pinecone/core-2020-05-10-deduplication huggingface dataset:
B Cell apoptosis in the central nervous system in experimental autoimmune Encephalomyelitis: Roles of B Cell CD95, CD95L and Bcl-2 expression

The role and fate of B cells in the central nervous system (CNS) in experimental autoimmune encephalomyelitis (EAE) are unknown. Using enzyme-linked immunospot assays we now show that B cells reactive to myelin basic protein (MBP) accumulate in the CNS of Lewis rats with acute EAE induced by immunization with MBP and adjuvants. We also report that B cells are eliminated from the CNS by apoptosis during spontaneous recovery from this disease. Apoptotic B cells were identified by flow cytometry of inflammatory cells extracted from the spinal cord and by histological sections of the spinal cord using light and electron microscopic immunocytochemistry. B cell apoptosis occurred preferentially in the CNS rather than in the peripheral lymphoid organs and was maximal just prior to the onset of spontaneous clinical recovery. Three colour flow cytometry indicated that B cells expressing CD95 (Fas) or CD95 ligand (CD95L) were highly vulnerable to apoptosis, whereas B cells expressing Bcl-2 were relatively protected from apoptosis. We propose that B cells are eliminated from the CNS by the interaction of CD95L and CD95 on the same B cell and that this contributes to the spontaneous resolution of CNS inflammation and clinical recovery in acute EAE
A Comparison of Classification Algorithms to Automatically Identify Chest X-Ray Reports That Support Pneumonia

AbstractWe compared the performance of expert-crafted rules, a Bayesian network, and a decision tree at automatically identifying chest X-ray reports that support acute bacterial pneumonia. We randomly selected 292 chest X-ray reports, 75 (25%) of which were from patients with a hospital discharge diagnosis of bacterial pneumonia. The reports were encoded by our natural language processor and then manually corrected for mistakes. The encoded observations were analyzed by three expert systems to determine whether the reports supported pneumonia. The reference standard for radiologic support of pneumonia was the majority vote of three physicians. We compared (a) the performance of the expert systems against each other and (b) the performance of the expert systems against that of four physicians who were not part of the gold standard. Output from the expert systems and the physicians was transformed so that comparisons could be made with both binary and probabilistic output. Metrics of comparison for binary output were sensitivity (sens), precision (prec), and specificity (spec). The metric of comparison for probabilistic output was the area under the receiver operator characteristic (ROC) curve. We used McNemar's test to determine statistical significance for binary output and univariate z-tests for probabilistic output. Measures of performance of the expert systems for binary (probabilistic) output were as follows: Rules—sens, 0.92; prec, 0.80; spec, 0.86 (Az, 0.960); Bayesian network—sens, 0.90; prec, 0.72; spec, 0.78 (Az, 0.945); decision tree—sens, 0.86; prec, 0.85; spec, 0.91 (Az, 0.940). Comparisons of the expert systems against each other using binary output showed a significant difference between the rules and the Bayesian network and between the decision tree and the Bayesian network. Comparisons of expert systems using probabilistic output showed no significant differences. Comparisons of binary output against physicians showed differences between the Bayesian network and two physicians. Comparisons of probabilistic output against physicians showed a difference between the decision tree and one physician. The expert systems performed similarly for the probabilistic output but differed in measures of sensitivity, precision, and specificity produced by the binary output. All three expert systems performed similarly to physicians
Exploring the Utility of Demographic Data and Vaccination History Data in the Deduplication of Immunization Registry Patient Records

AbstractDuplicate patient records pose a major problem for many immunization registries, as well as for many electronic patient record systems. This paper reports two complementary studies exploring the deduplication of immunization registry records. One study explores the utility of different demographic data elements, singly and in combination, to assist in the deduplication process. The second study explores how clinical patient data (vaccination history data) might assist in this process. To assess the utility of demographic data elements, data were used from three registries after duplicates had been identified. A computer program, IMM/Scan, was written to count the number of true-positive (TP) matches and false-positive (FP) matches found when using different Boolean combinations of demographic data elements. In this study, a strategy of “ORing high value ANDed pairs of data elements” appeared to be most powerful. To assess the utility of vaccination history data, record pairs were drawn from 440,000 patient records. Two metrics on patient history were tested: (1) the number of identical doses shared by two records, and (2) the number of “extra” doses in the combined history of two records. In this study, sample findings include: (1) for pairs of nonduplicate records, 93% had no identical doses and 90.6% had “extra” doses, and (2) for pairs of duplicate records, 83.8% had one or more identical doses and 82% contained no “extra” doses. These studies demonstrate potentially useful approaches to using demographic data and patient history data to assist the automated deduplication of immunization patient records
Methods of Cognitive Analysis to Support the Design and Evaluation of Biomedical Systems: The Case of Clinical Practice Guidelines

