Darth Vecdor: An Open-Source System for Generating Knowledge Graphs Through Large Language Model Queries

Many large language models (LLMs) are trained on a massive body of knowledge present on the Internet. Darth Vecdor (DV) was designed to extract this knowledge into a structured, terminology-mapped, SQL database ("knowledge base" or "knowledge graph"). Knowledge graphs may be useful in many domains, including healthcare. Although one might query an LLM directly rather than a SQL-based knowledge graph, concerns such as cost, speed, safety, and confidence may arise, especially in high-volume operations. These may be mitigated when the information is pre-extracted from the LLM and becomes query-able through a standard database. However, the author found the need to address several issues. These included erroneous, off-topic, free-text, overly general, and inconsistent LLM responses, as well as allowing for multi-element responses. DV was built with features intended to mitigate these issues. To facilitate ease of use, and to allow for prompt engineering by those with domain expertise but little technical background, DV provides a simple, browser-based graphical user interface. DV has been released as free, open-source, extensible software, on an "as is" basis, without warranties or conditions of any kind, either express or implied. Users need to be cognizant of the potential risks and benefits of using DV and its outputs, and users are responsible for ensuring any use is safe and effective. DV should be assumed to have bugs, potentially very serious ones. However, the author hopes that appropriate use of current and future versions of DV and its outputs can help improve healthcare.

Novel Techniques to Assess Predictive Systems and Reduce Their Alarm Burden

The performance of a binary classifier ("predictor") depends heavily upon the context ("workflow") in which it operates. Classic measures of predictor performance do not reflect the realized utility of predictors unless certain implied workflow assumptions are met. Failure to meet these implied assumptions results in suboptimal classifier implementations and a mismatch between predicted or assessed performance and the actual performance obtained in real-world deployments. The mismatch commonly arises when multiple predictions can be made for the same event, the event is relatively rare, and redundant true positive predictions for the same event add little value, e.g., a system that makes a prediction each minute, repeatedly issuing interruptive alarms for a predicted event that may never occur. We explain why classic metrics do not correctly represent the performance of predictors in such contexts, and introduce an improved performance assessment technique ("u-metrics") using utility functions to score each prediction. U-metrics explicitly account for variability in prediction utility arising from temporal relationships. Compared to traditional performance measures, u-metrics more accurately reflect the real-world benefits and costs of a predictor operating in a workflow context. The difference can be significant. We also describe the use of "snoozing," a method whereby predictions are suppressed for a period of time, commonly improving predictor performance by reducing false positives while retaining the capture of events. Snoozing is especially useful when predictors generate interruptive alerts, as so often happens in clinical practice. Utility-based performance metrics correctly predict and track the performance benefits of snoozing, whereas traditional performance metrics do not.