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Development and Validation of a Natural Language Processing Algorithm to Pseudonymize Documents in the Context of a Clinical Data Warehouse Methods Inf. Med. (IF 1.7) Pub Date : 2024-03-05 Xavier Tannier, Perceval Wajsbürt, Alice Calliger, Basile Dura, Alexandre Mouchet, Martin Hilka, Romain Bey
Objective The objective of this study is to address the critical issue of deidentification of clinical reports to allow access to data for research purposes, while ensuring patient privacy. The study highlights the difficulties faced in sharing tools and resources in this domain and presents the experience of the Greater Paris University Hospitals (AP-HP for Assistance Publique-Hôpitaux de Paris) in
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Use of Natural Language Processing to Identify Sexual and Reproductive Health Information in Clinical Text Methods Inf. Med. (IF 1.7) Pub Date : 2024-02-20 Elizabeth I. Harrison, Laura A. Kirkpatrick, Patrick W. Harrison, Traci M. Kazmerski, Yoshimi Sogawa, Harry S. Hochheiser
Objectives This study aimed to enable clinical researchers without expertise in natural language processing (NLP) to extract and analyze information about sexual and reproductive health (SRH), or other sensitive health topics, from large sets of clinical notes. Methods (1) We retrieved text from the electronic health record as individual notes. (2) We segmented notes into sentences using one of scispaCy's
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Artificial Intelligence-Based Prediction of Contrast Medium Doses for Computed Tomography Angiography Using Optimized Clinical Parameter Sets Methods Inf. Med. (IF 1.7) Pub Date : 2024-01-23 Marja Fleitmann, Hristina Uzunova, René Pallenberg, Andreas M. Stroth, Jan Gerlach, Alexander Fürschke, Jörg Barkhausen, Arpad Bischof, Heinz Handels
Objectives In this paper, an artificial intelligence-based algorithm for predicting the optimal contrast medium dose for computed tomography (CT) angiography of the aorta is presented and evaluated in a clinical study. The prediction of the contrast dose reduction is modelled as a classification problem using the image contrast as the main feature. Methods This classification is performed by random
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A Proposal for a Robust Validated Weighted General Data Protection Regulation-Based Scale to Assess the Quality of Privacy Policies of Mobile Health Applications: An eDelphi Study Methods Inf. Med. (IF 1.7) Pub Date : 2023-12-22 Jaime Benjumea, Jorge Ropero, Enrique Dorronzoro-Zubiete, Octavio Rivera-Romero, Alejandro Carrasco
Background Health care services are undergoing a digital transformation in which the Participatory Health Informatics field has a key role. Within this field, studies aimed to assess the quality of digital tools, including mHealth apps, are conducted. Privacy is one dimension of the quality of an mHealth app. Privacy consists of several components, including organizational, technical, and legal safeguards
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Performance Characteristics of a Rule-Based Electronic Health Record Algorithm to Identify Patients with Gross and Microscopic Hematuria Methods Inf. Med. (IF 1.7) Pub Date : 2023-10-06 Jasmine Kashkoush, Mudit Gupta, Matthew A. Meissner, Matthew E. Nielsen, H. Lester Kirchner, Tullika Garg
Background Two million patients per year are referred to urologists for hematuria, or blood in the urine. The American Urological Association recently adopted a risk-stratified hematuria evaluation guideline to limit multi-phase computed tomography to individuals at highest risk of occult malignancy. Objectives To understand population-level hematuria evaluations, we developed an algorithm to accurately
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An Exploratory Study on the Utility of Patient-Generated Health Data as a Tool for Health Care Professionals in Multiple Sclerosis Care Methods Inf. Med. (IF 1.7) Pub Date : 2023-09-25 Sharon Guardado, Vasiliki Mylonopoulou, Octavio Rivera-Romero, Nadine Patt, Jens Bansi, Guido Giunti
Background Patient-generated health data (PGHD) are data collected through technologies such as mobile devices and health apps. The integration of PGHD into health care workflows can support the care of chronic conditions such as multiple sclerosis (MS). Patients are often willing to share data with health care professionals (HCPs) in their care team; however, the benefits of PGHD can be limited if
