Logical Rules and a Preliminary Prototype for Translating Mortality Coding Rules from ICD-10 to ICD-11

Iris is a system for coding multiple causes of death in ICD-10 and for the selection of the underlying cause of death, based on a knowledge base composed by a large number of rules. With the adoption of ICD-11, those rules need translation to ICD-11. A pre-project has been carried out to evaluate feasibility of transition to ICD-11, which included the analysis of the logical meta-rules needed for rule translation and development of a prototype support system for the expert that will translate the coding rules

Proceedings of the International Collaborative Effort on Automating Mortality Statistics, Volume II

Arialdi M. Minino and Harry M. Rosenberg, editors."September 2001.""Second plenary meeting of the International Collaborative Effort on Automating Mortality Statistics held in Bethesda, Maryland, September 7-10, 1999"--Pref.Sponsored by the Centers for Disease Control and Prevention, National Center for Health Statistics.Also available via the World Wide Web.Includes bibliographical references

Repeatable and reusable research - Exploring the needs of users for a Data Portal for Disease Phenotyping

Background: Big data research in the field of health sciences is hindered by a lack of agreement on how to identify and define different conditions and their medications. This means that researchers and health professionals often have different phenotype definitions for the same condition. This lack of agreement makes it hard to compare different study findings and hinders the ability to conduct repeatable and reusable research. Objective: This thesis aims to examine the requirements of various users, such as researchers, clinicians, machine learning experts, and managers, for both new and existing data portals for phenotypes (concept libraries). Methods: Exploratory sequential mixed methods were used in this thesis to look at which concept libraries are available, how they are used, what their characteristics are, where there are gaps, and what needs to be done in the future from the point of view of the people who use them. This thesis consists of three phases: 1) two qualitative studies, including one-to-one interviews with researchers, clinicians, machine learning experts, and senior research managers in health data science, as well as focus group discussions with researchers working with the Secured Anonymized Information Linkage databank, 2) the creation of an email survey (i.e., the Concept Library Usability Scale), and 3) a quantitative study with researchers, health professionals, and clinicians. Results: Most of the participants thought that the prototype concept library would be a very helpful resource for conducting repeatable research, but they specified that many requirements are needed before its development. Although all the participants stated that they were aware of some existing concept libraries, most of them expressed negative perceptions about them. The participants mentioned several facilitators that would encourage them to: 1) share their work, such as receiving citations from other researchers; and 2) reuse the work of others, such as saving a lot of time and effort, which they frequently spend on creating new code lists from scratch. They also pointed out several barriers that could inhibit them from: 1) sharing their work, such as concerns about intellectual property (e.g., if they shared their methods before publication, other researchers would use them as their own); and 2) reusing others' work, such as a lack of confidence in the quality and validity of their code lists. Participants suggested some developments that they would like to see happen in order to make research that is done with routine data more reproducible, such as the availability of a drive for more transparency in research methods documentation, such as publishing complete phenotype definitions and clear code lists. Conclusions: The findings of this thesis indicated that most participants valued a concept library for phenotypes. However, only half of the participants felt that they would contribute by providing definitions for the concept library, and they reported many barriers regarding sharing their work on a publicly accessible platform such as the CALIBER research platform. Analysis of interviews, focus group discussions, and qualitative studies revealed that different users have different requirements, facilitators, barriers, and concerns about concept libraries. This work was to investigate if we should develop concept libraries in Kuwait to facilitate the development of improved data sharing. However, at the end of this thesis the recommendation is this would be unlikely to be cost effective or highly valued by users and investment in open access research publications may be of more value to the Kuwait research/academic community

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review

Background: Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. Methodology: After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: 1) medical note classification, 2) clinical entity recognition, 3) text summarisation, 4) deep learning (DL) and transfer learning architecture, 5) information extraction, 6) Medical language translation and 7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Result and Discussion: EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. Conclusion: We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification

A Model for a Data Dictionary Supporting Multiple Definitions, Views and Contexts

Auf dem Gebiet der Klinischen Studien sind präzise Begriffsdefinitionen äußerst wichtig, um eine objektive Datenerfassung und -auswertung zu gewährleisten. Zudem ermöglichen sie externen Experten die Forschungsergebnisse korrekt zu interpretieren und anzuwenden. Allerdings weisen viele Klinische Studien Defizite in diesem Punkt auf: Definitionen sind oft ungenau oder werden implizit verwendet. Außerdem sind Begriffe oft uneinheitlich definiert, obwohl standardisierte Definitionen im Hinblick auf einen weitreichenderen Austausch von Ergebnissen wünschenswert sind. Vor diesem Hintergrund entstand die Idee des Data Dictionary, dessen Ziel zunächst darin besteht, die Definitionsalternativen von Begriffen zu sammeln und Klinischen Studien zur Verfügung zu stellen. Zusätzlich soll die Analyse der Definitionen in Bezug auf ihre Gemeinsamkeiten und Unterschiede sowie deren Harmonisierung unterstützt werden. Standardisierte Begriffsdefinitionen werden jedoch nicht erzwungen, da die Unterschiede in Definitionen inhaltlich gerechtfertigt sein können, z.B. aufgrund der Verwendung in unterschiedlichen Fachgebieten, durch studienspezifische Bedingungen oder verschiedene Expertensichten. In der vorliegenden Arbeit wird ein Modell für das Data Dictionary entwickelt. Das entwickelte Modell folgt dem aus der Terminologie bekannten konzept-basierten Ansatz und erweitert diesen um die Möglichkeit der Repräsentation alternativer Definitionen. Insbesondere wird hierbei angestrebt, die Unterschiede in den Definitionen möglichst genau zu explizieren, um zwischen inhaltlich verschiedenen Definitionsalternativen (z.B. sich wider-sprechenden Expertenmeinungen) und konsistenten Varianten einer inhaltlichen Definition (z.B. verschiedene Sichten, Übersetzungen in verschiedene Sprachen) unterscheiden zu können. Mehrere Modellelemente widmen sich zudem der Explizierung von kontextuellen Informationen (z.B. der Gültigkeit innerhalb von Organisationen oder der Domäne zu der ein Konzept gehört), um die Auswahl und Wiederverwendung von Definitionen zu unterstützen. Diese Informationen erlauben verschiedene Sichten auf die Inhalte des Data Dictionary. Sichten werden dabei als kohärente Teilmengen des Data Dictionary betrachtet, die nur diejenigen Inhalte umfassen, die als relevant im ausgewählten Kontext spezifiziert sind