Methods of Information in Medicine

Objectives The TMF (Technology, Methods, and Infrastructure for Networked Medical Research) Data Protection Guide (TMF-DP) makes path-breaking recommendations on the subject of data protection in research projects. It includes comprehensive requirements for applications such as patient lists, pseudonymisation services and consent management services. Nevertheless, it lacks a structured, categorised list of requirements for simplified application in research projects and systematic evaluation. The DFG-founded 3LGM2IHE project ("Three-layer Graph-based meta model - Integrating the Healthcare Enterprise (IHE)") aims to define modeling paradigms and implement modeling tools for planning healthcare information systems...

BACKGROUND:  Breast cancer is the most prevailing heterogeneous disease among females characterized with distinct molecular subtypes and varied clinicopathological features. With the emergence of various artificial intelligence techniques especially machine learning, the breast cancer research has attained new heights in cancer detection and prognosis. OBJECTIVE:  Recent development in computer driven diagnostic system has enabled the clinicians to improve the accuracy in detecting various types of breast tumors...

INTRODUCTION:  Clinical procedures are often performed in outpatient clinics without prior scheduling at the administrative level, and documentation of the procedure often occurs solely in free-text clinical electronic notes. Natural language processing (NLP), particularly named entity recognition (NER), may provide a solution to extracting procedure data from free-text electronic notes. METHODS:  Free-text notes from outpatient ophthalmology visits were collected from the electronic clinical records at a single institution over 3 months...

OBJECTIVE:  The aim of the study is to identify the important clinical variables found in both pregnant and non-pregnant women who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, using an artificial intelligence (AI) platform. MATERIALS AND METHODS:  This was a retrospective cohort study of all women between the ages of 18 to 45, who were admitted to Maimonides Medical Center between March 10, 2020 and December 20, 2021...

BACKGROUND: One Digital Health (ODH) aims to propose a framework that merges One Health's and Digital Health's specific features into an innovative landscape. FAIR (Findable, Accessible, Interoperable, and Reusable) principles consider applications and computational agents (or in other terms, data, metadata, and infrastructures) as stakeholders with the capacity to find, access, interoperate, and reuse data with none or minimal human intervention. OBJECTIVES: This paper aims to elicit how the One Digital Health framework is compliant with FAIR principles and metrics, providing some thinking guide to investigate and define whether adapted metrics need to be figured out for an effective ODH Intervention setup...

OBJECTIVE: To provide high-quality data for COVID-19 research, we validated derived COVID-19 clinical indicators and 22 associated machine learning phenotypes, in the Mass General Brigham (MGB) COVID-19 Data Mart. MATERIALS AND METHODS: Fifteen reviewers performed a retrospective manual chart review for 150 COVID-19 positive patients in the data mart. To support rapid chart review for a wide range of target data, we offered a Natural Language Processing (NLP)-based chart review tool, the Digital Analytic Patient Reviewer (DAPR)...

BACKGROUND: Allergy is a hypersensitive reaction that occurs when the allergen reacts with the immune system. The prevalence and severity of the allergies are uprising in South Asian countries. Allergy often occurs in combinations which becomes difficult for physicians to diagnose. OBJECTIVES: This work aims to develop a decision-making model which aids physicians in diagnosing allergy comorbidities. The model intends to not only provide rational decisions, but also explainable knowledge about all alternatives...

BACKGROUND: Health care services are increasingly being digitized, but extant literature shows that digital technologies and applications are often developed without careful consideration of user needs. Research is needed to identify and investigate best-in-class methods to support user-centered design of mHealth applications. OBJECTIVES: The article investigates how the Kano model can be adapted and used for the purpose of eliciting child patients' information needs during the design phase of mHealth application development...

BACKGROUND: Though the process mining algorithms have evolved in the past decade, the lack of attention to extracting event logs from raw data of databases in an automatic manner is evident. These logs are available in a process-oriented manner in the process-aware information systems. Still, there are areas where their extraction is a challenge to address (e.g., trauma registries). OBJECTIVE: The registry data are recorded manually and follow an unstructured ad-hoc pattern; prone to high noises and errors; consequently, registry logs are classified at a maturity level of one, and extracting process-centric information is not a trivial task therein...