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Machine Learning Classification of Psychiatric Data Associated with Compensation Claims for Patient Injuries Methods Inf. Med. (IF 1.7) Pub Date : 2023-07-24 Martti Juhola, Tommi Nikkanen, Juho Niemi, Maiju Welling, Olli Kampman
Background Adverse events are common in health care. In psychiatric treatment, compensation claims for patient injuries appear to be less common than in other medical specialties. The most common types of patient injury claims in psychiatry include diagnostic flaws, unprevented suicide, or coercive treatment deemed as unnecessary or harmful. Objectives The objective was to study whether it is possible
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From Paper Files to Web-Based Application for Data-Driven Monitoring of HIV Programs: Nigeria's Journey to a National Data Repository for Decision-Making and Patient Care Methods Inf. Med. (IF 1.7) Pub Date : 2023-05-29 Ibrahim Dalhatu, Chinedu Aniekwe, Adebobola Bashorun, Alhassan Abdulkadir, Emilio Dirlikov, Stephen Ohakanu, Oluwasanmi Adedokun, Ademola Oladipo, Ibrahim Jahun, Lisa Murie, Steven Yoon, Mubarak G. Abdu-Aguye, Ahmed Sylvanus, Samuel Indyer, Isah Abbas, Mustapha Bello, Nannim Nalda, Matthias Alagi, Solomon Odafe, Sylvia Adebajo, Otse Ogorry, Murphy Akpu, Ifeanyi Okoye, Kunle Kakanfo, Amobi Andrew Onovo
Background Timely and reliable data are crucial for clinical, epidemiologic, and program management decision making. Electronic health information systems provide platforms for managing large longitudinal patient records. Nigeria implemented the National Data Repository (NDR) to create a central data warehouse of all people living with human immunodeficiency virus (PLHIV) while providing useful functionalities
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Rare Diseases in Hospital Information Systems—An Interoperable Methodology for Distributed Data Quality Assessments Methods Inf. Med. (IF 1.7) Pub Date : 2023-05-16 Kais Tahar, Tamara Martin, Yongli Mou, Raphael Verbuecheln, Holm Graessner, Dagmar Krefting
Background Multisite research networks such as the project “Collaboration on Rare Diseases” connect various hospitals to obtain sufficient data for clinical research. However, data quality (DQ) remains a challenge for the secondary use of data recorded in different health information systems. High levels of DQ as well as appropriate quality assessment methods are needed to support the reuse of such
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A Simple-to-Use R Package for Mimicking Study Data by Simulations Methods Inf. Med. (IF 1.7) Pub Date : 2023-04-11 Giorgos Koliopanos, Francisco Ojeda, Andreas Ziegler
Background Data protection policies might prohibit the transfer of existing study data to interested research groups. To overcome legal restrictions, simulated data can be transferred that mimic the structure but are different from the existing study data. Objectives The aim of this work is to introduce the simple-to-use R package Mock Data Generation (modgo) that may be used for simulating data from
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Workflow, Time Requirement, and Quality of Medication Documentation with or without a Computerized Physician Order Entry System—A Simulation-Based Lab Study Methods Inf. Med. (IF 1.7) Pub Date : 2023-04-05 Viktoria Jungreithmayr, Walter E. Haefeli, Hanna M. Seidling, and Implementation Team
Background The introduction of a computerized physician order entry (CPOE) system is changing workflows and redistributing tasks among health care professionals. Objectives The aim of this study is to describe exemplary changes in workflow, to objectify the time required for medication documentation, and to evaluate documentation quality with and without a CPOE system (Cerner® i.s.h.med). Methods Workflows
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An Alternative Application of Natural Language Processing to Express a Characteristic Feature of Diseases in Japanese Medical Records Methods Inf. Med. (IF 1.7) Pub Date : 2023-04-04 Yoshinori Yamanouchi, Taishi Nakamura, Tokunori Ikeda, Koichiro Usuku
Background Owing to the linguistic situation, Japanese natural language processing (NLP) requires morphological analyses for word segmentation using dictionary techniques. Objective We aimed to clarify whether it can be substituted with an open-end discovery-based NLP (OD-NLP), which does not use any dictionary techniques. Methods Clinical texts at the first medical visit were collected for comparison
