Vector Database Dataset
Abstract
Dataset generated from the following paper. AbstractBackground Strict privacy regulations and institutional barriers have created persistent medical data silos, limiting the development and deployment of artificial intelligence (AI) models across healthcare institutions. Although federated learning provides a potential solution, it still requires access to decentralized patient-level data and faces challenges related to interoperability, computational complexity, and institutional governance. We investigated whether a private-domain clinical vector database could enhance deep learning-based outcome prediction without exchanging raw patient data. Methods We retrospectively collected 10 years of real-world electronic health records (EHRs) from more than 40,000 cardiovascular inpatients at a tertiary hospital in Shanghai, China. The clinical cohort included patients hospitalized with cardiovascular diseases, with outcomes defined as in-hospital mortality and readmission events. Clinical text records were transformed into high-dimensional representations using a locally constructed Word2Vec-based cardiovascular clinical vector database. We developed and compared two modeling pipelines: (1) conventional pretrained language models using clinical text representations alone, and (2) hybrid models integrating pretrained language models with private-domain vector embeddings through embedding fusion. 20% of patients were independently reserved as a test cohort. Model performance was…
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