Classifying Patient Conditions Based on Drug Reviews Using Natural Language Processing

The exponential growth of clinical data in electronic health records (EHRs) has posed significant challenges for health care professionals in delivering accurate and timely patient-centered care. In particular, drug recommendation systems have become essential for assisting clinicians in prescribing appropriate medications, especially for patients with multiple comorbidities such as diabetes, hypertension, and cardiovascular diseases. Traditional manual recommendations by physicians often struggle to address the complexity of such cases. This study investigates both collaborative filtering techniques and tra-ditional machine-learning classifiers for drug recommendations. The early identification of a patient’s condition is captious for effective medical intervention and improved healthcare outcomes. This analysis influences Machine Learning(ML) and Natural Language Processing(NLP) to develop a system that classifies patient conditions using textual drug reviews. By analyzing the sentiments and patterns in these reviews, the system aims to reduce the work load of medical professionals and provide early insights into patient conditions. In parallel, the rise of healthcare-focused social media platforms has opened new avenues for analyzing patient reviews and identifying adverse drug reactions (ADRs). Mining medical content from these platforms aids in pharmacovigilance, medication repurposing, and enhancing healthcare outcomes. This study explores ADR detection and classification harnessing machine-learning algorithms, involving Passive Aggressive Classifiers, Nïive Bayes, and Deep Neural Networks (DNN). Preprocessing clinical data and integrating clinical vector spaces significantly improved classification accuracy, with DNN out performing other models. This research highlights the potential of NLP-driven approaches in classifying patient conditions based on drug reviews, bridging gaps in clinical decision support systems and pharmacovigilance frameworks.