From Symptoms to Services: An LLM Chatbot for Effective Departmental Referral

Detalhes bibliográficos
Autor(a) principal: Shi, Qi
Data de Publicação: 2024
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/164857
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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spelling From Symptoms to Services: An LLM Chatbot for Effective Departmental ReferralLarge Language Models (LLMs)Symptom CheckerMedical Diagnosis SupportArtificial IntelligenceDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoProject Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThis study explores integrating large language models (LLM) into the medical domain, focusing on developing and using the LLM tool Chat-SymChecker. Although LLM technology, such as ChatGPT and Copilot, is evolving rapidly, their use in medical consultations is still limited due to their complexity. To address this issue, we propose using Chat-Symptom Checker to supplement primary care consultation and specialist referral. Chat-Symptom Checker is based on the LLaMA model and is trained on extensive medical Question-answer datasets and patient-specific data from electronic health records, allowing it to provide rapid initial assessment and efficiently direct patients to the proper medical department. This article describes Chat-Symptom Checker's development process, functionality, and potential impact in increasing hospital efficiency, accelerating diagnostic procedures, and enhancing patient care. Chat-Symptom Checker shows the capability of processing complex natural language input, allowing users to describe symptoms and receive clear, individualized feedback. By integrating comprehensive patient data, such as past medical history and family history, the system will guide users to the proper medical department and provide initial recommendations and potential diagnoses, which can significantly decrease wait times and labor costs while simultaneously improving service efficiency. However, there are several challenges with our model. Issues such as redundant or nonsensical queries still need to be refined. In addition, an evaluation of the text quality of LLMs reveals that data volume is not necessarily correlated with enhanced performance. Studies reveal that smaller datasets with better text quality can perform better than more enormous datasets that lack context coherence. This shows the significance of data quality and contextual relevance in LLM medical model training. Despite some remaining limitations, Chat-Symptom Checker can serve as a beneficial healthcare support tool.Neto, Miguel de Castro Simões FerreiraHan, QiweiRUNShi, Qi2024-03-13T14:20:19Z2024-02-022024-02-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/164857TID:203544501enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-18T01:46:50Zoai:run.unl.pt:10362/164857Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T04:02:02.759952Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv From Symptoms to Services: An LLM Chatbot for Effective Departmental Referral
title From Symptoms to Services: An LLM Chatbot for Effective Departmental Referral
spellingShingle From Symptoms to Services: An LLM Chatbot for Effective Departmental Referral
Shi, Qi
Large Language Models (LLMs)
Symptom Checker
Medical Diagnosis Support
Artificial Intelligence
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short From Symptoms to Services: An LLM Chatbot for Effective Departmental Referral
title_full From Symptoms to Services: An LLM Chatbot for Effective Departmental Referral
title_fullStr From Symptoms to Services: An LLM Chatbot for Effective Departmental Referral
title_full_unstemmed From Symptoms to Services: An LLM Chatbot for Effective Departmental Referral
title_sort From Symptoms to Services: An LLM Chatbot for Effective Departmental Referral
author Shi, Qi
author_facet Shi, Qi
author_role author
dc.contributor.none.fl_str_mv Neto, Miguel de Castro Simões Ferreira
Han, Qiwei
RUN
dc.contributor.author.fl_str_mv Shi, Qi
dc.subject.por.fl_str_mv Large Language Models (LLMs)
Symptom Checker
Medical Diagnosis Support
Artificial Intelligence
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Large Language Models (LLMs)
Symptom Checker
Medical Diagnosis Support
Artificial Intelligence
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2024
dc.date.none.fl_str_mv 2024-03-13T14:20:19Z
2024-02-02
2024-02-02T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/164857
TID:203544501
url http://hdl.handle.net/10362/164857
identifier_str_mv TID:203544501
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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