Analysis of Voluntarily Reported Data Post Mesh Implantation for Detecting Public Emotion and Identifying Concern Reports

2025-09-03

AI Summary

This study addresses the dynamic evolution of patient-reported emotions following hernia mesh implantation. Method: Leveraging unstructured narrative texts from the FDA’s Manufacturer and User Facility Device Experience (MAUDE) database (2000–2021), we integrated the NRC Emotion Lexicon with TextBlob for fine-grained sentiment polarity analysis and applied time-series modeling to detect longitudinal emotional trends. Contribution/Results: We introduce the novel concept of “Concern Reports” to quantify emotion-laden adverse event narratives. Two distinct peaks in emotional intensity were identified—in 2011–2012 and 2017–2018—temporally aligned with major regulatory actions (e.g., FDA safety communications) and technological shifts in mesh design. This approach establishes a new paradigm for quantifying real-world patient experience, enabling data-driven optimization of postoperative care, enhancement of preoperative informed consent processes, identification of psychological intervention targets, and advancement of patient-centered medical device safety surveillance systems.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Mesh implants are widely utilized in hernia repair surgeries, but postoperative complications present a significant concern. This study analyzes patient reports from the Manufacturer and User Facility Device Experience (MAUDE) database spanning 2000 to 2021 to investigate the emotional aspects of patients following mesh implantation using Natural Language Processing (NLP). Employing the National Research Council Canada (NRC) Emotion Lexicon and TextBlob for sentiment analysis, the research categorizes patient narratives into eight emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and assesses sentiment polarity. The goal is to discern patterns in patient sentiment over time and to identify reports signaling urgent concerns, referred to as "Concern Reports," thereby understanding shifts in patient experiences in relation to changes in medical device regulation and technological advancements in healthcare. The study detected an increase in Concern Reports and higher emotional intensity during the periods of 2011-2012 and 2017-2018. Through temporal analysis of Concern Reports and overall sentiment, this research provides valuable insights for healthcare practitioners, enhancing their understanding of patient experiences post-surgery, which is critical for improving preoperative counselling, postoperative care, and preparing patients for mesh implant surgeries. The study underscores the importance of emotional considerations in medical practices and the potential for sentiment analysis to inform and enhance patient care.

Problem

• Analyzing patient emotions post-mesh implantation using NLP
• Identifying urgent concern reports from medical device database
• Tracking sentiment changes related to regulatory and technological shifts

Innovation

• NLP sentiment analysis on patient reports
• NRC Emotion Lexicon for emotion categorization
• Temporal analysis of concern report patterns