1,823 research outputs found
Symbolic modeling of structural relationships in the Foundational Model of Anatomy
The need for a sharable resource that can provide deep anatomical knowledge and support inference for biomedical applications has recently been the driving force in the creation of biomedical ontologies. Previous attempts at the symbolic representation of anatomical relationships necessary for such ontologies have been largely limited to general partonomy and class subsumption. We propose an ontology of anatomical relationships beyond class assignments and generic part-whole relations and illustrate the inheritance of structural attributes in the Digital Anatomist Foundational Model of Anatomy. Our purpose is to generate a symbolic model that accommodates all structural relationships and physical properties required to comprehensively and explicitly describe the physical organization of the human body
The Foundational Model of Anatomy Ontology
Anatomy is the structure of biological organisms. The term also denotes the scientific
discipline devoted to the study of anatomical entities and the structural and
developmental relations that obtain among these entities during the lifespan of an
organism. Anatomical entities are the independent continuants of biomedical reality on
which physiological and disease processes depend, and which, in response to etiological
agents, can transform themselves into pathological entities. For these reasons, hard copy
and in silico information resources in virtually all fields of biology and medicine, as a
rule, make extensive reference to anatomical entities. Because of the lack of a
generalizable, computable representation of anatomy, developers of computable
terminologies and ontologies in clinical medicine and biomedical research represented
anatomy from their own more or less divergent viewpoints. The resulting heterogeneity
presents a formidable impediment to correlating human anatomy not only across
computational resources but also with the anatomy of model organisms used in
biomedical experimentation. The Foundational Model of Anatomy (FMA) is being
developed to fill the need for a generalizable anatomy ontology, which can be used and
adapted by any computer-based application that requires anatomical information.
Moreover it is evolving into a standard reference for divergent views of anatomy and a
template for representing the anatomy of animals. A distinction is made between the FMA
ontology as a theory of anatomy and the implementation of this theory as the FMA
artifact. In either sense of the term, the FMA is a spatial-structural ontology of the
entities and relations which together form the phenotypic structure of the human
organism at all biologically salient levels of granularity. Making use of explicit
ontological principles and sound methods, it is designed to be understandable by human
beings and navigable by computers. The FMA’s ontological structure provides for
machine-based inference, enabling powerful computational tools of the future to reason
with biomedical data
The Potential of the Digital Anatomist Foundational Model for Assuring Consistency in UMLS Sources
Inconsistent anatomical concept representation can be identified in anatomy textbooks and hard copy term lists, as well as in UMLS source vocabularies and other controlled medical terminologies. In this report we select some examples of inconsistent representations of anatomical concepts, and illustrate how these inconsistencies can be explained and reconciled by the Digital Anatomist Foundational Model1. We use this process for gaining a measure of the validity of the logic-based Model
The Role of Definitions in Biomedical Concept Representation
The Foundational Model (FM) of anatomy, developed as an anatomical enhancement of UMLS, classifies anatomical entities in a structural context. Explicit definitions have played a critical role in the establishment of FM classes. Essential structural properties that distinguish a group of anatomical entities serve as the differentiae for defining classes. These, as well as other structural attributes, are introduced as template slots in Protege, a frame-based knowledge acquisition system, and are inherited by descendants of the class. A set of desiderata has evolved during the instantiation of the FM for formulating definitions. We contend that 1. these desiderata generalize to non-anatomical domains and 2. satisfying them in constituent vocabularies of UMLS would enhance the quality of information retrievable through UMLS
Representing Complexity in Part-Whole Relationships within the Foundational Model of Anatomy
The Foundational Model of Anatomy (FMA) is a frame-based ontology that represents declarative knowledge about the structural organization of the human body. Part-whole relationships play a particularly important role in this representation. In order to assure that knowledge-based applications relying on the FMA as a resource can reason about anatomy, we have modified and enhanced currently available schemes of meronymic relationships. We have introduced and defined distinct partitions for decomposing anatomical structures and attributed the part relationships in order to eliminate ambiguity and enhance specificity in the richness of meronymic relationships within the FMA
Generalised weibull model-based approaches to detect non-constant hazard to signal adverse drug reactions in longitudinal data
Pharmacovigilance is the process of monitoring the emergence of harm from a medicine once it has been licensed and is in use. The aim is to identify new adverse drug reactions (ADRs) or changes in frequency of known ADRs. The last decade has seen increased interest for the use of electronic health records (EHRs) in pharmacovigilance. The causal mechanism of an ADR will often result in the occurrence being time dependent. We propose identifying signals for ADRs based on detecting a variation in hazard of an event using a time-to-event approach. Cornelius et al. proposed a method based on the Weibull Shape Parameter (WSP) and demonstrated this to have optimal performance for ADRs occurring shortly after taking treatment or delayed ADRs, and introduced censoring at varying time points to increase performance for intermediate ADRs. We now propose two new approaches which combined perform equally well across all time periods. The performance of this new approach is illustrated through an EHR Bisphosphonates dataset and a simulation study. One new approach is based on the power generalised Weibull distribution (pWSP) introduced by Bagdonavicius and Nikulin alongside an extended version of the WSP test, which includes one censored dataset resulting in improved detection across time period (dWSP). In the Bisphosphonates example, the pWSP and dWSP tests correctly signalled two known ADRs, and signal one adverse event for which no evidence of association with the drug exist. A combined test involving both pWSP and dWSP is reliable independently of the time of occurrence of ADRs
Anatomical information science
The Foundational Model of Anatomy (FMA) is a map of the human body. Like maps of other sorts – including the map-like representations we find in familiar anatomical atlases – it is a representation of a certain portion of spatial reality as it exists at a certain (idealized) instant of time. But unlike other maps, the FMA comes in the form of a sophisticated ontology of its objectdomain, comprising some 1.5 million statements of anatomical relations among some 70,000 anatomical kinds. It is further distinguished from other maps in that it represents not some specific portion of spatial reality (say: Leeds in 1996), but rather the generalized or idealized spatial reality associated with a generalized or idealized human being at some generalized or idealized instant of time. It will be our concern in what follows to outline the approach to ontology that is represented by the FMA and to argue that it can serve as the basis for a new type of anatomical information science. We also draw some implications for our understanding of spatial reasoning and spatial ontologies in general
Label-free high-throughput photoacoustic tomography of suspected circulating melanoma tumor cells in patients in vivo
Significance: Detection and characterization of circulating tumor cells (CTCs), a key determinant of metastasis, are critical for determining risk of disease progression, understanding metastatic pathways, and facilitating early clinical intervention.
Aim: We aim to demonstrate label-free imaging of suspected melanoma CTCs.
Approach: We use a linear-array-based photoacoustic tomography system (LA-PAT) to detect melanoma CTCs, quantify their contrast-to-noise ratios (CNRs), and measure their flow velocities in most of the superficial veins in humans.
Results: With LA-PAT, we successfully imaged suspected melanoma CTCs in patients in vivo, with a CNR >9. CTCs were detected in 3 of 16 patients with stage III or IV melanoma. Among the three CTC-positive patients, two had disease progression; among the 13 CTC-negative patients, 4 showed disease progression.
Conclusions: We suggest that LA-PAT can detect suspected melanoma CTCs in patients in vivo and has potential clinical applications for disease monitoring in melanoma
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