## 2 Literature Review
Excessive system alerts and poor targeting still hinder the adoption of CDSS tools--nearly half of the alerts in outpatient care are ignored or overridden by clinicians [17]. That is why intelligent systems that can have a computational understanding of medical context are crucial. AI-enhanced CDSS prototypes that incorporate interaction polarity reduced override rates to 28%, supporting the clinical relevance of polarity-aware classifiers. In the medical field, it is especially important to understand how decisions are made. That is why explainability should be a core element in CDSS. [PERSON] et al. [18] proposed a heterogeneous attention network for drug-drug interaction prediction (HAN-DDI), a heterogeneous graph attention network trained on biomedical interaction graphs consisting of drugs, targets, enzymes, and side effects. The model achieved high performance in DDI prediction, reaching an F1 score of 95.18% for existing drugs and 82.87% for novel drugs, demonstrating strong generalization. However, the system depends on structured biomedical triples and does not operate directly on unstructured clinical narratives, limiting its utility in NLP-based settings. Unlike previous models that relied on structured biomedical triples or omics data, our LoRA-BiomedBERT model operates directly on free-text interaction statements, making it more suitable for NLP-based clinical decision systems where such unstructured data is prevalent. Despite recent advances, many DDI classification models either demand extensive experimental inputs or fail to process real-world textual descriptions found in clinical databases. This leaves academic prototypes apart from accessible tools. Our work seeks to close this gap by creating a lightweight, polarity-aware model capable of direct DDIs straight from raw narrative data classification. Another purpose of the research was to offer a scalable solution for real-time decision assistance in healthcare situations where structured data is often limited. The suggested technique balances accuracy against practicality. Table 1 summarizes representative approaches in DDI prediction, highlighting the input types, architectures, and whether polarity awareness was supported. Most prior works focused on interaction presence rather than directional classification, which this study addresses explicitly. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Method/Architecture** & **Input/Data Type** & **Limitations** \\ \hline [PERSON] et al. [11] & Heterogeneous GAT & Chemical, pathway, and gene-expression edges & Requires structured omics data not available at prescribing time \\ [PERSON] et al. [12] & SynGNet (GAT for synergy) & DrugComb synergy labels, cell-line viability, chemical descriptors & High performance, but depends on dose–response curves and experimental features \\ [PERSON] et al. [14] & Domain-specific LMs (BioBERT, PubMedBERT) & PubMed text corpora, fine-tuned on relation extraction & Strong generalization, but not polarity-aware and lacks directional prediction \\ [PERSON] and [PERSON] [10] & Rule-based similarity model & Tanimoto similarity thresholds applied to drug fingerprints & Captures structural similarity, but recall is limited; no linguistic inference \\ This work & LoRA-tuned BiomedBERT & Natural-language interaction descriptions & Trained on polarity labels (synergy vs. antagonism); limited to sentence-level cues \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of key models discussed in the literature review, including their primary features and limitations regarding polarity classification.