Social Media Mining in Drug Development Decision Making: Prioritizing Multiple Sclerosis Patients’ Unmet Medical Needs

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dc.contributor.authorKoss, Jonathan
dc.contributor.authorBohnet-Joschko, Sabine
dc.date.accessioned2021-12-24T17:45:13Z
dc.date.available2021-12-24T17:45:13Z
dc.date.issued2022-01-04
dc.description.abstractPharmaceutical companies increasingly must consider patients’ needs in drug development. Since patients’ needs are often difficult to measure, especially in rare diseases, information in drug development decision-making is limited. In the proposed study, we employ the opportunity algorithm to identify and prioritize unmet medical needs of multiple sclerosis patients shared in social media posts. Using topic modeling and sentiment analysis features of the opportunity algorithm are generated. The result implies that sensory problems, pain, mental health problems, fatigue and sleep disturbances represent the highest unmet medical needs of the samples population. The present study suggests a promising potential of this method to provide relevant insights into rare disease populations to promote patient-centered drug development.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.368
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79702
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining and Machine Learning for Social Media
dc.subjectmachine learning
dc.subjectmultiple sclerosis
dc.subjectsentiment analysis
dc.subjectsocial media mining
dc.subjecttopic modeling
dc.titleSocial Media Mining in Drug Development Decision Making: Prioritizing Multiple Sclerosis Patients’ Unmet Medical Needs
dc.type.dcmitext

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