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Intelligent User Interface for Decision Support in Drug Development

Published: 27 March 2023 Publication History

Abstract

Drug development is a complicated, costly, and lengthy process spanning multiple stages, which each may take multiple years. Only a few drugs reach or pass the clinical trial stage because high failure rates are common in the pharmaceutical industry. This work has been carried out in an applied research setting. Funded by industry, the work has strong requirements to develop working prototypes alongside scientists from the industry that guided the process. The goal of this project is to investigate support tools with computer intelligence methods that enable to shorten the amount of time needed during the pre-clinical stages of drug development and to reduce the associated experiment costs. As the result, three prototypes are developed addressing diverse requirements that arise at different stages of drug development in different units of an organization. To this end, three promising sub-problems were identified in a combined effort with industry experts: Drug formulation, Solvent selection, and Bio pharmaceutical literature research.
For each task an interactive tool is proposed which includes novel intelligent visualizations designed to directly or indirectly reduce the amount of time invested in the associated tasks. Each tool has been developed in close cooperation with industry experts over the last three years. The connection between these prototypes is the interactive presentation of information derived from a computer-based intelligent process
The next steps of the Ph.D. project are to assess the usability and effectiveness of each tool.

References

[1]
Melissa Birch, Steven J. Fussell, Paul D. Higginson, Neil McDowall, and Ivan Marziano. 2005. Towards a PAT-Based Strategy for Crystallization Development. Organic Process Research and Development 9, 3 (2005), 360–364. https://doi.org/10.1021/op0500077
[2]
John Brooke. 1995. SUS: A quick and dirty usability scale. Usability Eval. Ind. 189 (11 1995).
[3]
Maarten Grootendorst. 2022. BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794(2022).

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Published In

cover image ACM Conferences
IUI '23 Companion: Companion Proceedings of the 28th International Conference on Intelligent User Interfaces
March 2023
266 pages
ISBN:9798400701078
DOI:10.1145/3581754
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 March 2023

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Author Tags

  1. and recommendation system
  2. applications of intelligent user interfaces
  3. collaborative interfaces
  4. evaluations of intelligent user interfaces - reproducibility
  5. information retrieval
  6. interactive machine learning
  7. knowledge-based approaches to user interface design and generation
  8. search

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  • Invited-talk
  • Research
  • Refereed limited

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  • Know-Center
  • FFG

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IUI '23
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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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