PhenoAlign: A Hybrid Data-Knowledge-Driven Approach for Precisely Aligning Phenotype Information in Medical Texts

Precisely aligning phenotypic information within medical texts is paramount in advancing intelligent medical applications, such as similar patient case retrieval. However, despite its criticality, an algorithm specifically designed for this task is lacking. We previously introduced a fine-grained semantic information model, the semantic structured unit of phenotypes (PhenoSSU), and an automatic extraction algorithm. This model accurately characterizes and extracts phenotypic information from medical texts. In this study, we explore different PhenoSSU alignment strategies. The results show that the data-knowledge-driven approach best aligns PhenoSSUs. Specifically, employing a BERT-based pre-trained language model (PLMs) to align phrase-type PhenoSSUs and a knowledge-based method for logic-type PhenoSSUs demonstrates efficacy. Moreover, by successfully integrating the PhenoSSU alignment and extraction algorithms, we have developed PhenoAlign, a novel medical text phenotype alignment tool. This tool facilitates precisely aligning phenotypic information by processing two medical texts, generating accurate alignment outcomes. PhenoAlign exhibited satisfactory medical text phenotypic alignment using the expert annotated gold standard test set, with an end-to-end F1 score of 0.820. The F1 scores for phenoSSU extraction and alignment were 0.885 and 0.927, respectively. Our analysis extends to the potential application of ChatGPT in phenotype alignment tasks, and find significant challenges encountered by large language models in this domain. We developed a simple, effective tool for medical text phenotype information alignment. This tool will be valuable to intelligent medical applications, facilitating patient care and medical research advancements.