Daniel Fabbri

Cathy Shyr, Yan Hu, Rory J. Tinker et al.

Phenotyping is fundamental to rare disease diagnosis, but manual curation of structured phenotypes from clinical notes is labor-intensive and difficult to scale. Existing artificial intelligence approaches typically optimize individual components of phenotyping but do not operationalize the full clinical workflow of extracting features from clinical text, standardizing them to Human Phenotype Ontology (HPO) terms, and prioritizing diagnostically informative HPO terms. We developed RARE-PHENIX, an end-to-end AI framework for rare disease phenotyping that integrates large language model-based phenotype extraction, ontology-grounded standardization to HPO terms, and supervised ranking of diagnostically informative phenotypes. We trained RARE-PHENIX using data from 2,671 patients across 11 Undiagnosed Diseases Network clinical sites, and externally validated it on 16,357 real-world clinical notes from Vanderbilt University Medical Center. Using clinician-curated HPO terms as the gold standard, RARE-PHENIX consistently outperformed a state-of-the-art deep learning baseline (PhenoBERT) across ontology-based similarity and precision-recall-F1 metrics in end-to-end evaluation (i.e., ontology-based similarity of 0.70 vs. 0.58). Ablation analyses demonstrated performance improvements with the addition of each module in RARE-PHENIX (extraction, standardization, and prioritization), supporting the value of modeling the full clinical phenotyping workflow. By modeling phenotyping as a clinically aligned workflow rather than a single extraction task, RARE-PHENIX provides structured, ranked phenotypes that are more concordant with clinician curation and has the potential to support human-in-the-loop rare disease diagnosis in real-world settings.

Chao Yan, Haifeng Xu, Yevgeniy Vorobeychik et al.

Routine operational use of sensitive data is often governed by law and regulation. For instance, in the medical domain, there are various statues at the state and federal level that dictate who is permitted to work with patients' records and under what conditions. To screen for potential privacy breaches, logging systems are usually deployed to trigger alerts whenever suspicious access is detected. However, such mechanisms are often inefficient because 1) the vast majority of triggered alerts are false positives, 2) small budgets make it unlikely that a real attack will be detected, and 3) attackers can behave strategically, such that traditional auditing mechanisms cannot easily catch them. To improve efficiency, information systems may invoke signaling, so that whenever a suspicious access request occurs, the system can, in real time, warn the user that the access may be audited. Then, at the close of a finite period, a selected subset of suspicious accesses are audited. This gives rise to an online problem in which one needs to determine 1) whether a warning should be triggered and 2) the likelihood that the data request event will be audited. In this paper, we formalize this auditing problem as a Signaling Audit Game (SAG), in which we model the interactions between an auditor and an attacker in the context of signaling and the usability cost is represented as a factor of the auditor's payoff. We study the properties of its Stackelberg equilibria and develop a scalable approach to compute its solution. We show that a strategic presentation of warnings adds value in that SAGs realize significantly higher utility for the auditor than systems without signaling. We illustrate the value of the proposed auditing model and the consistency of its advantages over existing baseline methods.

Chao Yan, Bo Li, Yevgeniy Vorobeychik et al.

For enhancing the privacy protections of databases, where the increasing amount of detailed personal data is stored and processed, multiple mechanisms have been developed, such as audit logging and alert triggers, which notify administrators about suspicious activities; however, the two main limitations in common are: 1) the volume of such alerts is often substantially greater than the capabilities of resource-constrained organizations, and 2) strategic attackers may disguise their actions or carefully choosing which records they touch, making incompetent the statistical detection models. For solving them, we introduce a novel approach to database auditing that explicitly accounts for adversarial behavior by 1) prioritizing the order in which types of alerts are investigated and 2) providing an upper bound on how much resource to allocate for each type. We model the interaction between a database auditor and potential attackers as a Stackelberg game in which the auditor chooses an auditing policy and attackers choose which records to target. A corresponding approach combining linear programming, column generation, and heuristic search is proposed to derive an auditing policy. For testing the policy-searching performance, a publicly available credit card application dataset are adopted, on which it shows that our methods produce high-quality mixed strategies as database audit policies, and our general approach significantly outperforms non-game-theoretic baselines.