The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Harrison's Principles of Internal Medicine

What do a seventeenth-century mortality table (whose causes of death include "fainted in a bath," "frighted," and "itch"); the identification of South Africans during apartheid as European, Asian, colored, or black; and the separation of machine- from hand-washables have in common? All are examples of classification -- the scaffolding of information infrastructures. In Sorting Things Out, Geoffrey C. Bowker and Susan Leigh Star explore the role of categories and standards in shaping the modern world. In a clear and lively style, they investigate a variety of classification systems, including the International Classification of Diseases, the Nursing Interventions Classification, race classification under apartheid in South Africa, and the classification of viruses and of tuberculosis. The authors emphasize the role of invisibility in the process by which classification orders human interaction. They examine how categories are made and kept invisible, and how people can change this invisibility when necessary. They also explore systems of classification as part of the built information environment. Much as an urban historian would review highway permits and zoning decisions to tell a city's story, the authors review archives of classification design to understand how decisions have been made. Sorting Things Out has a moral agenda, for each standard and category valorizes some point of view and silences another. Standards and classifications produce advantage or suffering. Jobs are made and lost; some regions benefit at the expense of others. How these choices are made and how we think about that process are at the moral and political core of this work. The book is an important empirical source for understanding the building of information infrastructures.

https://www.reciis.icict.fiocruz.br/index.php/reciis/article/download/794/1436

Sorting Things Out: Classification and Its Consequences

The UMLS Metathesaurus, the largest thesaurus in the biomedical domain, provides a representation of biomedical knowledge consisting of concepts classified by semantic type and both hierarchical and non-hierarchical relationships among the concepts. This knowledge has proved useful for many applications including decision support systems, management of patient records, information retrieval (IR) and data mining. Gaining effective access to the knowledge is critical to the success of these applications. This paper describes MetaMap, a program developed at the National Library of Medicine (NLM) to map biomedical text to the Metathesaurus or, equivalently, to discover Metathesaurus concepts referred to in text. MetaMap uses a knowledge intensive approach based on symbolic, natural language processing (NLP) and computational linguistic techniques. Besides being applied for both IR and data mining applications, MetaMap is one of the foundations of NLM's Indexing Initiative System which is being applied to both semi-automatic and fully automatic indexing of the biomedical literature at the library.

Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program

This introduction presents the main motivations for the development of Description Logics (DLs) as a formalism for representing knowledge, as well as some important basic notions underlying all systems that have been created in the DL tradition. In addition, we provide the reader with an overview of the entire book and some guidelines for reading it. We first address the relationship between Description Logics and earlier semantic network and frame systems, which represent the original heritage of the field. We delve into some of the key problems encountered with the older efforts. Subsequently, we introduce the basic features of DL languages and related reasoning techniques. DL languages are then viewed as the core of knowledge representation systems, considering both the structure of a DL knowledge base and its associated reasoning services. The development of some implemented knowledge representation systems based on Description Logics and the first applications built with such systems are then reviewed. Finally, we address the relationship of Description Logics to other fields of Computer Science.We also discuss some extensions of the basic representation language machinery; these include features proposed for incorporation in the formalism that originally arose in implemented systems, and features proposed to cope with the needs of certain application domains.

https://assets.cambridge.org/97805218/76254/frontmatter/9780521876254_frontmatter.pdf

The Description Logic Handbook

https://www.acilci.net/wp-content/uploads/2014/02/Guidelines-for-reporting-reliability-and-agreement-studies-GRRAS-were-proposed.pdf

Guidelines for Reporting Reliability and Agreement Studies (GRRAS) were proposed.

Information needs studies in clinical settings often face the problem of integrating and analyzing data collected using different study tools. Generally, a coding taxonomy or a model is developed to capture and code the study data. Significant efforts are required to develop a model that not only captures the study data but is also closer to the clinical domain to draw meaningful real world inferences. Further, a study-specific model limits comparative evaluation of studies across different institutions. In this paper, we propose a reference model for representing nursing information needs. We use an iterative and collaborative approach in representing the concepts in the model. The model consists of 33 information (need) concepts, 29 information sources and 7 categories of nursing tasks which were used to code 228 identified information need instances.

