Publicação
Predictive modelling for clinical trial completion: assessing the phase success - incorporating RAG techniques for predictive modelling of clinical trial outcomes
| Resumo: | This study investigates predictive modeling of clinical trial completion using the HINTBasic and HINTPlus models. By integrating multimodal datasets, the models predict clinical trial phase success. It provides interpretability insights into the HINTPlus model's decision-making process. Retrieval-Augmented-Generation techniques were used to contextualize results. Our findings support informed decision-making, optimize resource allocation, and accelerate drug development in clinical trials. |
|---|---|
| Autores principais: | Hamrouni, Jasmin |
| Assunto: | Clinical trials Health care Artificial Intelligence Machine learning methods Predictive modelling Model interpretability Contextuality Retrieval-Augmented Generation |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1868414505405382656 |
|---|---|
| author | Hamrouni, Jasmin |
| author_facet | Hamrouni, Jasmin |
| author_role | author |
| contributor_name_str_mv | Han, Qiwei RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Hamrouni, Jasmin\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Han, Qiwei RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Hamrouni, Jasmin |
| datacite.date.Accepted.fl_str_mv | 2025-01-21T00:00:00Z |
| datacite.date.available.fl_str_mv | 2025-08-04T15:14:19Z |
| datacite.date.embargoed.fl_str_mv | 2025-08-04T15:14:19Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Clinical trials Health care Artificial Intelligence Machine learning methods Predictive modelling Model interpretability Contextuality Retrieval-Augmented Generation |
| datacite.titles.title.fl_str_mv | Predictive modelling for clinical trial completion: assessing the phase success - incorporating RAG techniques for predictive modelling of clinical trial outcomes |
| dc.contributor.none.fl_str_mv | Han, Qiwei RUN |
| dc.creator.none.fl_str_mv | Hamrouni, Jasmin |
| dc.date.Accepted.fl_str_mv | 2025-01-21T00:00:00Z |
| dc.date.available.fl_str_mv | 2025-08-04T15:14:19Z |
| dc.date.embargoed.fl_str_mv | 2025-08-04T15:14:19Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/186014 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Clinical trials Health care Artificial Intelligence Machine learning methods Predictive modelling Model interpretability Contextuality Retrieval-Augmented Generation |
| dc.title.fl_str_mv | Predictive modelling for clinical trial completion: assessing the phase success - incorporating RAG techniques for predictive modelling of clinical trial outcomes |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_bdcc |
| description | This study investigates predictive modeling of clinical trial completion using the HINTBasic and HINTPlus models. By integrating multimodal datasets, the models predict clinical trial phase success. It provides interpretability insights into the HINTPlus model's decision-making process. Retrieval-Augmented-Generation techniques were used to contextualize results. Our findings support informed decision-making, optimize resource allocation, and accelerate drug development in clinical trials. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/74878918-1144-406b-91e4-cdb355fe8b9d/download |
| id | run_32bfcd1c4bd81ac24ab54ce87fcf0dfe |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/186014 |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/186014 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Hamrouni, Jasmin |
| publishDate | 2025 |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engpt_PTThis study investigates predictive modeling of clinical trial completion using the HINTBasic and HINTPlus models. By integrating multimodal datasets, the models predict clinical trial phase success. It provides interpretability insights into the HINTPlus model's decision-making process. Retrieval-Augmented-Generation techniques were used to contextualize results. Our findings support informed decision-making, optimize resource allocation, and accelerate drug development in clinical trials.application/pdfpt_PTPredictive modelling for clinical trial completion: assessing the phase success - incorporating RAG techniques for predictive modelling of clinical trial outcomesHamrouni, JasminHan, QiweiHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptURNurn:tid:2039626992025-08-04T15:14:19Z2025-01-212024-12-172025-01-21T00:00:00ZHandlehttp://hdl.handle.net/10362/186014http://purl.org/coar/access_right/c_abf2open accessClinical trialsHealth careArtificial IntelligenceMachine learning methodsPredictive modellingModel interpretabilityContextuality Retrieval-Augmented Generation1647132 bytesliteraturehttp://purl.org/coar/resource_type/c_bdccmaster thesishttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/74878918-1144-406b-91e4-cdb355fe8b9d/download |
| spellingShingle | Predictive modelling for clinical trial completion: assessing the phase success - incorporating RAG techniques for predictive modelling of clinical trial outcomes Hamrouni, Jasmin Clinical trials Health care Artificial Intelligence Machine learning methods Predictive modelling Model interpretability Contextuality Retrieval-Augmented Generation |
| status | SINGLETON |
| subject.fl_str_mv | Clinical trials Health care Artificial Intelligence Machine learning methods Predictive modelling Model interpretability Contextuality Retrieval-Augmented Generation |
| title | Predictive modelling for clinical trial completion: assessing the phase success - incorporating RAG techniques for predictive modelling of clinical trial outcomes |
| title_full | Predictive modelling for clinical trial completion: assessing the phase success - incorporating RAG techniques for predictive modelling of clinical trial outcomes |
| title_fullStr | Predictive modelling for clinical trial completion: assessing the phase success - incorporating RAG techniques for predictive modelling of clinical trial outcomes |
| title_full_unstemmed | Predictive modelling for clinical trial completion: assessing the phase success - incorporating RAG techniques for predictive modelling of clinical trial outcomes |
| title_short | Predictive modelling for clinical trial completion: assessing the phase success - incorporating RAG techniques for predictive modelling of clinical trial outcomes |
| title_sort | Predictive modelling for clinical trial completion: assessing the phase success - incorporating RAG techniques for predictive modelling of clinical trial outcomes |
| topic | Clinical trials Health care Artificial Intelligence Machine learning methods Predictive modelling Model interpretability Contextuality Retrieval-Augmented Generation |
| topic_facet | Clinical trials Health care Artificial Intelligence Machine learning methods Predictive modelling Model interpretability Contextuality Retrieval-Augmented Generation |
| url | http://hdl.handle.net/10362/186014 |
| visible | 1 |