bluuebunny/crossref_metadata_2025_split huggingface dataset:
Chemoselective Strain Release of Bicyclo[1.1.1]pent-1-yl Alcohols
Authors: Yu Songjie , Ma Yue , Ai Yinan 
Published: December 2022

AbstractThe chemoselective C–C bond cleavage of bicyclo[1.1.1]pent-1-yl alcohols (BCP-OHs) was examined. Highly ring-strained BCP-OHs were found to be particularly reactive, delivering cyclobutanone derivatives through a base-mediated single C–C bond cleavage or α,β-unsaturated ketones by palladium-catalyzed dual C–C bond cleavage.
Chiral Bicyclo[2.2.2]octane-Fused and Ferrocene-Derived Cyclopentadienyl Ligands for Asymmetric C–H Activation
Authors: Wang Jun , Liang Hao 
Published: December 2022

AbstractTo fulfill increasing and diverse demands of cyclopentadienyl metal complexes (CpM) catalyzed asymmetric C–H activation, innovation in developing new types of chiral Cp ligands should be important. In this context, we found the structurally rigid chiral bicyclo[2.2.2]octane-fused Cp ligands originated by Vollhardt and ­Halterman were efficient to enable highly enantioselective C–H activation of N-methoxybenzamides with quinones. Besides, we also designed and synthesized a class of ferrocene-based chiral Cp ligand featuring a loose chiral pocket, whose rhodium complexes exhibited high reactivity and reasonable enantioselectivity in an asymmetric intramolecular aryl amination of alkene.1	Introduction2	Chiral Cp Ligands Applied in Asymmetric C–H Activation3	Chiral Bicyclo[2.2.2]octane-Fused Cp Ligands4	Chiral Ferrocene-Derived Cp Ligands5	Conclusion
Digital Health Equity: Addressing Power, Usability, and Trust to Strengthen Health Systems
Authors: Koehle Han , Kronk Clair , Lee Young Ji 
Published: December 2022

Background: Without specific attention to health equity considerations in design, implementation, and evaluation, the rapid expansion of digital health approaches threatens to exacerbate rather than ameliorate existing health disparities.
Methods: We explored known factors that increase digital health inequity to contextualize the need for equity-centered informatics. This work used a narrative review method to summarize issues about inequities in digital health and to discuss future directions for researchers and clinicians. We searched literature using a combination of relevant keywords (e.g., “digital health”, “health equity”, etc.) using PubMed and Google Scholar.
Results: We have highlighted strategies for addressing medical marginalization in informatics according to vectors of power such as race and ethnicity, gender identity and modality, sexuality, disability, housing status, citizenship status, and criminalization status.
Conclusions: We have emphasized collaboration with user and patient groups to define priorities, ensure accessibility and localization, and consider risks in development and utilization of digital health tools. Additionally, we encourage consideration of potential pitfalls in adopting these diversity, equity, and inclusion (DEI)-related strategies.
Translational Bioinformatics to Enable Precision Medicine for All: Elevating Equity across Molecular, Clinical, and Digital Realms
Authors: Tang Alice , Woldemariam Sarah , Roger Jacquelyn , Sirota Marina 
Published: December 2022

Objectives: Over the past few years, challenges from the pandemic have led to an explosion of data sharing and algorithmic development efforts in the areas of molecular measurements, clinical data, and digital health. We aim to characterize and describe recent advanced computational approaches in translational bioinformatics across these domains in the context of issues or progress related to equity and inclusion.
Methods: We conducted a literature assessment of the trends and approaches in translational bioinformatics in the past few years.
Results: We present a review of recent computational approaches across molecular, clinical, and digital realms. We discuss applications of phenotyping, disease subtype characterization, predictive modeling, biomarker discovery, and treatment selection. We consider these methods and applications through the lens of equity and inclusion in biomedicine.
Conclusion: Equity and inclusion should be incorporated at every step of translational bioinformatics projects, including project design, data collection, model creation, and clinical implementation. These considerations, coupled with the exciting breakthroughs in big data and machine learning, are pivotal to reach the goals of precision medicine for all.
Patient Experience from an eHealth Perspective: A Scoping Review of Approaches and Recent Trends
Authors: Viitanen Johanna , Valkonen Paula , Savolainen Kaisa , Karisalmi Nina , Hölsä Sini , Kujala Sari 
Published: December 2022

