Phenopacket-tools: Building and validating GA4GH Phenopackets.

The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.

GA4GH Phenopackets: A Practical Introduction.

The Global Alliance for Genomics and Health (GA4GH) is developing a suite of coordinated standards for genomics for healthcare. The Phenopacket is a new GA4GH standard for sharing disease and phenotype information that characterizes an individual person, linking that individual to detailed phenotypic descriptions, genetic information, diagnoses, and treatments. A detailed example is presented that illustrates how to use the schema to represent the clinical course of a patient with retinoblastoma, including demographic information, the clinical diagnosis, phenotypic features and clinical measurements, an examination of the extirpated tumor, therapies, and the results of genomic analysis. The Phenopacket Schema, together with other GA4GH data and technical standards, will enable data exchange and provide a foundation for the computational analysis of disease and phenotype information to improve our ability to diagnose and conduct research on all types of disorders, including cancer and rare diseases.

The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource.

The NHGRI-EBI GWAS Catalog (www.ebi.ac.uk/gwas) is a FAIR knowledgebase providing detailed, structured, standardised and interoperable genome-wide association study (GWAS) data to >200 000 users per year from academic research, healthcare and industry. The Catalog contains variant-trait associations and supporting metadata for >45 000 published GWAS across >5000 human traits, and >40 000 full P-value summary statistics datasets. Content is curated from publications or acquired via author submission of prepublication summary statistics through a new submission portal and validation tool. GWAS data volume has vastly increased in recent years. We have updated our software to meet this scaling challenge and to enable rapid release of submitted summary statistics. The scope of the repository has expanded to include additional data types of high interest to the community, including sequencing-based GWAS, gene-based analyses and copy number variation analyses. Community outreach has increased the number of shared datasets from under-represented traits, e.g. cancer, and we continue to contribute to awareness of the lack of population diversity in GWAS. Interoperability of the Catalog has been enhanced through links to other resources including the Polygenic Score Catalog and the International Mouse Phenotyping Consortium, refinements to GWAS trait annotation, and the development of a standard format for GWAS data.

PDCM Finder: an open global research platform for patient-derived cancer models.

PDCM Finder (www.cancermodels.org) is a cancer research platform that aggregates clinical, genomic and functional data from patient-derived xenografts, organoids and cell lines. It was launched in April 2022 as a successor of the PDX Finder portal, which focused solely on patient-derived xenograft models. Currently the portal has over 6200 models across 13 cancer types, including rare paediatric models (17%) and models from minority ethnic backgrounds (33%), making it the largest free to consumer and open access resource of this kind. The PDCM Finder standardises, harmonises and integrates the complex and diverse data associated with PDCMs for the cancer community and displays over 90 million data points across a variety of data types (clinical metadata, molecular and treatment-based). PDCM data is FAIR and underpins the generation and testing of new hypotheses in cancer mechanisms and personalised medicine development.

Building interpretable models for polypharmacy prediction in older chronic patients based on drug prescription records.

BACKGROUND: Multimorbidity presents an increasingly common problem in older population, and is tightly related to polypharmacy, i.e., concurrent use of multiple medications by one individual. Detecting polypharmacy from drug prescription records is not only related to multimorbidity, but can also point at incorrect use of medicines. In this work, we build models for predicting polypharmacy from drug prescription records for newly diagnosed chronic patients. We evaluate the models' performance with a strong focus on interpretability of the results. METHODS: A centrally collected nationwide dataset of prescription records was used to perform electronic phenotyping of patients for the following two chronic conditions: type 2 diabetes mellitus (T2D) and cardiovascular disease (CVD). In addition, a hospital discharge dataset was linked to the prescription records. A regularized regression model was built for 11 different experimental scenarios on two datasets, and complexity of the model was controlled with a maximum number of dimensions (MND) parameter. Performance and interpretability of the model were evaluated with AUC, AUPRC, calibration plots, and interpretation by a medical doctor. RESULTS: For the CVD model, AUC and AUPRC values of 0.900 (95% [0.898-0.901]) and 0.640 (0.635-0.645) were reached, respectively, while for the T2D model the values were 0.808 (0.803-0.812) and 0.732 (0.725-0.739). Reducing complexity of the model by 65% and 48% for CVD and T2D, resulted in 3% and 4% lower AUC, and 4% and 5% lower AUPRC values, respectively. Calibration plots for our models showed that we can achieve moderate calibration with reducing the models' complexity without significant loss of predictive performance. DISCUSSION: In this study, we found that it is possible to use drug prescription data to build a model for polypharmacy prediction in older population. In addition, the study showed that it is possible to find a balance between good performance and interpretability of the model, and achieve acceptable calibration at the same time.