AbstractThis article provides a theoretical and methodological framework for the use of cognitive analysis to support the representation of biomedical knowledge and the design of clinical systems, using clinical-practice guidelines (CPGs) as an example. We propose that propositional and semantic analyses, when used as part of the system-development process, can improve the validity, usability, and comprehension of the resulting biomedical applications. The framework we propose is based on a large body of research on the study of how people mentally represent information and subsequently use it for problem solving. This research encompasses many areas of psychology, but the more important ones are the study of memory and the study of comprehension. Of particular relevance is research devoted to investigating the comprehension and memory of language, expressed verbally or in text. In addition, research on how contextual variables affect performance is informative because these psychological processes are influenced by situational variables (e.g., setting, culture). One important factor limiting the acceptance and use of clinical-practice guidelines (CPGs) may be the mismatch between a guideline's recommended actions and the physician-user's mental models of what seems appropriate in a given case. Furthermore, CPGs can be semantically complex, often composed of elaborate collections of prescribed procedures with logical gaps or contradictions that can promote ambiguity and hence frustration on the part of those who attempt to use them. An improved understanding of the semantics and structure of CPGs may help to improve such matching, and ultimately the comprehensibility and usability of CPGs. Cognitive methods of analysis can help guideline designers and system builders throughout the development process, from the conceptual design of a computer-based system to its implementation phases. By studying how guideline creators and developers represent guidelines, both mentally and in text, and how end-users understand and make decisions with such guidelines, we can inform the development of technologies that seek to improve the match between the representations of experts and practitioners. We urge informaticians to recognize the potential relevance of cognitive analysis methods and to begin more extensive experimentation with the their use in biomedical informatics research
Automatic Lumbar Vertebral Identification Using Surface-Based Registration

AbstractThis work proposes the use of surface-based registration to automatically select a particular vertebra of interest during surgery. Manual selection of the correct vertebra can be a challenging task, especially for closed-back, minimally invasive procedures. Our method uses shape variations that exist among lumbar vertebrae to automatically determine the portion of the spinal column surface that correctly matches a set of physical vertebral points. In our experiments, we register vertebral points representing posterior elements of a single vertebra in physical space to spinal column surfaces extracted from computed tomography images of multiple vertebrae. After registering the set of physical points to each vertebral surface that is a potential match, we then compute the standard deviation of the surface error for each registration trial. The registration that corresponds to the lowest standard deviation designates the correct match. We have performed our current experiments on two plastic spine phantoms and two patients
A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions

AbstractWe analyze the discriminatory power of k-nearest neighbors, logistic regression, artificial neural networks (ANNs), decision tress, and support vector machines (SVMs) on the task of classifying pigmented skin lesions as common nevi, dysplastic nevi, or melanoma. Three different classification tasks were used as benchmarks: the dichotomous problem of distinguishing common nevi from dysplastic nevi and melanoma, the dichotomous problem of distinguishing melanoma from common and dysplastic nevi, and the trichotomous problem of correctly distinguishing all three classes. Using ROC analysis to measure the discriminatory power of the methods shows that excellent results for specific classification problems in the domain of pigmented skin lesions can be achieved with machine-learning methods. On both dichotomous and trichotomous tasks, logistic regression, ANNs, and SVMs performed on about the same level, with k-nearest neighbors and decision trees performing worse
Extracting Knowledge from Dynamics in Gene Expression

AbstractMost investigations of coordinated gene expression have focused on identifying correlated expression patterns between genes by examining their normalized static expression levels. In this study, we focus on the dynamics of gene expression by seeking to identify correlated patterns of changes in genetic expression level. In doing so, we build upon methods developed in clinical informatics to detect temporal trends of laboratory and other clinical data. We construct relevance networks from Saccharomyces cerevisiae gene-expression dynamics data and find genes with related functional annotations grouped together. While some of these associations are also found using a standard expression level analysis, many are identified exclusively through the dynamic analysis. These results strongly suggest that the analysis of gene expression dynamics is a necessary and important tool for studying regulatory and other functional relationships among genes. The source code developed for this investigation is freely available to all noncommercial investigators by contacting the authors
Cell Image Segmentation with Kernel-Based Dynamic Clustering and an Ellipsoidal Cell Shape Model

