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.

OBJECTIVE
To assess physicians' perceptions about integrated displays for chart review based on a formal representation of patients' care context.


METHODS
We iteratively designed a conceptual prototype of an integrated patient summary and conducted an online survey with a multi-specialty panel of outpatient physicians from a large health system to collect their perceptions of the usefulness of our prototype. Survey questions were responded with a 7-point Likert scale and include two open-ended questions for comments on challenges and suggestions related to electronic health record (EHR) navigation, with which a thematic analysis was performed.


RESULTS
Forty-nine physicians completed the survey. The usefulness of our integrated display was rated slightly positive, and respondents did not consider it confusing. Challenges related to EHR navigation frequently reported by physicians included the need to navigate between multiple functionalities and to manually search for relevant data. The most common suggestions were related to facilitating integration of data from multiple parts of the record to facilitate data visualization and comprehension.


CONCLUSION
Physicians' rating of usefulness was slightly positive, and several insights to improve EHR navigation were derived from their comments. More effective EHR navigation may be achieved through facilitating integration of data from multiple parts of the record to simplify data retrieval and synthesis.

Physicians' perceptions about a semantically integrated display for chart review: A Multi-Specialty survey

Hematologic data in the case records.

The US National Library of Medicine’s Biomedical Informatics Short Course ran from 1992 to 2017, most of that time at the Marine Biological Laboratory in Woods Hole, Massachusetts. Its intention was to provide physicians, medical librarians and others engaged in health care with a basic understanding of the major topics in informatics so that they could return to their home institutions as “change agents”. Over the years, the course provided week-long, intense, morning-to-night experiences for some 1,350 students, consisting of lectures and hands-on project development, taught by many luminaries in the field, not the least of which was Donald A.B. Lindberg M.D., who spoke on topics ranging from bioinformatics to national policy.

The biomedical informatics short course at Woods Hole/Georgia: Training to support institutional change

Polygenic risk scores (PRS) have improved in predictive performance supporting their use in clinical practice. Reduced predictive performance of PRS in diverse populations can exacerbate existing health disparities. The NHGRI-funded eMERGE Network is returning a PRS-based genome-informed risk assessment to 25,000 diverse adults and children. We assessed PRS performance, medical actionability, and potential clinical utility for 23 conditions. Standardized metrics were considered in the selection process with additional consideration given to strength of evidence in African and Hispanic populations. Ten conditions were selected with a range of high-risk thresholds: atrial fibrillation, breast cancer, chronic kidney disease, coronary heart disease, hypercholesterolemia, prostate cancer, asthma, type 1 diabetes, obesity, and type 2 diabetes. We developed a pipeline for clinical PRS implementation, used genetic ancestry to calibrate PRS mean and variance, created a framework for regulatory compliance, and developed a PRS clinical report. eMERGEs experience informs the infrastructure needed to implement PRS-based implementation in diverse clinical settings.

/pdf/selection-optimization-and-validation-of-ten-chronic-disease-2wxsnto0.pdf

Selection, optimization, and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse populations

Medication reconciliation and clinician-friendly recording of medication is an ongoing informatics challenge. Users often prefer unrestricted free-text entry. We sought to see if simple Natural Language Processing (NLP) methods can be used to convert real-life medication records into coded concepts without additional interface changes to clinicians. We used data from the National Institutes of Health Clinical Center (NIH CC). Outpatient medications are recorded by research nurses using a semi-structured form that does not alert for incorrect data entry. We present data on accuracy of the NLP pipeline and suggest improvements to RxNorm tools. Objective: To utilize simplified automatic NLP tools for drug entry harmonization. Materials and Methods: We used National Library of Medicine’s RxNorm API through the RxMix tool (http://rxnav.nlm.nih.gov/, accessed on 2014-01-30) to convert free-text medication entries into RxNorm concepts, namely RxNorm ingredients. Medication free text entries were first preprocessed with regular expressions to extract the drug names, drug strength, and drug forms from the original string that also included dosing and frequency information. The resulting simplified string was processed using the function getApproxMatch to find matching concepts in RxNorm (RxCUIs). The RxCUI was then processed using the getAllRelatedInfo function to obtain the ingredient and additional information. We used the concept match score and rank provided by the getApproxMatch function to further consider only well-matched concepts. For example, from the original input string: “Metronidazole 250 mg , 2 tablet , PO , Every 12 hours.”, we first extract the drug entity “metronidazole 250 mg”, which maps to the RxNorm concept “Metronidazole 250 MG” (316300) with a score of 100. Its ingredient is “Metronidazole” (6922). The remainder of the input string drug dose and frequency can be used to estimate drug doses or as part of a free text drug entry system automatic fill option. Results: The original 43,303 medication strings were preprocessed into distinct 9,466 drug input strings. A total of 5,680 (60%) strings mapped to either RxNorm Ingredient (IN) concept or Clinical Drug (SCD) or both. Online appendix at http://dx.doi.org/10.6084/m9.figshare.960060 shows complete numerical results together with the impact on the string mapping counts when score thresholds are used to classify strings into two scenarios: (A) likely correction to a single target concept (score 80-100; single misspelled character: “atenlol” to “atenolol”) or (B) nurse may be asked to pick the best term from several matching contexts (score 50-79; multiple misspellings). A total of 502 concepts had either no matches or a score of less than 50 most of which are not ingredients or meaningless strings eg: “vitamin juice”, “zyrec”. Evaluation: The evaluation of the automatic mapping was done by one of the authors (clinician LMR). The unmatched set of strings was analyzed to detect those that should have found a match and did not. Two mapping errors were found: “codeine” and “chlorpheniramine” mapped to two unrelated ingredient names with the highest score of 100. (These errors were reported to and corrected by the RxMix team). Overall, the method had a 0.99 precision, 0.97 recall and 0.98 F1-measure. The manual evaluation was performed by only one clinician. Conclusions: The results demonstrate that simple NLP methods that use the existing free RxNorm API can be used to convert free text medications records into RxNorm concepts. Coded concepts offer researchers better data queries that can utilize drug class hierarchies and outperform the existing free-text search. The quality of the mapping using the automated string matching is excellent provided the total number of input strings with only two mapping errors (employing also the above score filters). Although the approximate matching function of RxMix was primarily developed to support matching of clinical drug names, not ingredients alone, we found it useful in the context of this experiment where our input data had various degrees of specification. References 1. Zhou L, Plasek JM, Mahoney LM, Karipineni N, Chang F, Yan X, et al. Using Medical Text Extraction, Reasoning and Mapping System (MTERMS) to process medication information in outpatient clinical notes. AMIA Annu Symp Proc AMIA Symp AMIA Symp. 2011;2011:1639–48. AMIA Annu Symp Proc 2014:1565.

James J. Cimino

Papers

Physicians' perceptions about a semantically integrated display for chart review: A Multi-Specialty survey

Hematologic data in the case records.

The biomedical informatics short course at Woods Hole/Georgia: Training to support institutional change

Selection, optimization, and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse populations

Automatic coding of Free-Text Medication Data recorded by Research Coordinators.