Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings

Nelson, Charlotte; Butte, Atul J.; Baranzini, Sergio E.

doi:10.1038/s41467-019-11069-0

Cited by 59 publications

(64 citation statements)

References 27 publications

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“…105 Other studies such as SPOKE (Scalable Precision Medicine Oriented Knowledge Engine), wish to integrate these data within the storage platform, by building a knowledge network using unsupervised machine learning that informs on how data types such as GWAS, gene ontology, pathways and drug data are connected to EMRs. 106 Improving knowledge of how these data are related is a key step towards implementing precision medicine.…”

Section: Validation and Independent Testingmentioning

confidence: 99%

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

et al. 2020

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Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

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Section: Validation and Independent Testingmentioning

confidence: 99%

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

et al. 2020

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“…Furthermore, the CKG has the potential to work as a clinical decision support tool and we have already used it in this capacity (Doll et al, , 2019b. A next step could be the integration of electronic patient records (EHRs) into the graph, which has been suggested as a way to bridge the gap between research and direct patient care (Jensen et al, 2014;Nelson et al, 2019). However, this will require addressing a plethora of regulatory and ethical issues (Abul-Husn and Kenny, 2019; Grote and Berens, 2020).…”

Section: Discussionmentioning

confidence: 99%

Clinical Knowledge Graph Integrates Proteomics Data into Clinical Decision-Making

Santos

Colaço

Nielsen

et al. 2020

Preprint

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The promise of precision medicine is to deliver personalized treatment based on the unique physiology of each patient. This concept was fueled by the genomic revolution, but it is now evident that integrating other types of omics data, like proteomics, into the clinical decisionmaking process will be essential to accomplish precision medicine goals. However, quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across myriad biomedical databases and publications makes this exceptionally difficult. To address this, we developed the Clinical Knowledge Graph (CKG), an open source platform currently comprised of more than 16 million nodes and 220 million relationships to represent relevant experimental data, public databases and the literature. The CKG also incorporates the latest statistical and machine learning algorithms, drastically accelerating analysis and interpretation of typical proteomics workflows. We use several biomarker studies to illustrate how the CKG may support, enrich and accelerate clinical decision-making. Graphical Abstract

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“…Other efforts to produce domain-specific KGs were published in a variety of areas, including biodiversity (71)(72)(73), the microbiome (74), and for the purpose of enriching clinical data (75,76). The articles on biodiversity focused specifically on how a KG could be created and linked to identifiers in the literature (71,72) or other important biodiversity resources (73).…”

Section: Constructing Knowledge Graphsmentioning

confidence: 99%

“…The articles on biodiversity focused specifically on how a KG could be created and linked to identifiers in the literature (71,72) or other important biodiversity resources (73). In contrast, papers using KGs for clinical enrichment aimed to use KGs as way to link clinical data to sources of evidence to provide support to clinical observations (76)(77)(78) or to help make the data more interpretable with respect to underlying biological mechanism(s) (75) for improved diagnosis (79).…”

Section: Constructing Knowledge Graphsmentioning

confidence: 99%

Knowledge-Based Biomedical Data Science

Callahan

Tripodi

Pielke-Lombardo

et al. 2020

Annu. Rev. Biomed. Data Sci.

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Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.TBoxes (for terminology), and assertions composed of them are ABoxes (for assertion) (12). Knowledge-base vs. Knowledge GraphKnowledge-bases that can be represented as graphs are often called knowledge graphs.While not all knowledge-bases are implemented as graphs (e.g. some are databases where table structure makes implicit assertions), in recent years, it has become very common to represent knowledge-bases using the Semantic Web standard or, at least be able to produce and consume Semantic Web compatible versions. For that reason, the terms knowledge-base and knowledge graph are often used interchangeably. In 2012, Google announced its proprietary Knowledge Graph, which also popularized the use of the term (13). The literature sometimes contains terminological imprecision about what the differences are between knowledge-bases, knowledge graphs and ontologies; there is a review and analysis of various published definitions (14). In this review, we use the term knowledge graph (or KG) and say a KG is grounded in the set of primitives from which it is constructed. Some KGs also include a set of logical rules that relate assertions to each other (e.g. Human TP53 is the subclass of TP53 proteins that is found in the organism human) called axioms. Biomedical ApplicationsKBDS does computation over KGs (and perhaps other inputs) to make inferences about biomedicine. While each of the publications surveyed below addresses different problems using different techniques, there are some common themes in the computational approaches to using KGs.

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Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings

Cited by 59 publications

References 27 publications

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Clinical Knowledge Graph Integrates Proteomics Data into Clinical Decision-Making

Knowledge-Based Biomedical Data Science

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