CogStack - experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital.

BACKGROUND: Traditional health information systems are generally devised to support clinical data collection at the point of care. However, as the significance of the modern information economy expands in scope and permeates the healthcare domain, there is an increasing urgency for healthcare organisations to offer information systems that address the expectations of clinicians, researchers and the business intelligence community alike. Amongst other emergent requirements, the principal unmet need might be defined as the 3R principle (right data, right place, right time) to address deficiencies in organisational data flow while retaining the strict information governance policies that apply within the UK National Health Service (NHS). Here, we describe our work on creating and deploying a low cost structured and unstructured information retrieval and extraction architecture within King's College Hospital, the management of governance concerns and the associated use cases and cost saving opportunities that such components present. RESULTS: To date, our CogStack architecture has processed over 300 million lines of clinical data, making it available for internal service improvement projects at King's College London. On generated data designed to simulate real world clinical text, our de-identification algorithm achieved up to 94% precision and up to 96% recall. CONCLUSION: We describe a toolkit which we feel is of huge value to the UK (and beyond) healthcare community. It is the only open source, easily deployable solution designed for the UK healthcare environment, in a landscape populated by expensive proprietary systems. Solutions such as these provide a crucial foundation for the genomic revolution in medicine.

Cancer Care Treatment Outcome Ontology: A Novel Computable Ontology for Profiling Treatment Outcomes in Patients With Solid Tumors.

PURPOSE: There is as yet no computer-processable resource to describe treatment end points in cancer, hindering our ability to systematically capture and share outcomes data to inform better patient care. To address these unmet needs, we have built an ontology, the Cancer Care Treatment Outcome Ontology (CCTOO), to organize high-level concepts of treatment end points with structured knowledge representation to facilitate standardized sharing of real-world data. METHODS: End points from oncology trials in ClinicalTrials.gov were extracted, queried using the keyword cancer, and followed by an expert appraisal. Synonyms and relevant terms were imported from the National Cancer Institute Thesaurus and Common Terminology Criteria for Adverse Events. Logical relationships among concepts were manually represented by production rules. The applicability of 1,847 rules was tested in an index case. RESULTS: After removing duplicated terms from 54,705 trial entries, an ontology holding 1,133 terms was built. CCTOO organized concepts into four domains (cancer treatment, health services, physical, and psychosocial health-related concepts), 13 subgroups (including efficacy, safety, and quality of life), and two (taxonomic and evaluative) concept hierarchies. This ontology has a comprehensive term coverage in the cancer trial literature: at least one term was mentioned in 98% of MEDLINE abstracts of phase I to III trials, whereas concepts about efficacy were mentioned in 7,208 (79%) phase I, 15,051 (92%) phase II, and 3,884 (86%) phase III trials. The event sequence of the index case was readily convertible to a comprehensive profile incorporating response, treatment toxicity, and survival by applying the set of production rules curated in the CCTOO. CONCLUSION: CCTOO categorizes high-level treatment end points used in oncology and provides a mechanism for profiling individual patient data by outcomes to facilitate translational analysis.

Semantic interestingness measures for discovering association rules in the skeletal dysplasia domain.

