Assisted knowledge discovery for the maintenance of clinical guidelines

A scoping review of knowledge authoring tools used for developing computerized clinical decision support systems

Objective

Clinical Knowledge Authoring Tools (CKATs) are integral to the computerized Clinical Decision Support (CDS) development life cycle. CKATs enable authors to generate accurate, complete, and reliable digital knowledge artifacts in a relatively efficient and affordable manner. This scoping review aims to compare knowledge authoring tools and derive the common features of CKATs.

Materials and Methods

We performed a keyword-based literature search, followed by a snowball search, to identify peer-reviewed publications describing the development or use of CKATs. We used PubMed and Embase search engines to perform the initial search (n = 1579). After removing duplicate articles, nonrelevant manuscripts, and not peer-reviewed publication, we identified 47 eligible studies describing 33 unique CKATs. The reviewed CKATs were further assessed, and salient characteristics were extracted and grouped as common CKAT features.

Results

Among the identified CKATs, 55% use an open source platform, 70% provide an application programming interface for CDS system integration, and 79% provide features to validate/test the knowledge. The majority of the reviewed CKATs describe the flow of information, offer a graphical user interface for knowledge authors, and provide intellisense coding features (94%, 97%, and 97%, respectively). The composed list of criteria for CKAT included topics such as simulating the clinical setting, validating the knowledge, standardized clinical models and vocabulary, and domain independence. None of the reviewed CKATs met all common criteria.

Conclusion

Our scoping review highlights the key specifications for a CKAT. The CKAT specification proposed in this review can guide CDS authors in developing more targeted CKATs.

UPCLASS: a deep learning-based classifier for UniProtKB entry publications

In the UniProt Knowledgebase (UniProtKB), publications providing evidence for a specific protein annotation entry are organized across different categories, such as function, interaction and expression, based on the type of data they contain. To provide a systematic way of categorizing computationally mapped bibliographies in UniProt, we investigate a convolutional neural network (CNN) model to classify publications with accession annotations according to UniProtKB categories. The main challenge of categorizing publications at the accession annotation level is that the same publication can be annotated with multiple proteins and thus be associated with different category sets according to the evidence provided for the protein. We propose a model that divides the document into parts containing and not containing evidence for the protein annotation. Then, we use these parts to create different feature sets for each accession and feed them to separate layers of the network. The CNN model achieved a micro F1-score of 0.72 and a macro F1-score of 0.62, outperforming baseline models based on logistic regression and support vector machine by up to 22 and 18 percentage points, respectively. We believe that such an approach could be used to systematically categorize the computationally mapped bibliography in UniProtKB, which represents a significant set of the publications, and help curators to decide whether a publication is relevant for further curation for a protein accession. Database URL: https://goldorak.hesge.ch/bioexpclass/upclass/.

AMERICAN ASSOCIATION OF CLINICAL ENDOCRINOLOGISTS AND AMERICAN COLLEGE OF ENDOCRINOLOGY PROTOCOL FOR STANDARDIZED PRODUCTION OF CLINICAL PRACTICE GUIDELINES, ALGORITHMS, AND CHECKLISTS - 2017 UPDATE

Clinical practice guideline (CPG), clinical practice algorithm (CPA), and clinical checklist (CC, collectively CPGAC) development is a high priority of the American Association of Clinical Endocrinologists (AACE) and American College of Endocrinology (ACE). This 2017 update in CPG development consists of (1) a paradigm change wherein first, environmental scans identify important clinical issues and needs, second, CPA construction focuses on these clinical issues and needs, and third, CPG provide CPA node/edge-specific scientific substantiation and appended CC; (2) inclusion of new technical semantic and numerical descriptors for evidence types, subjective factors, and qualifiers; and (3) incorporation of patient-centered care components such as economics and transcultural adaptations, as well as implementation, validation, and evaluation strategies. This third point highlights the dominating factors of personal finances, governmental influences, and third-party payer dictates on CPGAC implementation, which ultimately impact CPGAC development. The AACE/ACE guidelines for the CPGAC program is a successful and ongoing iterative exercise to optimize endocrine care in a changing and challenging healthcare environment.

ABBREVIATIONS

AACE = American Association of Clinical Endocrinologists ACC = American College of Cardiology ACE = American College of Endocrinology ASeRT = ACE Scientific Referencing Team BEL = best evidence level CC = clinical checklist CPA = clinical practice algorithm CPG = clinical practice guideline CPGAC = clinical practice guideline, algorithm, and checklist EBM = evidence-based medicine EHR = electronic health record EL = evidence level G4GAC = Guidelines for Guidelines, Algorithms, and Checklists GAC = guidelines, algorithms, and checklists HCP = healthcare professional(s) POEMS = patient-oriented evidence that matters PRCT = prospective randomized controlled trial.