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PubMed2026

The Progress of Gout Prediction Models Based on Multi-source Data.

INTRODUCTION: Gout, a highly serious inflammatory disease that is caused by monosodium urate crystals, is becoming an increasingly significant health concern. Artificial Intelligence and multi-omics-based research have made significant gains for the early detection and prevention of gout based on diverse approaches. This review intends to summarize current advances in forecasting gout susceptibility and gout-related symptoms, evaluate the predictive efficacy of different features, and ascertain which clinical and omics characteristics are most effective in these prediction models. METHODS: We explored the PubMed database after 2010 using keywords such as "gout", "predictive model", "risk prediction", and "machine learning", and confined our search to Englishlanguage articles. The original peer-reviewed research articles that developed gout models were selected. Research that was not original or lacked internal validation was excluded. RESULTS: Clinical features, genomics, microbiomics, radiomics, and metabolomics have been utilized to construct models related to gout and have demonstrated excellent predictive performance. Multisource data prediction models usually exhibit better effectiveness. DISCUSSION: Gout-oriented models performed excellently in predictive performance but present limitations in certain clinical and omics domains. However, if they are to affect actual patient care, they must overcome some external confirmation roadblocks and the fiscal and practical implications they will face ahead of time. CONCLUSION: This review indicates that clinical and multi-omics models of gout are significant instruments for clinical decision-making. The models constructed in these studies may be crucial for the treatment of gout and its practical benefits.

Gout↗
PubMed1991

Best kept secrets ... Source Data Systems, Inc. (SDS).

The SDS/MEDNET system is a cost-effective option for small- to medium-size hospitals (up to 400 beds). The parameter-driven system lets users control operations with only occasional SDS assistance. A full application set, available for modular selection to reduce upfront costs while facilitating steady growth and protecting client investment, is adaptable to multi-facility environments. The industry-standard, Intel-based multi-user processors, network communications and protocols assure high efficiency, low-cost solutions independent of any one hardware vendor. Sustained growth in both client base and product offerings point to a high level of responsiveness and healthcare industry commitment. Corporate emphasis on user involvement and open systems integration assures clients of leading-edge capabilities. SDS/MEDNET will be a strong contender in selected marketing environments.

Computer Communication Networks↗
PubMed1999

Using external data sources to improve audit trail analysis.

Audit trail analysis is the primary means of detection of inappropriate use of the medical record. While audit logs contain large amounts of information, the information required to determine useful user-patient relationships is often not present. Adequate information isn't present because most audit trail analysis systems rely on the limited information available within the medical record system. We report a feature of the STAR (System for Text Archive and Retrieval) audit analysis system where information available in the medical record is augmented with external information sources such as: database sources, Light-weight Directory Access Protocol (LDAP) server sources, and World Wide Web (WWW) database sources. We discuss several issues that arise when combining the information from each of these disparate information sources. Furthermore, we explain how the enhanced person specific information obtained can be used to determine user-patient relationships that might signify a motive for inappropriately accessing a patient's medical record.

Computer Security↗