Patient Sentiment Analysis with AI | Introduction to generative ai google | Generative ai course google | Traditional ai examples | Turtles AI

The study develops an advanced AI model to analyze patient sentiment in drug reviews. It combines machine and deep learning techniques, improving accuracy through clinical word embeddings and providing explainability with LIME.

Key Points:

• Developing an advanced AI model to analyze patient sentiment in drug reviews.
• Integrating machine and deep learning models to improve predictions.
• Using clinical word embedding to optimize model performance.
• Applying the LIME technique to explain and make model decisions interpretable.

The development of advanced AI models to analyze patient sentiments in drug reviews represents a significant innovation in the field of healthcare. The goal of this work is to create tools that support physicians in making informed decisions about treatments, improving the quality of care and optimizing decision-making processes. The developed model focuses on the analysis of sentiment in patient reviews about drugs, an aspect that can provide useful information on patient satisfaction and side effects associated with medicines. Sentiment analysis can, in fact, represent a fundamental aid for healthcare professionals, who can use this information to personalize therapies and improve the overall patient experience. The developed model uses an advanced approach, using an ensemble of deep learning models (DL_ENS) that integrate pre-trained representations of word embeddings, specifically in the medical domain, such as those derived from PubMed and PMC. These word embeddings, compared to general models, have been shown to significantly improve prediction accuracy, contributing to a better interpretation of the sentiments expressed in reviews.

The model was tested on three scenarios of classification of reviews based on drug scores, dividing sentiments into different categories, such as negative, neutral and positive. The results obtained were excellent, with an accuracy and F1 score above 92% in the scenarios with two and three classes, and good results even in the case of ten classes. The adoption of advanced ensemble learning and deep learning techniques allowed to obtain superior performances compared to previous models, opening new possibilities for the automatic analysis of drug reviews. Furthermore, one of the main innovations of this work was the introduction of model explainability, a fundamental component in the clinical field. The application of the LIME (Local Interpretable Model-agnostic Explanations) technique allowed to provide interpretable explanations of the model’s decisions, making the prediction process transparent and increasing the confidence of doctors in using the system. In clinical contexts, where understanding the decisions taken by an AI model is essential, the introduction of explainability techniques is an important step for the integration of AI models into medical workflows.

The data used in the study comes from a large collection of drug reviews, with over 200,000 reviews from drugs.com. This large and diverse dataset allowed the model to be trained robustly and tested for its ability to generalise across a variety of reviews. However, there are limitations. One significant issue encountered is the imbalance of the dataset, with an uneven distribution across sentiment classes, which could negatively impact the accuracy of the models. Additionally, the DL_ENS model, while performing well, is computationally demanding, which may limit its real-time application, an aspect that the authors plan to improve in the future. Another important aspect is external validation: the developed models could benefit from testing on external datasets to verify their generalizability and robustness in different contexts.

Looking ahead, the authors plan to continue developing even more advanced models that can further increase the accuracy of their predictions. Furthermore, we intend to explore multimodal approaches, which integrate other types of data, such as medical images or textual information from other sources, to further improve the ability to predict patients’ feelings.

The result of this study therefore opens the way to new and promising applications of AI in clinical decision support, with the aim of improving patient care and optimizing pharmacological treatments.