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Defining and Scoping Participatory Health Informatics: An eDelphi Study Methods Inf. Med. (IF 1.7) Pub Date : 2023-03-14 Kerstin Denecke, Octavio Rivera Romero, Carolyn Petersen, Marge Benham-Hutchins, Miguel Cabrer, Shauna Davies, Rebecca Grainger, Rada Hussein, Guillermo Lopez-Campos, Fernando Martin-Sanchez, Mollie McKillop, Mark Merolli, Talya Miron-Shatz, Jesús Daniel Trigo, Graham Wright, Rolf Wynn, Carol Hullin Lucay Cossio, Elia Gabarron
Background Health care has evolved to support the involvement of individuals in decision making by, for example, using mobile apps and wearables that may help empower people to actively participate in their treatment and health monitoring. While the term “participatory health informatics” (PHI) has emerged in literature to describe these activities, along with the use of social media for health purposes
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Evaluating the Impact of Health Care Data Completeness for Deep Generative Models Methods Inf. Med. (IF 1.7) Pub Date : 2023-03-10 Benjamin Smith, Senne Van Steelandt, Anahita Khojandi
Background Deep generative models (DGMs) present a promising avenue for generating realistic, synthetic data to augment existing health care datasets. However, exactly how the completeness of the original dataset affects the quality of the generated synthetic data is unclear. Objectives In this paper, we investigate the effect of data completeness on samples generated by the most common DGM paradigms
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High-Quality Data for Health Care and Health Research. Methods Inf. Med. (IF 1.7) Pub Date : 2023-03-02 Jürgen Stausberg,Sonja Harkener
In the 19th century, Florence Nightingale pointed to the importance of nursing documentation for the care of patients and the necessity of data-based statistics for quality improvement. The same century, John Snow projected his observations about patients with Cholera on a street map, laying the ground for modern epidemiological science. The historical examples demonstrate that proper data are the
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Prehospital Cardiac Arrest Should be Considered When Evaluating Coronavirus Disease 2019 Mortality in the United States Methods Inf. Med. (IF 1.7) Pub Date : 2023-02-27
Background Public health emergencies leave little time to develop novel surveillance efforts. Understanding which preexisting clinical datasets are fit for surveillance use is of high value. Coronavirus disease 2019 (COVID-19) offers a natural applied informatics experiment to understand the fitness of clinical datasets for use in disease surveillance. Objectives This study evaluates the agreement
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Evaluation of Feature Selection Methods for Preserving Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine Methods Inf. Med. (IF 1.7) Pub Date : 2023-02-22 Joshua Lemmon, Lin Lawrence Guo, Jose Posada, Stephen R. Pfohl, Jason Fries, Scott Lanyon Fleming, Catherine Aftandilian, Nigam Shah, Lillian Sung
Background Temporal dataset shift can cause degradation in model performance as discrepancies between training and deployment data grow over time. The primary objective was to determine whether parsimonious models produced by specific feature selection methods are more robust to temporal dataset shift as measured by out-of-distribution (OOD) performance, while maintaining in-distribution (ID) performance
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Aligning Semantic Interoperability Frameworks with the FOXS Stack for FAIR Health Data Methods Inf. Med. (IF 1.7) Pub Date : 2023-02-13 John Meredith, Nik Whitehead, Michael Dacey
Background FAIR Guiding Principles present a synergy with the use cases for digital health records, in that clinical data need to be found, accessible within a range of environments, and data must interoperate between systems and subsequently reused. The use of HL7 FHIR, openEHR, IHE XDS, and SNOMED CT (FOXS) together represents a specification to create an open digital health platform for modern health
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Trans-O-MIM—An International Research Project on Open Access Transformation: Outcomes and Lessons Learned Methods Inf. Med. (IF 1.7) Pub Date : 2023-02-07 Reinhold Haux, Esther Greussing, Stefanie Kuballa, Corinna Mielke, Mareike Schulze, Monika Taddicken
Background During the last decades, the Open Access paradigm has become an important approach for publishing new scientific knowledge. From 2015 to 2020, the Trans-O-MIM research project was undertaken with the intention to identify and to explore solutions in transforming subscription-based journals into Open Access journals. Trans-O-MIM stands for strategies, models, and evaluation metrics for the