BACKGROUND: In recent years, researchers have used many computerized interventions to reduce medical errors, the third cause of death in developed countries. One of such interventions is using differential diagnosis generators in primary care, where physicians may encounter initial symptoms without any diagnostic presuppositions. These systems generate multiple diagnoses, ranked by their likelihood. As such, these reports' accuracy can be determined by the location of the correct diagnosis in the list...

BACKGROUND: Generative pre-trained transformer (GPT) models are one of the latest large pre-trained natural language processing (NLP) models, which enables model training with limited datasets, and reduces dependency on large datasets which are scarce and costly to establish and maintain. There is a rising interest to explore the use of GPT models in healthcare. OBJECTIVE: We investigate the performance of GPT-2 and GPT-Neo models for medical text prediction using 374,787 free-text dental notes...

BACKGROUND: A large volume of heavily fragmented data is generated daily in different healthcare contexts and is stored using various structures with different semantics. This fragmentation and heterogeneity make secondary use of data a challenge. Data integration approaches that derive a common data model from sources or requirements have some advantages. However, these approaches are often built for a specific application where the research questions are known. Thus, the semantic and structural reconciliation is often not reusable nor reproducible...

Background At present, artificial intelligence (AI) is incrementally used in clinical data analysis and clinical decision-making. Dental cusp angulation provide valuable insight into chewing efficiency and prosthesis safety issues. AI-enable computing cusp angles have potential important value but there is no reliable digital measurement method at present. Objectives To establish a digital method for measuring cusp angles and investigate the inter-rater and intra-rater reliability. Methods Two cusp angles (angle α and angle β) of the first molar were measured on 21 plaster casts using a goniometer, and on their corresponding digital models using PicPick software after scanning with a CEREC Bluecam three-dimensional (3D) intraoral scanner...

BACKGROUND: Receiver operating characteristic (ROC) analysis is commonly used for comparing models and humans; however, the exact analytical techniques vary and some are flawed. OBJECTIVES: The aim of the study is to identify common flaws in ROC analysis for human versus model performance, and address them. METHODS: We review current use and identify common errors. We also review the ROC analysis literature for more appropriate techniques...

BACKGROUND: Management of child healthcare can be negatively affected by incomplete recording, low data quality, and lack of data integration of health management information system (HMIS) to support decision making and public health program needs. Given the importance of identifying key determinants of child health via capturing and integrating accurate and high-quality information, we aim to address this gap through the development and testing requirements for an integrated child health information system...

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INTRODUCTION: Pneumonia is caused by microbes that establish an infectious process in the lungs. The gold standard for pneumonia diagnosis is radiologist-documented pneumonia-related features in radiology notes that are captured in electronic health records in an unstructured format. OBJECTIVE: The study objective was to develop a methodological approach for assessing validity of a pneumonia diagnosis based on identifying presence or absence of key radiographic features in radiology reports with subsequent rendering of diagnostic decisions into a structured format...

BACKGROUND: Easy identification of immunocompromised hosts (ICH) would allow for stratification of culture results based on host type. METHODS: We utilized antimicrobial stewardship (ASP) team notes written during handshake stewardship rounds in the pediatric intensive care unit as the gold standard for host status; clinical notes from the primary team, medication orders during the encounter, problem list and billing diagnoses documented prior to the ASP documentation were extracted to develop models that predict host status...

BACKGROUND: International Classification of Disease (ICD) coding for pneumonia classification is based on causal organism or use of general pneumonia codes, creating challenges for epidemiological evaluations, where pneumonia is standardly subtyped by settings, exposures and time of emergence. Pneumonia subtype classification requires data available in electronic health records (EHR), frequently in non-structured formats including radiological interpretation or clinical notes that complicate electronic classification...

BACKGROUND:  It would be useful to be able to assess the utility of predictive models of continuous values before clinical trials are performed. OBJECTIVE:  The aim of the study is to compare metrics to assess the potential clinical utility of models that produce continuous value forecasts. METHODS:  We ran a set of data assimilation forecast algorithms on time series of glucose measurements from neurological intensive care unit patients...