Resolution-Alignment-Completion of Tabular Electronic Health Records via Meta-Path Generative Sampling (2025)

Abstract: The increasing availability of electronic health records (EHR) offers significant opportunities in data-driven healthcare, yet much of this data remains fragmented, semantically inconsistent, or incomplete. These issues are particularly evident in tabular patient records where important contextual information are lacking from the input for effective modeling. In this work, we introduce a system that performs ontology-based entity alignment to resolve and complete tabular data used in real-world clinical units. We transform patient records into a knowledge graph and capture its hidden structures through graph embeddings. We further propose a meta-path sample generation approach for completing the missing information. Our experiments demonstrate the system’s ability to augment cardiovascular disease (CVD) data for lab event detection, diagnosis prediction, and drug recommendation, enabling more robust and precise predictive models in clinical decision-making.

## introduction the amount of data stored as electronic health records (ehr) in tabular format has grown significantly in recent years, now including an immense quantity of interactions, events and interconnected information. as such, data integration will play a transformative role in health information systems for the years to come, bridging the gap between research and applications. existing machine learning paradigms however cannot directly operate on relational data due to the complex structure of interconnected tables. domain specific algorithms therefore are in need for efficient and robust processing of tabular ehr for use in clinical decision making @xcite . in recent years, graph representation learning has been proposed as an approach for modeling relational data where rows become nodes, columns form node features, and primary-foreign key links establish edges. to learn their underlying structure, embedding models have been successfully applied to capture hidden hierarchies for downstream clinical tasks, such as comorbidity and readmission prediction @xcite . in @xcite , entity-level features are extracted and embedded via graph neural networks (gnn) for training a task-specific model by adopting a schema-less approach, modeling relational data as a heterogeneous graph. while schema-less design offers flexibility, it is less suited for integrating external knowledge sources due to the absence of a predefined structure @xcite . in contrast, a fixed schema can be imposed enabling seamless extension to external knowledge sources which exist in the form of clinical and biomedical ontologies. however, integrating these sources necessitates ontology alignment to resolve semantic ambiguities and maintain coherent representations. in @xcite , a graph representation learning approach is proposed that maps tabular data sources to a domain specific ontology in order to mitigate the presence of ambiguous information. these models continue to suffer from the inherent incompleteness, missing values, and inconsistent codification from legacy systems. in this work, we propose a robust resolutionalignment-completeness (rac) system for consolidating tabular ehr into semantically consistent health knowledge graphs, using standard terminologies aligned with medical ontologies. unlike prior schema-less, graph-based approaches, our fixed schema approach prioritizes structural integration and scalability for enhancing predictive performance by aligning domain-specific knowledge with relational data. our modular design consists of the following components: generate triples generate alignments pathway informed generative sampling extract bgp domain knowledge graph tabular data medial code 52702003 blood glucose 5.7 mmol/l augmented graph table with augmented values missing data constraint satisfaction domain knowledge table representation through resolution (r), alignment (a), and completeness (c) modules: ehr entities are mapped to basic graph patterns (bpg) of a reference schema, clinical codes are resolved and aligned to snomed ct, and meta-path sampling augments representations with missing and task-relevant knowledge. we use the mimic repository for experimentation which contains data associated with distinct hospital admissions concerning adult patients ad-mitted to critical care units @xcite . in order to map patient relational records, we use the swiss personal health network schema (sphn) foot_0 and a fine-tuned language model to process the input data. the resulting health knowledge graph is embedded using relational graph neural networks and aligned with the systematized nomenclature of medicine foot_1 (snomed) as domain knowledge graph. we test our framework for three different down-stream clinical tasks, namely lab event detection, diagnosis prediction, and drug recommendation. our experiments demonstrate the contributions from each component, namely semantic annotation, schema-based entity resolution and domain ontology alignment, to predictive performance using precision, recall, and f1 scores as classification metrics. ## method the meta-path sampling framework proposed for tabular ehr processing in this work, is shown in figure 1 . related entities from tables are extracted and mapped to the relevant parts represented by basic graph patterns (bpg) in a reference schema. rows (records) are assigned unique identifiers and instances of the corresponding class and column attributes are mapped to retrieved predicates as triples. clinical codes (e.g. icd 3 , etc) are assigned unique identifiers to resolve their semantically equivalent instances and aligned with a domain-specific ontology, namely snomed ct. lastly, the transformed records are embedded and enriched using meta-pathway informed sampling in order to augment their representations, including missing and domain knowledge, as described in the following subsections. knowledge represented through this system can ultimately be utilized to complete the input data in tabular format. ## experimentation in this section, extensive experiments are conducted and reported for evaluating the proposed framework towards aligning and completing tabular ehr records. we report on dataset pre-processing steps, semantic annotation accuracies, ontology alignment results, and predictive performance for ## conclusions in this work, a framework is proposed that transposes the electronic health records from real-world patients in tabular format with graphical representation using generative sampling. the representations are aligned with a domain specific ontology to further disambiguate and contextualize. a graph neural network that supports multi-relational entities is trained and meta-path sampling is applied to generate missing information according to upper-level ontological information. the generation process applied to tabular inputs related to cardiovascular disease, achieve precision, recall, and f1 scores in the ideal range for clinical data augmentation and decision making.