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Consistency as a Data Quality Measure for German Corona Consensus Items Mapped from National Pandemic Cohort Network Data Collections Methods Inf. Med. (IF 1.7) Pub Date : 2023-01-30 Khalid O. Yusuf, Olga Miljukov, Anne Schoneberg, Sabine Hanß, Martin Wiesenfeldt, Melanie Stecher, Lazar Mitrov, Sina Marie Hopff, Sarah Steinbrecher, Florian Kurth, Thomas Bahmer, Stefan Schreiber, Daniel Pape, Anna-Lena Hofmann, Mirjam Kohls, Stefan Störk, Hans Christian Stubbe, Johannes J. Tebbe, Johannes C. Hellmuth, Johanna Erber, Lilian Krist, Siegbert Rieg, Lisa Pilgram, Jörg J. Vehreschild
Background As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from coronavirus disease 2019 (COVID-19) patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19
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Data Quality in Health Care: Main Concepts and Assessment Methodologies Methods Inf. Med. (IF 1.7) Pub Date : 2023-01-30 Mehrnaz Mashoufi, Haleh Ayatollahi, Davoud Khorasani-Zavareh, Tahere Talebi Azad Boni
Introduction In the health care environment, a huge volume of data is produced on a daily basis. However, the processes of collecting, storing, sharing, analyzing, and reporting health data usually face with numerous challenges that lead to producing incomplete, inaccurate, and untimely data. As a result, data quality issues have received more attention than before. Objective The purpose of this article
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Targeted Data Quality Analysis for a Clinical Decision Support System for SIRS Detection in Critically Ill Pediatric Patients Methods Inf. Med. (IF 1.7) Pub Date : 2023-01-11
Background Data quality issues can cause false decisions of clinical decision support systems (CDSSs). Analyzing local data quality has the potential to prevent data quality-related failure of CDSS adoption. Objectives To define a shareable set of applicable measurement methods (MMs) for a targeted data quality assessment determining the suitability of local data for our CDSS. Methods We derived task-specific
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Definition of a Practical Taxonomy for Referencing Data Quality Problems in Health Care Databases Methods Inf. Med. (IF 1.7) Pub Date : 2023-01-09
Introduction Health care information systems can generate and/or record huge volumes of data, some of which may be reused for research, clinical trials, or teaching. However, these databases can be affected by data quality problems; hence, an important step in the data reuse process consists in detecting and rectifying these issues. With a view to facilitating the assessment of data quality, we developed
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Nurse Managers' Opinions of Information System Support for Performance Management: A Correlational Study Methods Inf. Med. (IF 1.7) Pub Date : 2023-01-09 Kaija Saranto, Samuli Koponen, Tuulikki Vehko, Eija Kivekäs
Background Current information systems do not effectively support nurse managers' duties, such as reporting, resource management, and assessing clinical performance. Few performance management information systems are available and features in many are scattered. Objectives The purpose of the study was to determine nurse managers' opinions of information system support for performance management. Methods An
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Information Technology Systems for Infection Control in German University Hospitals—Results of a Structured Survey a Year into the Severe Acute Respiratory Syndrome Coronavirus 2 Pandemic Methods Inf. Med. (IF 1.7) Pub Date : 2023-01-09 Nicolás Reinoso Schiller, Martin Wiesenfeldt, Ulrike Loderstädt, Hani Kaba, Dagmar Krefting, Simone Scheithauer
Background Digitalization is playing a major role in mastering the current coronavirus 2019 (COVID-19) pandemic. However, several outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in German hospitals last year have shown that many of the surveillance and warning mechanisms related to infection control (IC) in hospitals need to be updated. Objectives The main objective of the
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We Know What You Agreed To, Don't We?—Evaluating the Quality of Paper-Based Consents Forms and Their Digitalized Equivalent Using the Example of the Baltic Fracture Competence Centre Project Methods Inf. Med. (IF 1.7) Pub Date : 2023-01-09 Henriette Rau, Dana Stahl, Anna-Juliana Reichel, Martin Bialke, Thomas Bahls, Wolfgang Hoffmann
