Review of Medical Decision Support and Machine-Learning Methods.

Machine-learning methods can assist with the medical decision-making processes at the both the clinical and diagnostic levels. In this article, we first review historical milestones and specific applications of computer-based medical decision support tools in both veterinary and human medicine. Next, we take a mechanistic look at 3 archetypal learning algorithms-naive Bayes, decision trees, and neural network-commonly used to power these medical decision support tools.

Duration of β-adrenoceptor blockade associated with once-daily oral administration of atenolol in healthy dogs.

OBJECTIVE To test the hypothesis that once-daily oral administration of atenolol would attenuate the heart rate response to isoproterenol for 24 hours. ANIMALS 20 healthy dogs. PROCEDURES A double-blind randomized placebo-controlled crossover study was conducted.

Identifying free-text features to improve automated classification of structured histopathology reports for feline small intestinal disease.

The histologic evaluation of gastrointestinal (GI) biopsies is the standard for diagnosis of a variety of GI diseases (e.g., inflammatory bowel disease [IBD] and alimentary lymphoma [ALA]).

A review of medical terminology standards and structured reporting.

Much effort has been invested in standardizing medical terminology for representation of medical knowledge, storage in electronic medical records, retrieval, reuse for evidence-based decision making, and for efficient messaging between users. We only focus on those efforts related to the representation of clinical medical knowledge required for capturing diagnoses and findings from a wide range of general to specialty clinical perspectives (e.g.

Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats.

Inflammatory bowel disease (IBD) and alimentary lymphoma (ALA) are common gastrointestinal diseases in cats. The very similar clinical signs and histopathologic features of these diseases make the distinction between them diagnostically challenging. We tested the use of supervised machine-learning algorithms to differentiate between the 2 diseases using data generated from noninvasive diagnostic tests.

Development and evaluation of methods for structured recording of heart murmur findings using SNOMED-CT post-coordination.

Objective: This study evaluated an existing SNOMED-CT model for structured recording of heart murmur findings and compared it to a concept-dependent attributes model using content from SNOMED-CT.

Methods: The authors developed a model for recording heart murmur findings as an alternative to SNOMED-CT's use of Interprets and Has interpretation. A micro-nomenclature was then created to support each model using subset and extension mechanisms described for SNOMED-CT.