Background: Integrated health data are foundational for secondary use, research, and policymaking. However, data quality issues—such as missing values and inconsistencies—are common due to the heterogeneity of health data sources. Existing frameworks often use static, 1-time assessments, which limit their ability to address quality issues across evolving data pipelines. Objective: This study evaluates the AIDAVA (artificial intelligence–powered data curation and validation) data quality framework, which introduces dynamic, life cycle–based validation of health data using knowledge graph technologies and SHACL (Shapes Constraint Language)–based rules. The framework is assessed for its ability to detect and manage data quality issues—specifically, completeness and consistency—during integration. Methods: Using the MIMIC-III (Medical Information Mart for Intensive Care-III) dataset, we simulated real-world data quality challenges by introducing structured noise, including missing values and logical inconsistencies. The data was transformed into source knowledge graphs and integrated into a unified personal health knowledge graph. SHACL validation rules were applied iteratively during the integration process, and data quality was assessed under varying noise levels and integration orders. Results: The AIDAVA framework effectively detected completeness and consistency issues across all scenarios. Completeness was shown to influence the interpretability of consistency scores, and domain-specific attributes (eg, diagnoses and procedures) were more sensitive to integration order and data gaps. Conclusions: AIDAVA supports dynamic, rule-based validation throughout the data life cycle. By addressing both dimension-specific vulnerabilities and cross-dimensional effects, it lays the groundwork for scalable, high-quality health data integration. Future work should explore deployment in live clinical settings and expand to additional quality dimensions.

S Mehryar. Proceedings of the 4th Table Representation Learning Workshop. 2025.

Data-driven AI models in clinical care increasingly rely on integrating data from heterogeneous sources to improve predictive performance. Traditional Electronic Health Record (EHR) systems however pose significant challenges for data integration due to the use of diverse standards and inconsistent semantics. Personalized Health Knowledge Graphs (PHKG), which leverage biomedical ontologies to encode and interconnect clinical knowledge, have emerged as a promising solution for unifying heterogeneous health data. Yet the process of PHKG integration can result in incomplete and ambiguous representations. Our primary objective is to apply advanced embedding techniques to mitigate the incompleteness problem in PHKGs. We propose a framework that combines schema-based semantic integration, domain-specific ontology alignment, and patient context representations through embedding techniques to improve incomplete and ambiguous PHKG representations. We further demonstrate the framework’s capabilities for producing enriched representations in a use case concerning cardiovascular outcome prediction by training machine learning models which utilize the learned embeddings. We embed EHR from a critical care unit as structured PHKGs mapped with two different schemas for comparison across knowledge completion and alignment tasks following a modular design. Each module is individually optimized using three baseline embedding methods with enhanced loss functions. We evaluate each task for both accuracy and semantic consistency and show the contribution of each module to the overall performance. We find and report settings for each module in which the proposed framework outperforms baseline results. The learned representations are subsequently used to generate patient contexts for the task of Heart Failure diagnosis as a use case. Our experiments demonstrate that semantically enhanced PHKG embeddings achieve better precision and recall scores compared to baseline models. Our proposed method addresses the challenges in generating heterogenuous Personalized Health Knowledge Graphs (PHKG) through a modular framework that integrates schema mapping, ontology alignment, and contextual patient embeddings. Results on real-world patient records support the potential for improved performance in clinical decision making. Particularly in scenarios where sparse and fragmented health records prove problematic for data driven applications, our method can provide a robust approach for the disambiguation and completion of coded information. Our implementation is available at: https://github.com/AIDAVA-DEV/kge-framework.

The prevalence of radio frequency signals in indoor environments has in recent years given rise to new technologies across many domains such as robotics, healthcare, and surveillance. Radio frequency signals propagate in the wireless medium through multiple paths and carry useful environment-dependent information. Capturing and analyzing these signal patterns can offer new solutions for a number of applications relevant to ranging, tracking, perception and recognition. In this work we propose a novel architecture, separating physical, back-bone networks, and inference layers, towards fully ubiquitous passive recognition systems that scale with the number of environments and applications. We propose a back-bone architecture that utilizes a novel Cross Dual-Path Attention (CDPA) block to capture spatial and temporal correlations from Channel State Information (CSI) for device-free, multi-task applications. Subsequently, a distill and transfer algorithm is proposed to generalize the inference capabilities of CDPA over multiple target environments for scalable training and reduced computational costs. By sharing knowledge between models across a shared network, experimentation shows that edge devices can be deployed with improved performance while simultaneously meeting strict computation and memory requirements. Our distributed learning paradigm demonstrates that CDPA-based models are capable of using passive signals in a non-intrusive and privacy-protecting manner, in order to achieve ubiquitous recognition at scale in smart environments.

BACKGROUND Integrated health data are foundational for secondary use, research, and policymaking. However, data quality issues—such as missing values and inconsistencies—are common due to the heterogeneity of health data sources. Existing frameworks often use static, 1-time assessments, which limit their ability to address quality issues across evolving data pipelines. OBJECTIVE This study evaluates the AIDAVA (artificial intelligence–powered data curation and validation) data quality framework, which introduces dynamic, life cycle–based validation of health data using knowledge graph technologies and SHACL (Shapes Constraint Language)–based rules. The framework is assessed for its ability to detect and manage data quality issues—specifically, completeness and consistency—during integration. METHODS Using the MIMIC-III (Medical Information Mart for Intensive Care-III) dataset, we simulated real-world data quality challenges by introducing structured noise, including missing values and logical inconsistencies. The data was transformed into source knowledge graphs and integrated into a unified personal health knowledge graph. SHACL validation rules were applied iteratively during the integration process, and data quality was assessed under varying noise levels and integration orders. RESULTS The AIDAVA framework effectively detected completeness and consistency issues across all scenarios. Completeness was shown to influence the interpretability of consistency scores, and domain-specific attributes (eg, diagnoses and procedures) were more sensitive to integration order and data gaps. CONCLUSIONS AIDAVA supports dynamic, rule-based validation throughout the data life cycle. By addressing both dimension-specific vulnerabilities and cross-dimensional effects, it lays the groundwork for scalable, high-quality health data integration. Future work should explore deployment in live clinical settings and expand to additional quality dimensions.