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.

One of the challenges to using electronic health record (EHR) repositories for research is the difficulty mapping study subject eligibility criteria to the query capabilities of the repository We sought to characterize criteria as "easy" (searchable in a typical repository), "hard" (requiring manual review of the record data), and "impossible" (not typically available in EHR repositories) We obtained 292 criteria from 20 studies available from Clinical Trialsgov and rated them according to our three types, plus a fourth "mixed" type We had good agreement among three independent reviewers and chose 274 criteria that were characterized by single types for further analysis The resulting analysis showed typical features of criteria that do and don't map to repositories We propose that these features be used to guide researchers in specifying eligibility criteria to improve development of enrollment workflow, including the definition of EHR repository queries for self-service or analyst-mediated retrievals

Classification of Clinical Research Study Eligibility Criteria to Support Multi-Stage Cohort Identification Using Clinical Data Repositories

Physicians' perceptions about narrative note sections format and content: A multi-specialty survey.

DXplain is a computer-based medical information system designed to accept a patient's historical, physical, and laboratory findings and then generate a list of diseases which could explain some or all of the data. This paper describes recent updating of the database with special attention to the software techniques used increasingly to facilitate the process.

Updating the DXplain Database.

Clinical laboratory results are stored in electronic health records (EHRs) as structured data coded with local or standard terms. However, laboratory tests that are performed at outside laboratories are often simply labeled "outside test" or something similar, with the actual test name in a free-text result or comment field. After being aggregated into clinical data repositories, these ambiguous labels impede the retrieval of specific test results. We present a general multi-step solution that can facilitate the identification, standardization, reconciliation, and transformation of such test results. We applied our approach to data in the NIH Biomedical Translational Research Information System (BTRIS) to identify laboratory tests, map comment values to the LOINC codes that will be incorporated into our Research Entities Dictionary (RED), and develop a reference table that can be used in the EHR data extract-transform-load (ETL) process.

Identifying the Clinical Laboratory Tests from Unspecified "Other Lab Test" Data for Secondary Use.

Spoken question answering for clinical decision support is a potentially revolutionary technology for improving the efficiency and quality of health care delivery. This application involves many technologies currently being researched, including automatic speech recognition (ASR), information retrieval (IR), and summarization, all in the biomedical domain. In certain domains, the problem of spoken document retrieval has been declared solved because of the robustness of IR to ASR errors. This study investigates the extent to which spoken medical question answering benefits from that same robustness. We used the best results from previous speech recognition experiments as inputs to a clinical question answering system, and had physicians perform blind evaluations of results generated both by ASR transcripts of questions and gold standard transcripts of the same questions. Our results suggest that the medical domain differs enough from the open domain to require additional work in automatic speech recognition adapted for the biomedical domain.

James J. Cimino

Papers

Classification of Clinical Research Study Eligibility Criteria to Support Multi-Stage Cohort Identification Using Clinical Data Repositories

Physicians' perceptions about narrative note sections format and content: A multi-specialty survey.

Updating the DXplain Database.

Identifying the Clinical Laboratory Tests from Unspecified "Other Lab Test" Data for Secondary Use.

An Investigation into the Feasibility of Spoken Clinical Question Answering