtained from the Patients’ Advisory Committee.
The results showed that the generated summaries from Flan-T5 and GPT-Sw3 did not meet the criteria for a good summary. The Patients’ Advisory Committee evaluated the generated summaries from BART, PEGASUS, TextRank, and T5 based on the criteria. The result was that TextRank generated the best summaries. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9125590
- author
- Aho, David LU and Nhieu, Leah LU
- supervisor
- organization
- course
- EITL05 20231
- year
- 2023
- type
- M2 - Bachelor Degree
- subject
- keywords
- Dataset, AI models, Natural Language Processing, Text summarization, Machine Learning, BART, PEGASUS, T5, Flan-T5, GPT-Sw3
- report number
- LU/LTH-EIT 2023-928
- language
- Swedish
- id
- 9125590
- date added to LUP
- 2023-06-20 11:42:25
- date last changed
- 2023-06-21 16:29:13
@misc{9125590, abstract = {{This thesis was conducted in collaboration with the Patients’ Advisory Committee, which is one of the independent administrations of Region Skåne. Patients’ Advisory Committee is responsible, among other things, for handling patient complaints regarding healthcare services belonging to Region Skåne. Summarizing complaints is one of the tasks within the complaint handling process. The Patients’ Advisory Committee faces two challenges with this task. Firstly, the task is time-consuming. Secondly, the quality of the summaries varies depending on the writer. The Patients’ Advisory Committee sees artificial intelligence (AI) as a possible solution to these two challenges. The purpose of this thesis is to investigate which AI models can generate good summaries of complaints. During the thesis work, the criteria for a good summary were defined. Various publicly available datasets suitable for summarizing complaints were explored since a dataset containing the complaints and corresponding summaries from the Patients’ Advisory Committee was not available for this thesis. Based on the results of the exploration, it was determined that a Swedish translated version of the dataset cnn_dailymail was the most suitable dataset for summarizing complaints, compared to other available datasets. Subsequently, different models capable of performing summarization were identified, and the models BART, PEGASUS, TextRank, T5, Flan T5, and GPT-Sw3 were selected for further study. For each model, summaries were generated for fabricated complaints. The fabricated complaints were obtained from the Patients’ Advisory Committee. The results showed that the generated summaries from Flan-T5 and GPT-Sw3 did not meet the criteria for a good summary. The Patients’ Advisory Committee evaluated the generated summaries from BART, PEGASUS, TextRank, and T5 based on the criteria. The result was that TextRank generated the best summaries.}}, author = {{Aho, David and Nhieu, Leah}}, language = {{swe}}, note = {{Student Paper}}, title = {{AI-sammanfattade klagomål på hälso- och sjukvård}}, year = {{2023}}, }