  Medical toxicology is the clinical specialty that treats the toxic effects of
substances, be it an overdose, a medication error, or a scorpion sting. The
volume of toxicological knowledge and research has, as with other medical
specialties, outstripped the ability of the individual clinician to entirely
master and stay current with it. The application of machine learning techniques
to medical toxicology is challenging because initial treatment decisions are
often based on a few pieces of textual data and rely heavily on prior
knowledge. ML techniques often do not represent knowledge in a way that is
transparent for the physician, raising barriers to usability. Rule-based
systems and decision tree learning are more transparent approaches, but often
generalize poorly and require expert curation to implement and maintain. Here,
we construct a probabilistic logic network to represent a portion of the
knowledge base of a medical toxicologist. Our approach transparently mimics the
knowledge representation and clinical decision-making of practicing clinicians.
The software, dubbed Tak, performs comparably to humans on straightforward
cases and intermediate difficulty cases, but is outperformed by humans on
challenging clinical cases. Tak outperforms a decision tree classifier at all
levels of difficulty. Probabilistic logic provides one form of explainable
artificial intelligence that may be more acceptable for use in healthcare, if
it can achieve acceptable levels of performance.
