Assessing Open-Ended Human-Computer Collaboration Systems: Applying a Hallmarks Approach.

There is a growing desire to create computer systems that can collaborate with humans on complex, open-ended activities. These activities typically have no set completion criteria and frequently involve multimodal communication, extensive world knowledge, creativity, and building structures or compositions through multiple steps. Because these systems differ from question and answer (Q&A) systems, chatbots, and simple task-oriented assistants, new methods for evaluating such collaborative computer systems are needed.

ADE Eval: An Evaluation of Text Processing Systems for Adverse Event Extraction from Drug Labels for Pharmacovigilance.

Introduction: The US FDA is interested in a tool that would enable pharmacovigilance safety evaluators to automate the identification of adverse drug events (ADEs) mentioned in FDA prescribing information. The MITRE Corporation (MITRE) and the FDA organized a shared task-Adverse Drug Event Evaluation (ADE Eval)-to determine whether the performance of algorithms currently used for natural language processing (NLP) might be good enough for real-world use.

Objective: ADE Eval was conducted to evaluate a range of NLP techniques for identifying ADEs mentioned in publicly available FDA-approved drug labels (package inserts).

Resilience of clinical text de-identified with "hiding in plain sight" to hostile reidentification attacks by human readers.

Objective: Effective, scalable de-identification of personally identifying information (PII) for information-rich clinical text is critical to support secondary use, but no method is 100% effective. The hiding-in-plain-sight (HIPS) approach attempts to solve this "residual PII problem." HIPS replaces PII tagged by a de-identification system with realistic but fictitious (resynthesized) content, making it harder to detect remaining unredacted PII.

The machine giveth and the machine taketh away: a parrot attack on clinical text deidentified with hiding in plain sight.

Objective: Clinical corpora can be deidentified using a combination of machine-learned automated taggers and hiding in plain sight (HIPS) resynthesis. The latter replaces detected personally identifiable information (PII) with random surrogates, allowing leaked PII to blend in or "hide in plain sight." We evaluated the extent to which a malicious attacker could expose leaked PII in such a corpus.

An annotation and modeling schema for prescription regimens.

Background: We introduce TranScriptML, a semantic representation schema for prescription regimens allowing various properties of prescriptions (e.g. dose, frequency, route) to be specified separately and applied (manually or automatically) as annotations to patient instructions.