BACKGROUND: Lately, ontologies have become a fundamental building block in the process of formalising and storing complex biomedical information. With the currently existing wealth of formalised knowledge, the ability to discover implicit relationships between different ontological concepts becomes particularly important. One of the most widely used methods to achieve this is association rule mining. However, while previous research exists on applying traditional association rule mining on ontologies, no approach has, to date, exploited the advantages brought by using the structure of these ontologies in computing rule interestingness measures. RESULTS: We introduce a method that combines concept similarity metrics, formulated using the intrinsic structure of a given ontology, with traditional interestingness measures to compute semantic interestingness measures in the process of association rule mining. We apply the method in our domain of interest - bone dysplasias - using the core ontologies characterising it and an annotated dataset of patient clinical summaries, with the goal of discovering implicit relationships between clinical features and disorders. Experimental results show that, using the above mentioned dataset and a voting strategy classification evaluation, the best scoring traditional interestingness measure achieves an accuracy of 57.33%, while the best scoring semantic interestingness measure achieves an accuracy of 64.38%, both at the recall cut-off point 5. CONCLUSIONS: Semantic interestingness measures outperform the traditional ones, and hence show that they are able to exploit the semantic similarities inherently present between ontological concepts. Nevertheless, this is dependent on the domain, and implicitly, on the semantic similarity metric chosen to model it.

Inferring characteristic phenotypes via class association rule mining in the bone dysplasia domain.

Finding, capturing and describing characteristic features represents a key aspect in disorder definition, diagnosis and management. This process is particularly challenging in the case of rare disorders, due to the sparse nature of data and expertise. From a computational perspective, finding characteristic features is associated with some additional major challenges, such as formulating a computationally tractable definition, devising appropriate inference algorithms or defining sound validation mechanisms. In this paper we aim to deal with each of these problems in the context provided by the skeletal dysplasia domain. We propose a clear definition for characteristic phenotypes, we experiment with a novel, class association rule mining algorithm and we discuss our lessons learned from both an automatic and human-based validation of our approach.

Decision support methods for finding phenotype--disorder associations in the bone dysplasia domain.

A lack of mature domain knowledge and well established guidelines makes the medical diagnosis of skeletal dysplasias (a group of rare genetic disorders) a very complex process. Machine learning techniques can facilitate objective interpretation of medical observations for the purposes of decision support. However, building decision support models using such techniques is highly problematic in the context of rare genetic disorders, because it depends on access to mature domain knowledge. This paper describes an approach for developing a decision support model in medical domains that are underpinned by relatively sparse knowledge bases. We propose a solution that combines association rule mining with the Dempster-Shafer theory (DST) to compute probabilistic associations between sets of clinical features and disorders, which can then serve as support for medical decision making (e.g., diagnosis). We show, via experimental results, that our approach is able to provide meaningful outcomes even on small datasets with sparse distributions, in addition to outperforming other Machine Learning techniques and behaving slightly better than an initial diagnosis by a clinician.

The Bone Dysplasia Ontology: integrating genotype and phenotype information in the skeletal dysplasia domain.

BACKGROUND: Skeletal dysplasias are a rare and heterogeneous group of genetic disorders affecting skeletal development. Patients with skeletal dysplasias suffer from many complex medical issues including degenerative joint disease and neurological complications. Because the data and expertise associated with this field is both sparse and disparate, significant benefits will potentially accrue from the availability of an ontology that provides a shared conceptualisation of the domain knowledge and enables data integration, cross-referencing and advanced reasoning across the relevant but distributed data sources. RESULTS: We introduce the design considerations and implementation details of the Bone Dysplasia Ontology. We also describe the different components of the ontology, including a comprehensive and formal representation of the skeletal dysplasia domain as well as the related genotypes and phenotypes. We then briefly describe SKELETOME, a community-driven knowledge curation platform that is underpinned by the Bone Dysplasia Ontology. SKELETOME enables domain experts to use, refine and extend and apply the ontology without any prior ontology engineering experience--to advance the body of knowledge in the skeletal dysplasia field. CONCLUSIONS: The Bone Dysplasia Ontology represents the most comprehensive structured knowledge source for the skeletal dysplasias domain. It provides the means for integrating and annotating clinical and research data, not only at the generic domain knowledge level, but also at the level of individual patient case studies. It enables links between individual cases and publicly available genotype and phenotype resources based on a community-driven curation process that ensures a shared conceptualisation of the domain knowledge and its continuous incremental evolution.