Zequan Liu, Jiawen Lyn, Wei Zhu, Xing Tian. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Background: Fraud, Waste, and Abuse (FWA) detection is a significant yet challenging problem in the health insurance industry. An essential step in FWA detection is to check whether the medication is clinically reasonable with respect to the diagnosis. Currently, human experts with sufficient medical knowledge are required to perform this task. To reduce the cost, insurance inspectors tend to build an intelligent system to detect suspicious claims with inappropriate diagnoses/medications automatically. Objective: The aim of this study was to develop an automated method for making use of a medical knowledge graph to identify clinically suspected claims for FWA detection. Methods: First, we identified the medical knowledge that is required to assess the clinical rationality of the claims. We then searched for data sources that contain information to build such knowledge. In this study, we focused on Chinese medical knowledge. Second, we constructed a medical knowledge graph using unstructured knowledge. We used a deep learning–based method to extract the entities and relationships from the knowledge sources and developed a multilevel similarity matching approach to conduct the entity linking. To guarantee the quality of the medical knowledge graph, we involved human experts to review the entity and relationships with lower confidence. These reviewed results could be used to further improve the machine-learning models. Finally, we developed the rules to identify the suspected claims by reasoning according to the medical knowledge graph. Results: We collected 185,796 drug labels from the China Food and Drug Administration, 3390 types of disease information from medical textbooks (eg, symptoms, diagnosis, treatment, and prognosis), and information from 5272 examinations as the knowledge sources. The final medical knowledge graph includes 1,616,549 nodes and 5,963,444 edges. We designed three knowledge graph reasoning rules to identify three kinds of inappropriate diagnosis/medications. The experimental results showed that the medical knowledge graph helps to detect 70% of the suspected claims. Conclusions: The medical knowledge graph–based method successfully identified suspected cases of FWA (such as fraud diagnosis, excess prescription, and irrational prescription) from the claim documents, which helped to improve the efficiency of claim processing.

Introduction: Cardiac troponin I (cTnI) has been regarded as a gold standard for early diagnosis and prognosis monitoring of acute myocardial infarction (AMI) in clinical practice. Owing to its low concentration in blood, accurate determination of cTnI often requires high sensitivity. However, current established point-of-care (POC) assays are insufficient to meet clinically analytical requirements due to their low sensitivity. Methods: To this end, we established a highly sensitive and reliable POC lateral flow strip based on lanthanide-doped nanoparticles (NPs) for cTnI determination in human blood samples. The capture of cTnI on the lateral flow strip was performed in a sandwich assay, where Eu³⁺-doped vanadate nanoparticles (GdVO₄:30% Eu NPs) were used as luminescent probes to allow quantification. Results: Our platform realized the analytical sensitivity enhancement with limit-of-detection (LOD) as low as 17 pg mL^{− 1} for cTnI detection, which was lower than the commercial counterpart; meanwhile, it displayed high specificity, excellent reproducibility and outstanding accuracy for analyzing clinical serum samples. Conclusion: Overall, this strategy provided an ultrasensitive, cost-effective and user-friendly platform for on-site cTnI detection, demonstrating the prospect of lanthanide-doped NPs-based POC diagnosis of disease-related biomarkers.

Given the significant potential of large language models (LLMs) in sequence modeling, emerging studies have begun applying them to time-series forecasting. Despite notable progress, existing methods still face two critical challenges: (1) their reliance on large amounts of paired text data, limiting the model applicability, and (2) a substantial modality gap between text and time series, leading to insufficient alignment and suboptimal performance. This paper introduces Hierarchical Text-Free Alignment (TS-HTFA) a novel method that leverages hierarchical alignment to fully exploit the representation capacity of LLMs for time-series analysis while eliminating the dependence on text data. Specifically, paired text data are replaced with adaptive virtual text based on QR decomposition word embeddings and learnable prompts. Furthermore, comprehensive cross-modal alignment is established at three levels: input, feature, and output, contributing to enhanced semantic symmetry between modalities. Extensive experiments on multiple time-series benchmarks demonstrate that TS-HTFA achieves state-of-the-art performance, significantly improving prediction accuracy and generalization.