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

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dc.contributor.author Koss, Jonathan
dc.contributor.author Bohnet-Joschko, Sabine
dc.date.accessioned 2021-12-24T17:45:13Z
dc.date.available 2021-12-24T17:45:13Z
dc.date.issued 2022-01-04
dc.description.abstract Pharmaceutical 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.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.368
dc.identifier.isbn 978-0-9981331-5-7
dc.identifier.uri http://hdl.handle.net/10125/79702
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Analytics, Data Mining and Machine Learning for Social Media
dc.subject machine learning
dc.subject multiple sclerosis
dc.subject sentiment analysis
dc.subject social media mining
dc.subject topic modeling
dc.title Social Media Mining in Drug Development Decision Making: Prioritizing Multiple Sclerosis Patients’ Unmet Medical Needs
dc.type.dcmi text
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