Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk

Guidelines for the management of atherosclerotic cardiovascular disease (ASCVD) recommend the use of risk stratification models to identify patients most likely to benefit from cholesterol-lowering and other therapies. These models have differential performance across race and gender groups with inconsistent behavior across studies, potentially resulting in an inequitable distribution of beneficial therapy. In this work, we leverage adversarial learning and a large observational cohort extracted from electronic health records (EHRs) to develop a "fair" ASCVD risk prediction model with reduced variability in error rates across groups. We empirically demonstrate that our approach is capable of aligning the distribution of risk predictions conditioned on the outcome across several groups simultaneously for models built from high-dimensional EHR data. We also discuss the relevance of these results in the context of the empirical trade-off between fairness and model performance

Matching Pharmacogenomic Knowledge: Particularities, Results, and Perspectives

International audienceKnowledge in pharmacogenomics (PGx) is scattered across several resources, e.g., reference databases and the biomedical literature. Matching their content would thus lead to a consolidated view of the available PGx knowledge that could, in turn, support multiple downstream applications, including knowledge curation and precision medicine. However, matching atomic units of PGx knowledge is challenging due to their peculiarities: they are of n-ary nature, represented with heterogeneous vocabularies, and with various levels of granularity. In this paper, we frame the matching of PGx knowledge units of various provenance as an instance matching problem. We summarize our work to represent such units within a knowledge graph named PGxLOD, and to match them with a rule-based and a graph embedding-based matching approaches. We then particularly discuss the remaining challenges and how our research artifacts opened to the community could foster new benchmarks and methods for structure-based instance matching

Knowledge-Based Matching of nn-ary Tuples

An increasing number of data and knowledge sources are accessible by human and software agents in the expanding Semantic Web. Sources may differ in granularity or completeness, and thus be complementary. Consequently, they should be reconciled in order to unlock the full potential of their conjoint knowledge. In particular, units should be matched within and across sources, and their level of relatedness should be classified into equivalent, more specific, or similar. This task is challenging since knowledge units can be heterogeneously represented in sources (e.g., in terms of vocabularies). In this paper, we focus on matching n-ary tuples in a knowledge base with a rule-based methodology. To alleviate heterogeneity issues, we rely on domain knowledge expressed by ontologies. We tested our method on the biomedical domain of pharmacogenomics by searching alignments among 50,435 n-ary tuples from four different real-world sources. Results highlight noteworthy agreements and particularities within and across sources

Étudier les mécanismes des effets indésirables des médicaments avec l'IA explicable : expériences avec la fouille de graphes de connaissances

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Mining Electronic Health Records to Validate Knowledge in Pharmacogenomics

International audienceThe state of the art in pharmacogenomics (PGx) is based on a bank of knowledge resulting from sporadic observations, and so is not considered to be statistically valid. The PractiKPharma project is mining data from electronic health record repositories, and composing novel cohorts of patients for confirming (or moderating) pharmacogenomics knowledge on the basis of observations made in clinical practice

Using an ontological representation of chemotherapy toxicities for guiding information extraction and integration from EHRs

International audienceIntroduction. Chemotherapies against cancers are often interrupted due to severe drug toxicities, reducing treatment opportunities. For this reason, the detection of toxicities and their severity from EHRs is of importance for many downstream applications. However toxicity information is dispersed in various sources in the EHRs, making its extraction challenging. Methods. We introduce OntoTox, an ontology designed to represent chemotherapy toxicities, its attributes and provenance. We illustrated the interest of OntoTox by integrating toxicities and grading information extracted from three heterogeneous sources: EHR questionnaires, semi-structured tables, and free-text. Results. We instantiated 53,510, 2,366 and 54,420 toxicities from questionnaires, tables and free-text respectively, and compared the complementarity and redundancy of the three sources. Discussion. We illustrated with this preliminary study the potential of OntoTox to guide the integration of multiple sources, and identified that the three sources are only moderately overlapping, stressing the need for a common representation

Learning Subgraph Patterns from text for Extracting Disease–Symptom Relationships

International audienceTo some extent, texts can be represented in the form of graphs, such as dependency graphs in which nodes represent words and edges represent grammatical dependencies between words. Graph representation of texts is an interesting alternative to string representation because it provides an additional level of abstraction over the syntax that is sometime easier to compute. In this paper, we study the use of graph mining methods on texts represented as dependency graphs, for extracting relationships between pairs of annotated entities. We propose a three step approach that includes (1) the transformation of texts in a collection of dependency graphs; (2) the selection of frequent subgraphs, named hereafter patterns, on the basis of positive sentences; and (3) the extraction of relationships by searching for occurrences of patterns in novel sentences. Our method has been experimented by extracting disease–symptom relationships from a corpus of 51,292 PubMed abstracts (428,491 sentences)related to 50 rare diseases. The extraction of correct disease–symptom relationships has been evaluated on 565 sentences, showing a precision of 0.91 and a recall of 0.49 (F-Meaure is 0.63). These preliminary experiments show the feasibility of extracting good quality relationships using frequent subgraph mining