Towards a reference model for representing nursing information needs.

Biomedical terminologies often contain composite concepts that cannot be translated into single unique synonymous concepts in a target controlled terminology. Such composite concepts need to be decomposed into sets of component concepts present in the target terminology that can serve as the proxy for applications in information retrieval, decision support or data analysis. Towards this goal, we use a “clustering coefficient” over the UMLS Metathesaurus to traverse the closely clustered neighbors of the composite source concept to generate a ranked list of possible component concepts. Using the MeSH Associated Expression mappings as the gold-standard, we show that the proposed approach generates relevant component concepts as compared to existing semantic locality based methods. The topological connectivity of the concepts in the UMLS Metathesaurus is a useful feature that can be coupled with existing lexical and semantic locality based approaches towards terminology translation.

Decompositional terminology translation using network analysis.

The information needs of clinicians, as they interact with the EHR, are well-studied. Clinical researchers also interact with the EHR and, while they might be expected to have some similar needs, the unique needs that arise due to nature of their work remain largely unstudied. For clinicians, infobuttons (context-aware hyperlinks) provide a mechanism of studying these information needs. Here we describe the integration of infobuttons into i2b2, a popular data warehouse commonly used by clinical researchers, using a plugin. A preliminary survey of i2b2 developers suggests a general interest in infobuttons for i2b2 and indicates good likelihood for their deployment, where they may be used as a tool for further studying these needs in greater detail.

i3b3: Infobuttons for i2b2 as a Mechanism for Investigating the Information Needs of Clinical Researchers.

OBJECTIVES Automatic de-identification to remove protected health information (PHI) from clinical text can use a "binary" model that replaces redacted text with a generic tag (e.g., " "), or can use a "multiclass" model that retains more class information (e.g., " "). Binary models are easier to develop, but result in text that is potentially less informative. We investigated whether building a multiclass de-identification is worth the extra effort. METHODS Using the 2014 i2b2 dataset, we compared the accuracy and impact on document readability of two models. In the first experiment, we generated one binary and two multiclass versions trained with the same machine-learning algorithm Conditional Random Field (CRF). Accuracy (recall, precision, f-score) and secondary metrics (e.g, training time, testing time, minimum memory required) were measured. In the second experiment, three reviewers accessed the readability of two redacted documents using the binary and multiclass methods. We estimated a pooled Kappa to estimate the inter-rater agreement. RESULTS The multiclass model did not demonstrate a clear accuracy advantage, with lower recall (-1.9%) and only slightly better precision (+0.6%), despite requiring additional computing resources. Three raters reached a very high agreement (Kappa = 0.975, 95% Confidence Interval (0.946, 1.00), p < 0.0001) that both binary and multiclass models have the same impact on document readability. CONCLUSIONS This study suggests that the development of more sophisticated classification of PHI may not be worth the effort in terms of both system accuracy and the usefulness of the output.

Is Multiclass Automatic Text De-Identification Worth the Effort?

Effective clinical communication among health care providers is essential to provide high-quality care for patients Poor communication has been recognized as a significant source of medical errors To help facilitate clinical communication between physicians and nurses, we have developed a virtual whiteboard extension to our clinical information system, WebCIS The virtual whiteboard can be used to identify other health care providers associated with the users patients, and facilitate exchange of brief asynchronous communications among those providers

James J. Cimino

Papers

Towards a reference model for representing nursing information needs.

Decompositional terminology translation using network analysis.

i3b3: Infobuttons for i2b2 as a Mechanism for Investigating the Information Needs of Clinical Researchers.

Is Multiclass Automatic Text De-Identification Worth the Effort?

Improving Clinical Communication with a Virtual Whiteboard