AbstractIn this paper, we propose a novel approach to cell image segmentation under severe noise conditions by combining kernel-based dynamic clustering and a genetic algorithm. Our method incorporates a priori knowledge about cell shape. That is, an elliptical cell contour model is introduced to describe the boundary of the cell. Our method consists of the following components: (1) obtain the gradient image; (2) use the gradient image to obtain points which possibly belong to cell boundaries; (3) adjust the parameters of the elliptical cell boundary model to match the cell contour using a genetic algorithm. The method is tested on images of noisy human thyroid and small intestine cells
Wavelets and Imaging Informatics: A Review of the Literature

AbstractModern medicine is a field that has been revolutionized by the emergence of computer and imaging technology. It is increasingly difficult, however, to manage the ever-growing enormous amount of medical imaging information available in digital formats. Numerous techniques have been developed to make the imaging information more easily accessible and to perform analysis automatically. Among these techniques, wavelet transforms have proven prominently useful not only for biomedical imaging but also for signal and image processing in general. Wavelet transforms decompose a signal into frequency bands, the width of which are determined by a dyadic scheme. This particular way of dividing frequency bands matches the statistical properties of most images very well. During the past decade, there has been active research in applying wavelets to various aspects of imaging informatics, including compression, enhancements, analysis, classification, and retrieval. This review represents a survey of the most significant practical and theoretical advances in the field of wavelet-based imaging informatics
Maintaining and Incrementally Revalidating a Computer-Based Clinical Guideline: A Case Study

AbstractThe paper explores the issues involved in maintaining the logic within a complex computer-based clinical guideline, using as a case study IMM/Serve, an operational guideline whose domain is childhood immunization. For a period of more than a year and a half, we have maintained a log of (1) the national changes to the immunization recommendations, (2) the local customizations of IMM/Serve's logic, and (3) certain logic problems that arose in the process of accommodating these changes and customizations. We describe the nature of these changes, customizations, and problems. We also discuss how different types of domain knowledge might assist in the automated process of validating successive versions of the logic. The paper's goal is to use the immunization domain to provide specific examples of the issues and problems that arise in maintaining a computer-based clinical guideline
Accessing Heterogeneous Sources of Evidence to Answer Clinical Questions

AbstractThe large and rapidly growing number of information sources relevant to health care, and the increasing amounts of new evidence produced by researchers, are improving the access of professionals and students to valuable information. However, seeking and filtering useful, valid information can be still very difficult. An online information system that conducts searches based on individual patient data can have a beneficial influence on the particular patient's outcome and educate the healthcare worker. In this paper, we describe the underlying model for a system that aims to facilitate the search for evidence based on clinicians' needs. This paper reviews studies of information needs of clinicians, describes principles of information retrieval, and examines the role that standardized terminologies can play in the integration between a clinical system and literature resources, as well as in the information retrieval process. The paper also describes a model for a digital library system that supports the integration of clinical systems with online information sources, making use of information available in the electronic medical record to enhance searches and information retrieval. The model builds on several different, previously developed techniques to identify information themes that are relevant to specific clinical data. Using a framework of evidence-based practice, the system generates well-structured questions with the intent of enhancing information retrieval. We believe that by helping clinicians to pose well-structured clinical queries and including in them relevant information from individual patients' medical records, we can enhance information retrieval and thus can improve patient-care
A Knowledge-Based, Concept-Oriented View Generation System for Clinical Data

AbstractInformation overload is a well-known problem for clinicians who must review large amounts of data in patient records. Concept-oriented views, which organize patient data around clinical concepts such as diagnostic strategies and therapeutic goals, may offer a solution to the problem of information overload. However, although concept-oriented views are desirable, they are difficult to create and maintain. We have developed a general-purpose, knowledge-based approach to the generation of concept-oriented views and have developed a system to test our approach. The system creates concept-oriented views through automated identification of relevant patient data. The knowledge in the system is represented by both a semantic network and rules. The key relevant data identification function is accomplished by a rule-based traversal of the semantic network. This paper focuses on the design and implementation of the system; an evaluation of the system is reported separately
Analysis of Complex Decision-Making Processes in Health Care: Cognitive Approaches to Health Informatics