Introduction The informed consent is the legal basis for research with human subjects. Therefore, the consent form (CF) as legally binding document must be valid, that is, be completely filled-in stating the person's decision clearly and signed by the respective person. However, especially paper-based CFs might have quality issues and the transformation into machine-readable information could add to
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Automatic Identification of Self-Reported COVID-19 Vaccine Information from Vaccine Adverse Events Reporting System Methods Inf. Med. (IF 1.7) Pub Date : 2023-01-09 Jay S. Patel, Sonya Zhan, Zasim Siddiqui, Bari Dzomba, Huanmei Wu
Background The short time frame between the coronavirus disease 2019 (COVID-19) pandemic declaration and the vaccines authorization led to concerns among public regarding the safety and efficacy of the vaccines. The Food and Drug Administration uses the Vaccine Adverse Events Reporting System (VAERS) where general population can report their vaccine side effects in the text box. This information could
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Synthetic Tabular Data Evaluation in the Health Domain Covering Resemblance, Utility, and Privacy Dimensions Methods Inf. Med. (IF 1.7) Pub Date : 2023-01-09 Mikel Hernadez, Gorka Epelde, Ane Alberdi, Rodrigo Cilla, Debbie Rankin
Background Synthetic tabular data generation is a potentially valuable technology with great promise for data augmentation and privacy preservation. However, prior to adoption, an empirical assessment of generated synthetic tabular data is required across dimensions relevant to the target application to determine its efficacy. A lack of standardized and objective evaluation and benchmarking strategy
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Multivariate Sequential Analytics for Cardiovascular Disease Event Prediction Methods Inf. Med. (IF 1.7) Pub Date : 2022-12-23 William Hsu, Jim Warren, Patricia Riddle
Background Automated clinical decision support for risk assessment is a powerful tool in combating cardiovascular disease (CVD), enabling targeted early intervention that could avoid issues of overtreatment or undertreatment. However, current CVD risk prediction models use observations at baseline without explicitly representing patient history as a time series. Objective The aim of this study is to
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The Leipzig Health Atlas—An Open Platform to Present, Archive, and Share Biomedical Data, Analyses, and Models Online Methods Inf. Med. (IF 1.7) Pub Date : 2022-12-23 Toralf Kirsten, Frank A. Meineke, Henry Loeffler-Wirth, Christoph Beger, Alexandr Uciteli, Sebastian Stäubert, Matthias Löbe, René Hänsel, Franziska G. Rauscher, Judith Schuster, Thomas Peschel, Heinrich Herre, Jonas Wagner, Silke Zachariae, Christoph Engel, Markus Scholz, Erhard Rahm, Hans Binder, Markus Loeffler, on behalf of the LHA team
Background Clinical trials, epidemiological studies, clinical registries, and other prospective research projects, together with patient care services, are main sources of data in the medical research domain. They serve often as a basis for secondary research in evidence-based medicine, prediction models for disease, and its progression. This data are often neither sufficiently described nor accessible
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The Digital Analytic Patient Reviewer (DAPR) for COVID-19 Data Mart Validation Methods Inf. Med. (IF 1.7) Pub Date : 2022-12-20
Objective To provide high-quality data for coronavirus disease 2019 (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. 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
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3LGM2IHE: Requirements for Data-Protection-Compliant Research Infrastructures—A Systematic Comparison of Theory and Practice-Oriented Implementation Methods Inf. Med. (IF 1.7) Pub Date : 2022-12-15 Robert Gött, Sebastian Stäubert, Alexander Strübing, Alfred Winter, Angela Merzweiler, Björn Bergh, Knut Kaulke, Thomas Bahls, Wolfgang Hoffmann, Martin Bialke
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, pseudonymization services, and consent management services. Nevertheless, it lacks a structured, categorized
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FAIR Aspects of a Health Information Protection and Management System Methods Inf. Med. (IF 1.7) Pub Date : 2022-12-09