Objectives: Patients’ experiences are increasingly gaining interest in multiple research fields. Researchers have applied various approaches to studying patient experience (PX); however, there is no commonly agreed-upon definition of PX. This scoping review focuses on PX from an eHealth perspective. Our aim was to: 1) describe how PX has been defined, 2) investigate which factors influencing PX and components of PX have been identified and researched, 3) explore the methods used in studying PX, and 4) find out the recent trends in PX research from an eHealth perspective.
Methods: We selected six major journals covering the fields of health informatics, PX, and nursing informatics. Using the search terms “patient experience” and technology-related terms (e.g., digital, eHealth), we searched for articles published between 2019 and 2021. From 426 articles, 44 were included in the analysis.
Results: Multiple concepts and meanings are used to refer to PX. Few articles include vague descriptions of the concept. Numerous eHealth factors are influencing PX, as well as components considering PX. The influencing factors were related to eHealth solutions’ type and quality, and care process, when the components of PX were related to communication, remote interaction, risks and concerns, and patients’ attitudes towards telehealth. Surveys were the main method used to study PX, followed by interviews.
Conclusions: PX is a complex and multifaceted phenomenon, and it is described as a synonym for patient satisfaction and telehealth experiences. Further multidisciplinary research is needed to understand PX as a phenomenon and to outline a framework for the research.
A Literature Review on Ethics for AI in Biomedical Research and Biobanking
Authors: Kargl Michaela , Plass Markus , Müller Heimo 
Published: December 2022

Background: Artificial Intelligence (AI) is becoming more and more important especially in datacentric fields, such as biomedical research and biobanking. However, AI does not only offer advantages and promising benefits, but brings about also ethical risks and perils. In recent years, there has been growing interest in AI ethics, as reflected by a huge number of (scientific) literature dealing with the topic of AI ethics. The main objectives of this review are: (1) to provide an overview about important (upcoming) AI ethics regulations and international recommendations as well as available AI ethics tools and frameworks relevant to biomedical research, (2) to identify what AI ethics can learn from findings in ethics of traditional biomedical research - in particular looking at ethics in the domain of biobanking, and (3) to provide an overview about the main research questions in the field of AI ethics in biomedical research.
Methods: We adopted a modified thematic review approach focused on understanding AI ethics aspects relevant to biomedical research. For this review, four scientific literature databases at the cross-section of medical, technical, and ethics science literature were queried: PubMed, BMC Medical Ethics, IEEE Xplore, and Google Scholar. In addition, a grey literature search was conducted to identify current trends in legislation and standardization.
Results: More than 2,500 potentially relevant publications were retrieved through the initial search and 57 documents were included in the final review. The review found many documents describing high-level principles of AI ethics, and some publications describing approaches for making AI ethics more actionable and bridging the principles-to-practice gap. Also, some ongoing regulatory and standardization initiatives related to AI ethics were identified. It was found that ethical aspects of AI implementation in biobanks are often like those in biomedical research, for example with regards to handling big data or tackling informed consent. The review revealed current ‘hot’ topics in AI ethics related to biomedical research. Furthermore, several published tools and methods aiming to support practical implementation of AI ethics, as well as tools and frameworks specifically addressing complete and transparent reporting of biomedical studies involving AI are described in the review results.
Conclusions: The review results provide a practically useful overview of research strands as well as regulations, guidelines, and tools regarding AI ethics in biomedical research. Furthermore, the review results show the need for an ethical-mindful and balanced approach to AI in biomedical research, and specifically reveal the need for AI ethics research focused on understanding and resolving practical problems arising from the use of AI in science and society.
A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images
Authors: Carmo Diedre , Ribeiro Jean , Dertkigil Sergio , Appenzeller Simone , Lotufo Roberto , Rittner Leticia 
Published: December 2022

Objectives: Automated computational segmentation of the lung and its lobes and findings in X-Ray based computed tomography (CT) images is a challenging problem with important applications, including medical research, surgical planning, and diagnostic decision support. With the increase in large imaging cohorts and the need for fast and robust evaluation of normal and abnormal lungs and their lobes, several authors have proposed automated methods for lung assessment on CT images. In this paper we intend to provide a comprehensive summarization of these methods.
Methods: We used a systematic approach to perform an extensive review of automated lung segmentation methods. We chose Scopus, PubMed, and Scopus to conduct our review and included methods that perform segmentation of the lung parenchyma, lobes or internal disease related findings. The review was not limited by date, but rather by only including methods providing quantitative evaluation.
Results: We organized and classified all 234 included articles into various categories according to methodological similarities among them. We provide summarizations of quantitative evaluations, public datasets, evaluation metrics, and overall statistics indicating recent research directions of the field.
Conclusions: We noted the rise of data-driven models in the last decade, especially due to the deep learning trend, increasing the demand for high-quality data annotation. This has instigated an increase of semi-supervised and uncertainty guided works that try to be less dependent on human annotation. In addition, the question of how to evaluate the robustness of data-driven methods remains open, given that evaluations derived from specific datasets are not general.
Electronic Health Record-Integrated Clinical Decision Support for Clinicians Serving Populations Facing Health Care Disparities: Literature Review
Authors: Stipelman Carole H. , Kukhareva Polina V. , Trepman Elly , Nguyen Quang-Tuyen , Valdez Lourdes , Kenost Colleen , Hightower Maia , Kawamoto Kensaku 
Published: December 2022

