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.

Conceptual Guidance in Information Retrieval

Introduction We have established that inadequate access to timely information and ineffective communication among patient care team members are frequent events that are proximal causes of medical errors. We are exploring the use of wireless mobile computing technology to help reduce these problems through extensions to the Web-based clinical information system at New York Presbyterian Hospital (NYPH).

Use of Wireless Technology for Reducing Medical Errors

Background Scientific hypothesis generation is a critical step in scientific research that determines the direction and impact of any investigation. Despite its vital role, we have limited knowledge of the process itself, thus hindering our ability to address some critical questions. Objective This study aims to answer the following questions: To what extent can secondary data analytics tools facilitate the generation of scientific hypotheses during clinical research? Are the processes similar in developing clinical diagnoses during clinical practice and developing scientific hypotheses for clinical research projects? Furthermore, this study explores the process of scientific hypothesis generation in the context of clinical research. It was designed to compare the role of VIADS, a visual interactive analysis tool for filtering and summarizing large data sets coded with hierarchical terminologies, and the experience levels of study participants during the scientific hypothesis generation process. Methods This manuscript introduces a study design. Experienced and inexperienced clinical researchers are being recruited since July 2021 to take part in this 2×2 factorial study, in which all participants use the same data sets during scientific hypothesis–generation sessions and follow predetermined scripts. The clinical researchers are separated into experienced or inexperienced groups based on predetermined criteria and are then randomly assigned into groups that use and do not use VIADS via block randomization. The study sessions, screen activities, and audio recordings of participants are captured. Participants use the think-aloud protocol during the study sessions. After each study session, every participant is given a follow-up survey, with participants using VIADS completing an additional modified System Usability Scale survey. A panel of clinical research experts will assess the scientific hypotheses generated by participants based on predeveloped metrics. All data will be anonymized, transcribed, aggregated, and analyzed. Results Data collection for this study began in July 2021. Recruitment uses a brief online survey. The preliminary results showed that study participants can generate a few to over a dozen scientific hypotheses during a 2-hour study session, regardless of whether they used VIADS or other analytics tools. A metric to more accurately, comprehensively, and consistently assess scientific hypotheses within a clinical research context has been developed. Conclusions The scientific hypothesis–generation process is an advanced cognitive activity and a complex process. Our results so far show that clinical researchers can quickly generate initial scientific hypotheses based on data sets and prior experience. However, refining these scientific hypotheses is a much more time-consuming activity. To uncover the fundamental mechanisms underlying the generation of scientific hypotheses, we need breakthroughs that can capture thinking processes more precisely. International Registered Report Identifier (IRRID) DERR1-10.2196/39414

https://jmir.org/api/download?alt_name=resprot_v11i7e39414_app1.pdf&filename=1f00dae88feabfdc21f050179845ae15.pdf

The Roles of a Secondary Data Analytics Tool and Experience in Scientific Hypothesis Generation in Clinical Research: Protocol for a Mixed Methods Study

We hypothesize that the functionality of electronic health records could be improved with the addition of formal representations of clinicians' cognitive processes, including such things as the interpretation and synthesis of patient findings and the rational for diagnostic and therapeutic decisions. We carried out a four-phase analysis of clinical case studies to characterize how such processes are represented through relationships between clinical terms. The result is an terminology of 26 relationships that were validated against published clinical cases with 85.4% interrater reliability. We believe that capturing patient-specific information with these relationships can lead to improvements in clinical decision support systems, information retrieval and learning health systems.

An Exploration of the Terminology of Clinical Cognition and Reasoning.

Controlled medical vocabularies are useful in application areas such as medical information-systems and decision-support. However, such vocabularies are large and complex, and working with them can be daunting. It is important to provide a means for orienting users to the vocabulary's contents. This paper introduces a methodology for partitioning a vocabulary into small, meaningful pieces. The partitioning is done with respect to the vocabulary's IS-A hierarchy. The methodology, based on a set of rules for refining the IS-A hierarchy, is a process carried out by a user in conjunction with the computer. The methodology is demonstrated on a complex portion of a vocabulary.

James J. Cimino

Papers

Conceptual Guidance in Information Retrieval

Use of Wireless Technology for Reducing Medical Errors

The Roles of a Secondary Data Analytics Tool and Experience in Scientific Hypothesis Generation in Clinical Research: Protocol for a Mixed Methods Study

An Exploration of the Terminology of Clinical Cognition and Reasoning.

Partitioning a vocabulary's IS-A hierarchy into trees.