Background Privacy management is a key issue when dealing with storage and distribution of health information. However, FAIR (Findability, Accessibility, Interoperability, and Reusability) principles when sharing information are in increasing demand in several organizations, especially for information generated in public-funded research projects. Objectives The two main objectives of the presented
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One Digital Health for more FAIRness Methods Inf. Med. (IF 1.7) Pub Date : 2022-12-03 Oscar Tamburis, Arriel Benis
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
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Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records Methods Inf. Med. (IF 1.7) Pub Date : 2022-11-22
Objective Our objective was to phenotype periodontal disease (PD) diagnoses from three different sections (diagnosis codes, clinical notes, and periodontal charting) of the electronic dental records (EDR) by developing two automated computer algorithms. Methods We conducted a retrospective study using EDR data of patients (n = 27,138) who received care at Temple University Maurice H. Kornberg School
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An Intelligent Medical Isolation Observation Management System Based on the Internet of Things Methods Inf. Med. (IF 1.7) Pub Date : 2022-11-15 Wensheng Sun, Chunmei Wang, Jimin Sun, Ziping Miao, Feng Ling, Guangsong Wu
Background Since COVID-19 (coronavirus disease 2019) was discovered in December 2019, it has spread worldwide. Early isolation and medical observation management of cases and their close contacts are the key to controlling the spread of the epidemic. However, traditional medical observation requires medical staff to measure body temperature and other vital signs face to face and record them manually
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Maturity Level of Digital Reproductive, Maternal, Newborn, and Child Health Initiatives in Jordan and Palestine Methods Inf. Med. (IF 1.7) Pub Date : 2022-11-15 Mohammad S. Alyahya, Niveen M. E. Abu-Rmeileh, Yousef S. Khader, Maysaa Nemer, Nihaya A. Al-Sheyab, Alexandrine Pirlot de Corbion, Laura Lazaro Cabrera, Sundeep Sahay
Background While there is a rapid increase in digital health initiatives focusing on the processing of personal data for strengthening the delivery of reproductive, maternal, newborn, and child health (RMNCH) services in fragile settings, these are often unaccompanied at both the policy and operational levels with adequate legal and regulatory frameworks. Objective The main aim was to understand the
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Using an ontology to derive a sharable and interoperable relational data model for heterogeneous healthcare data and various applications Methods Inf. Med. (IF 1.7) Pub Date : 2022 Christina Khnaisser, Luc Lavoie, Benoit Fraikin, Adrien Barton, Samuel Dussault, Anita Burgun, Jean-François Ethier
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
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Self-service registry log builder: A case study in national trauma registry of Iran Methods Inf. Med. (IF 1.7) Pub Date : 2022 Mansoureh Yari eili, Safar Vafadar, Jalal Rezaeenour, Mahdi Sharif-Alhoseini
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
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Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data Methods Inf. Med. (IF 1.7) Pub Date : 2022-10-11 Brian L. Thomas, Lawrence B. Holder, Diane J. Cook
Background Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputation. Objective The objective of this work is to adapt a data generation algorithm to impute multivariate time series data. This will allow us to create
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An Explainable Knowledge-Based System Using Subjective Preferences and Objective Data for Ranking Decision Alternatives Methods Inf. Med. (IF 1.7) Pub Date : 2022-10-11 Kavya Ramisetty, Jabez Christopher, Subhrakanta Panda, Baktha Singh Lazarus, Julie Dayalan
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
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TransformEHRs: a flexible methodology for building transparent ETL processes for EHR reuse Methods Inf. Med. (IF 1.7) Pub Date : 2022-10-11 Miguel Pedrera-Jiménez, Noelia García-Barrio, Paula Rubio-Mayo, Alberto Tato-Gómez, Juan Luis Cruz-Bermúdez, José Luis Bernal-Sobrino, Adolfo Muñoz-Carrero, Pablo Serrano-Balazote
