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. 2023 Oct 12:6:1237124.
doi: 10.3389/frai.2023.1237124. eCollection 2023.

Artificial intelligence-driven approach for patient-focused drug development

Prathamesh Karmalkar  1 , Harsha Gurulingappa  1 , Erica Spies  2 , Jennifer A Flynn  2
Affiliations

Artificial intelligence-driven approach for patient-focused drug development

Prathamesh Karmalkar et al. Front Artif Intell. .

Abstract

Patients' increasing digital participation provides an opportunity to pursue patient-centric research and drug development by understanding their needs. Social media has proven to be one of the most useful data sources when it comes to understanding a company's potential audience to drive more targeted impact. Navigating through an ocean of information is a tedious task where techniques such as artificial intelligence and text analytics have proven effective in identifying relevant posts for healthcare business questions. Here, we present an enterprise-ready, scalable solution demonstrating the feasibility and utility of social media-based patient experience data for use in research and development through capturing and assessing patient experiences and expectations on disease, treatment options, and unmet needs while creating a playbook for roll-out to other indications and therapeutic areas.

Keywords: artificial intelligence; natural language processing; patient experience; patient-focused drug development; social media; text analytics; unmet needs.

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Conflict of interest statement

PK and HG are current employees of Merck Data & AI Organization, Merck Group. ES and JF are current employees of EMD Serono Research & Development Institute, Inc.

Figures

Figure 1
Figure 1
Overview of AI-enabled solution.
Figure 2
Figure 2
Sample view of (A) symptom distribution and (B) top 10 symptoms by severity.
Figure 3
Figure 3
The model's confidence of the data with 30% noise after trained with fixed complementary labels. Green indicates the corrupted data; purple indicates the original clean data.
Figure 4
Figure 4
The accuracy of the data with 30% noise.
See this image and copyright information in PMC

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