Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives—a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA)

Lustgarten, Jonathan Lyle; Zehnder, Ashley; Shipman, L. W.; Gancher, Elizabeth; Webb, Tracy L.

doi:10.1093/jamiaopen/ooaa005

Cited by 29 publications

(17 citation statements)

References 153 publications

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“…15 out of 30 veterinary schools across the United States have formed partnerships with their respective medical schools in order to perform cross-species translational research within the Clinical and Translational Science Award One Health Alliance (COHA, [29]). Of these schools, only two have active programs to assign disease codes to their medical records.…”

Section: A Primer On Automatic Text Classificationmentioning

confidence: 99%

FasTag: Automatic text classification of unstructured medical narratives

et al. 2020

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Unstructured clinical narratives are continuously being recorded as part of delivery of care in electronic health records, and dedicated tagging staff spend considerable effort manually assigning clinical codes for billing purposes. Despite these efforts, however, label availability and accuracy are both suboptimal. In this retrospective study, we aimed to automate the assignment of top-level International Classification of Diseases version 9 (ICD-9) codes to clinical records from human and veterinary data stores using minimal manual labor and feature curation. Automating top-level annotations could in turn enable rapid cohort identification, especially in a veterinary setting. To this end, we trained long short-term memory (LSTM) recurrent neural networks (RNNs) on 52,722 human and 89,591 veterinary records. We investigated the accuracy of both separate-domain and combined-domain models and probed model portability. We established relevant baseline classification performances by training Decision Trees (DT) and Random Forests (RF). We also investigated whether transforming the data using MetaMap Lite, a clinical natural language processing tool, affected classification performance. We showed that the LSTM-RNNs accurately classify veterinary and human text narratives into top-level categories with an average weighted macro F1 score of 0.74 and 0.68 respectively. In the "neoplasia" category, the model trained on veterinary data had a high validation accuracy in veterinary data and moderate accuracy in human data, with F1 scores of 0.91 and 0.70 respectively. Our LSTM method scored slightly higher than that of the DT and RF models. The use of LSTM-RNN models represents a scalable structure that could prove useful in cohort identification for comparative oncology studies. Digitization of human and veterinary health information will continue to be a reality, particularly in the form of unstructured narratives. Our approach is a step forward for these two domains to learn from and inform one another.

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Section: A Primer On Automatic Text Classificationmentioning

confidence: 99%

FasTag: Automatic text classification of unstructured medical narratives

et al. 2020

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“…However, siloed data between different academic and corporate institutions and inconsistent data formats often make data sharing difficult and therefore remains an open area of research and innovation. 95 A solution to this problem is developing a culture of data sharing among different institutions, potentially utilizing common data models like the OHDSI (Observational Health Data Sciences and Informatics) data sharing initiative. OHDSI produces tools like the OMOP (Observational Medical Outcomes Partnership) Common Data Model, which transforms data within disparate observational databases into a common representation (terminologies, vocabularies, and coding schemes) so that they can be analyzed using standardized analytics tools.…”

Section: Discussionmentioning

confidence: 99%

Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review

Kumar

Schindler

et al. 2021

JAMIA Open

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Objective Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. Materials and Methods We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus. Results There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values). Discussion Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research.

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“…These systems also collect and provide large amounts of data about each animal, as well as the herd, to the producer and veterinarian. Remote communication technologies have facilitated new models of healthcare delivery, such as LiveDVM 21 and Fuzzy 22 , which incorporate aspects of telehealth. And finally, already-existing data in traditional 5) Competency framework development usually requires 12 h. For this project, the meetings were split into 4 × 3 h meetings.…”

Section: Objective 1: Develop Competencies Relevant To New Veterinarians Regarding Health Informaticsmentioning

confidence: 99%

“…In parallel with developments in medical education, calls for competency-based education in veterinary medicine began as early as 1989 and continue today in order to adapt to the changing landscape of veterinary practice (1, 2,9,[11][12][13][14][15]. The emergence of informatics is a significant recent change in the medical (16) and veterinary fields (17)(18)(19)(20)(21)(22)(23)(24), and its relevance has been highlighted by the Coronavirus Disease 2019 (COVID- 19) pandemic. Health informatics (HI) is a broad and rapidly evolving field, which is used here in general terms to describe the combination of health and information technology.…”

Section: Introductionmentioning

confidence: 99%

Day-1 Competencies for Veterinarians Specific to Health Informatics

et al. 2021

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In 2015, the American Association of Veterinary Medical Colleges (AAVMC) developed the Competency-Based Veterinary Education (CBVE) framework to prepare practice-ready veterinarians through competency-based education, which is an outcomes-based approach to equipping students with the skills, knowledge, attitudes, values, and abilities to do their jobs. With increasing use of health informatics (HI: the use of information technology to deliver healthcare) by veterinarians, competencies in HI need to be developed. To reach consensus on a HI competency framework in this study, the Competency Framework Development (CFD) process was conducted using an online adaptation of Developing-A-Curriculum, an established methodology in veterinary medicine for reaching consensus among experts. The objectives of this study were to (1) create an HI competency framework for new veterinarians; (2) group the competency statements into common themes; (3) map the HI competency statements to the AAVMC competencies as illustrative sub-competencies; (4) provide insight into specific technologies that are currently relevant to new veterinary graduates; and (5) measure panelist satisfaction with the CFD process. The primary emphasis of the final HI competency framework was that veterinarians must be able to assess, select, and implement technology to optimize the client-patient experience, delivery of healthcare, and work-life balance for the veterinary team. Veterinarians must also continue their own education regarding technology by engaging relevant experts and opinion leaders.

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Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives—a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA)

Cited by 29 publications

References 153 publications

FasTag: Automatic text classification of unstructured medical narratives

FasTag: Automatic text classification of unstructured medical narratives

Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review

Day-1 Competencies for Veterinarians Specific to Health Informatics

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