Background During the COVID-19 pandemic, several methodologies were designed for obtaining electronic health record (EHR)-derived datasets for research. These processes are often based on black boxes, on which clinical researchers are unaware of how the data were recorded, extracted, and transformed. In order to solve this, it is essential that extract, transform, and load (ETL) processes are based
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Eliciting Information Needs of Child Patients: Adapting the Kano Model to the Design of mHealth Applications Methods Inf. Med. (IF 1.7) Pub Date : 2022-10-11 Sune Dueholm Müller, Georgios Tsirozidis, Morten Mathiasen, Louise Nordenhof, Daniel Jakobsen, Birgitte Mahler
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
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Reliability of Cusp Angulation Using Three-Dimensional Digital Models: A Preliminary In Vitro Study Methods Inf. Med. (IF 1.7) Pub Date : 2022-10-11 Xinggang Liu, Xiaoxian Chen
Background Dental cusp angulation provides valuable insights into chewing efficiency and prosthesis safety. Artificial intelligence-enabled computing of cusp angles has potential important value, but there is currently no reliable digital measurement method as a cornerstone. Objectives To establish a digital method for measuring cusp angles and investigate inter-rater and intra-rater reliabilities
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DxGenerator: an Improved Differential Diagnosis Generator for Primary Care based on MetaMap and Semantic Reasoning Methods Inf. Med. (IF 1.7) Pub Date : 2022 Ali Sanaeifar, Saeid Eslami, Mitra Ahadi, Mohsen Kahani, Hasan Vakili Arki
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
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Development and Testing Requirements for an Integrated Maternal and Child Health Information System in Iran: A Design Thinking Case Study Methods Inf. Med. (IF 1.7) Pub Date : 2022-09-19 Zahra Meidani, Alireza Moravveji, Shirin Gohari, Hamideh Ghaffarian, Sahar Zare, Fatemeh Vaseghi, Gholam Abbas Moosavi, Ali mohammad Nickfarjam, Felix Holl
Background Management of child health care can be negatively affected by incomplete recording, low data quality, and lack of data integration of health management information systems 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
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A Systematic Approach to Configuring MetaMap for Optimal Performance Methods Inf. Med. (IF 1.7) Pub Date : 2022-09-19 Xia Jing, Akash Indani, Nina Hubig, Hua Min, Yang Gong, James J. Cimino, Dean F. Sittig, Lior Rennert, David Robinson, Paul Biondich, Adam Wright, Christian Nøhr, Timothy Law, Arild Faxvaag, Ronald Gimbel
Background MetaMap is a valuable tool for processing biomedical texts to identify concepts. Although MetaMap is highly configurative, configuration decisions are not straightforward. Objective To develop a systematic, data-driven methodology for configuring MetaMap for optimal performance. Methods MetaMap, the word2vec model, and the phrase model were used to build a pipeline. For unsupervised training
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Breast Cancer Subtypes Classification with Hybrid Machine Learning Model Methods Inf. Med. (IF 1.7) Pub Date : 2022-09-12 Suvobrata Sarkar, Kalyani Mali
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
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Automated Identification of Clinical Procedures in Free-Text Electronic Clinical Records with a Low-Code Named Entity Recognition Workflow Methods Inf. Med. (IF 1.7) Pub Date : 2022-09-12 Carmelo Macri, Ian Teoh, Stephen Bacchi, Michelle Sun, Dinesh Selva, Robert Casson, WengOnn Chan
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
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Use of Machine Learning to Identify Clinical Variables in Pregnant and Non-pregnant Women with SARS-CoV-2 Infection Methods Inf. Med. (IF 1.7) Pub Date : 2022-09-12 Itamar D. Futterman, Rodney McLaren Jr., Hila Friedmann, Nael Musleh, Shoshana Haberman
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
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A Methodological Approach to Validate Pneumonia Encounters from Radiology Reports Using Natural Language Processing Methods Inf. Med. (IF 1.7) Pub Date : 2022-08-19 AlokSagar Panny, Harshad Hegde, Ingrid Glurich, Frank A. Scannapieco, Jayanth G. Vedre, Jeffrey J. VanWormer, Jeffrey Miecznikowski, Amit Acharya
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
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Automated Identification of Immunocompromised Status in Critically Ill Children Methods Inf. Med. (IF 1.7) Pub Date : 2022-08-19 Swaminathan Kandaswamy, Evan W. Orenstein, Elizabeth Quincer, Alfred J. Fernandez, Mark D. Gonzalez, Lydia Lu, Rishikesan Kamaleswaran, Imon Banerjee, Preeti Jaggi