AbstractDecision making by health care professionals is often complicated by the need to integrate ill-structured, uncertain, and potentially conflicting information from various sources. In this paper cognitive approaches to the study of decision making are presented within the context of a variety of complex health care applications. In recent years it has become increasingly accepted that in order to build information systems that can support complex decision making it will be necessary to more fully understand human decision-making processes. Methodological approaches are described that aim to explicate the decision making and reasoning skills of subjects as they perform activities involving the processing of complex information. The paper begins by presenting the theoretical foundations for cognitive analyses of decision making, including discussion of major approaches to the study of decision making in a range of real-world domains, including medicine. Applications of cognitive approaches are then illustrated, including a description of a study in which subjects were asked to “think aloud” in providing treatment decisions for complex medical cases. The resulting protocols were then analyzed for subjects' use of decision strategies and problems in reasoning. Extension of cognitive approaches to the study of group decision-making processes is also described. Recent approaches are discussed which borrow from advances in the study of human-computer interaction and which utilize video analysis of decision-making activities involving information technologies. Using these approaches it has been found that health care information systems, such as computerized patient record systems, may have inadvertent effects on human decision making. Implications of a cognitive approach to improving our understanding of complex decision making are discussed in the context of developing appropriate computer-based decision support for both individuals and groups
A Hybrid Input-Output Approach to Model Metabolic Systems: An Application to Intracellular Thiamine Kinetics

AbstractModels of the dynamics of complex metabolic systems offer potential benefits to the deep comprehension of the system under study as well as for the performance of certain tasks. Unfortunately, dynamic modeling of a great deal of metabolic systems may be problematic due to the incompleteness of the available knowledge about the underlying mechanisms and to the lack of an adequate observational data set. In theory, a valid alternative to classical structural modeling through ordinary differential equations could be represented by input-output approaches. But, in practice, such methods, which learn the nonlinear dynamics of the system from input-output data, fail when the experimental data set is poor either in size or in quality. Such a situation is not rare in the case of metabolic systems. This paper deals with a hybrid approach which aims at overcoming the problems addressed above. More specifically, it allows us to solve the identification problems of the intracellular thiamine kinetics in the intestine tissue. The method, which is half way between the structural and input-output approach, uses the outcomes of the simulation of a qualitative structural model to build a good initialization of a fuzzy system identifier. Such an initialization allows us to efficiently cope with both the incompleteness of knowledge and the inadequacy of the available data set, and to derive an input-output model of the intracellular thiamine kinetics in the intestine tissue. The comparison of the predictions of the intracellular thiamine kinetics obtained by the application of such a model with those obtained by traditional approaches, namely compartmental models, neural networks, and fuzzy systems, highlighted a better performance of our model. As the structural assumptions are relaxed, we obtained a model slightly less informative than a purely structural one but robust enough to be used as a simulator. The paper also discusses the interpretative potential offered by such a model, as tested on diabetic subjects
Disambiguating Ambiguous Biomedical Terms in Biomedical Narrative Text: An Unsupervised Method

AbstractWith the growing use of Natural Language Processing (NLP) techniques for information extraction and concept indexing in the biomedical domain, a method that quickly and efficiently assigns the correct sense of an ambiguous biomedical term in a given context is needed concurrently. The current status of word sense disambiguation (WSD) in the biomedical domain is that handcrafted rules are used based on contextual material. The disadvantages of this approach are (i) generat ing WSD rules manually is a time-consuming and tedious task, (ii) maintenance of rule sets becomes increasingly difficult over time, and (iii) handcrafted rules are often incomplete and perform poorly in new domains comprised of specialized vocabularies and different genres of text. This paper presents a two-phase unsupervised method to build a WSD classifier for an ambiguous biomedical term W. The first phase automatically creates a sense-tagged corpus for W, and the second phase derives a classifier for W using the derived sense-tagged corpus as a training set. A formative experiment was performed, which demonstrated that classifiers trained on the derived sense-tagged corpora achieved an overall accuracy of about 97%, with greater than 90% accuracy for each individual ambiguous term
Heterogeneous Database Integration in Biomedicine

AbstractThe rapid expansion of biomedical knowledge, reduction in computing costs, and spread of internet access have created an ocean of electronic data. The decentralized nature of our scientific community and healthcare system, however, has resulted in a patchwork of diverse, or heterogeneous, database implementations, making access to and aggregation of data across databases very difficult. The database heterogeneity problem applies equally to clinical data describing individual patients and biological data characterizing our genome. Specifically, databases are highly heterogeneous with respect to the data models they employ, the data schemas they specify, the query languages they support, and the terminologies they recognize. Heterogeneous database systems attempt to unify disparate databases by providing uniform conceptual schemas that resolve representational heterogeneities, and by providing querying capabilities that aggregate and integrate distributed data. Research in this area has applied a variety of database and knowledge-based techniques, including semantic data modeling, ontology definition, query translation, query optimization, and terminology mapping. Existing systems have addressed heterogeneous database integration in the realms of molecular biology, hospital information systems, and application portability