Background Easy identification of immunocompromised hosts (ICHs) would allow for stratification of culture results based on host type. Methods We utilized antimicrobial stewardship program (ASP) team notes written during handshake stewardship rounds in the pediatric intensive care unit (PICU) as the gold standard for host status; clinical notes from the primary team, medication orders during the encounter
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Identifying Pneumonia Subtypes from Electronic Health Records Using Rule-Based Algorithms Methods Inf. Med. (IF 1.7) Pub Date : 2022-06-28 Harshad Hegde, Ingrid Glurich, Aloksagar Panny, Jayanth G. Vedre, Jeffrey J. VanWormer, Richard Berg, Frank A. Scannapieco, Jeffrey Miecznikowski, Amit Acharya
Background The 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 (EHRs), frequently
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Evaluating Prediction of Continuous Clinical Values: A Glucose Case Study Methods Inf. Med. (IF 1.7) Pub Date : 2022-02-23
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
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A Comparison of Methods to Detect Changes in Prediction Models Methods Inf. Med. (IF 1.7) Pub Date : 2022-02-12 Erin M. Schnellinger, Wei Yang, Michael O. Harhay, Stephen E. Kimmel
Background Prediction models inform decisions in many areas of medicine. Most models are fitted once and then applied to new (future) patients, despite the fact that model coefficients can vary over time due to changes in patients' clinical characteristics and disease risk. However, the optimal method to detect changes in model parameters has not been rigorously assessed. Methods We simulated data
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Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing Methods Inf. Med. (IF 1.7) Pub Date : 2022-02-10
Background Numerous prediction models for readmissions are developed from hospital data whose predictor variables are based on specific data fields that are often not transferable to other settings. In contrast, routine data from statutory health insurances (in Germany) are highly standardized, ubiquitously available, and would thus allow for automatic identification of readmission risks. Objectives To
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Security and Privacy in Distributed Health Care Environments. Methods Inf. Med. (IF 1.7) Pub Date : 2022-02-10 Stephen V Flowerday,Christos Xenakis
N.A.
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Disambiguating Clinical Abbreviations Using a One-Fits-All Classifier Based on Deep Learning Techniques Methods Inf. Med. (IF 1.7) Pub Date : 2022-02-01 Areej Jaber, Paloma Martínez
Background Abbreviations are considered an essential part of the clinical narrative; they are used not only to save time and space but also to hide serious or incurable illnesses. Misreckoning interpretation of the clinical abbreviations could affect different aspects concerning patients themselves or other services like clinical support systems. There is no consensus in the scientific community to
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Privacy-Preserving Artificial Intelligence Techniques in Biomedicine Methods Inf. Med. (IF 1.7) Pub Date : 2022-01-21 Reihaneh Torkzadehmahani, Reza Nasirigerdeh, David B. Blumenthal, Tim Kacprowski, Markus List, Julian Matschinske, Julian Spaeth, Nina Kerstin Wenke, Jan Baumbach
Background Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g., in the interpretation of next-generation sequencing data and in the design of clinical decision support systems. Objectives However, training an AI model on sensitive data raises concerns about the privacy of individual participants.
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A Privacy-Preserving Distributed Analytics Platform for Health Care Data Methods Inf. Med. (IF 1.7) Pub Date : 2022-01-17 Sascha Welten, Yongli Mou, Laurenz Neumann, Mehrshad Jaberansary, Yeliz Yediel Ucer, Toralf Kirsten, Stefan Decker, Oya Beyan
Background In recent years, data-driven medicine has gained increasing importance in terms of diagnosis, treatment, and research due to the exponential growth of health care data. However, data protection regulations prohibit data centralisation for analysis purposes because of potential privacy risks like the accidental disclosure of data to third parties. Therefore, alternative data usage policies