Objectives: To review current studies about designing and implementing clinician-facing clinical decision support (CDS) integrated or interoperable with an electronic health record (EHR) to improve health care for populations facing disparities.
Methods: We searched PubMed to identify studies published between January 1, 2011 and October 22, 2021 about clinician-facing CDS integrated or interoperable with an EHR. We screened abstracts and titles and extracted study data from articles using a protocol developed by team consensus. Extracted data included patient population characteristics, clinical specialty, setting, EHR, clinical problem, CDS type, reported user-centered design, implementation strategies, and outcomes.
Results: There were 28 studies (36 articles) included. Most studies were performed at safety net institutions (14 studies) or Indian Health Service sites (6 studies). CDS tools were implemented in primary care outpatient settings in 24 studies (86%) for screening or treatment. CDS included point-of-care alerts (93%), order facilitators (46%), workflow support (39%), relevant information display (36%), expert systems (11%), and medication dosing support (7%). Successful outcomes were reported in 19 of 26 studies that reported outcomes (73%). User-centered design was reported during CDS planning (39%), development (32%), and implementation phase (25%). Most frequent implementation strategies were education (89%) and consensus facilitation (50%).
Conclusions: CDS tools may improve health equity and outcomes for patients who face disparities. The present review underscores the need for high-quality analyses of CDS-associated health outcomes, reporting of user-centered design and implementation strategies used in low-resource settings, and methods to disseminate CDS created to improve health equity.
Transforming Health Data to Actionable Information: Recent Progress and Future Opportunities in Health Information Exchange
Authors: Sarkar Indra Neil 
Published: December 2022

Objectives: Provide a systematic review of literature pertaining to health information exchange (HIE) since 2018. Summarize HIE-associated literature for most frequently occurring topics, as well as within the context of the COVID-19 pandemic and health equity. Finally, provide recommendations for how HIE can advance the vision of a digital healthcare ecosystem.
Methods: A computer program was developed to mediate a literature search of primary literature indexed in MEDLINE that was: (1) indexed with “Health Information Exchange” MeSH descriptor as a major topic; and (2) published between January 2018 and December 2021. Frequency of MeSH descriptors was then used to identify and to rank topics associated with the retrieved literature. COVID-19 literature was identified using the general COVID-19 PubMed Clinical Query filter. Health equity literature was identified using additional MeSH descriptor-based searches. The retrieved literature was then reviewed and summarized.
Results: A total of 256 articles were retrieved and reviewed for this survey. The major thematic areas summarized were: (1) Information Dissemination; (2) Delivery of Health Care; (3) Hospitals; (4) Hospital Emergency Service; (5) COVID-19; (6) Health Disparities; and (7) Computer Security and Confidentiality. A common theme across all areas examined for this survey was the maturity of HIE to support data-driven healthcare delivery. Recommendations were developed based on opportunities identified across the reviewed literature.
Conclusions: HIE is an essential advance in next generation healthcare delivery. The review of the recent literature (2018-2021) indicates that successful HIE improves healthcare delivery, often resulting in improved health outcomes. There remain major opportunities for expanded use of HIE, including the active engagement of clinical and patient stakeholders. The maturity of HIE reflects the maturity of the biomedical informatics and health data science fields.
Consumer Health Informatics for Racial and Ethnic Minoritized Communities: Minor Progress, Major Opportunities
Authors: Valdez Rupa S. , Rogers Courtney C. 
Published: December 2022

Objective: By reducing barriers to accessing health services and by supporting health management, consumer health informatics has the potential to reduce health disparities. Yet, technologies are still being designed without considerations for racial and ethnic minoritized populations. This paper reviews consumer health informatics research within this population to assess for whom and how such technologies are being designed.
Methods: We searched four databases from January 2020- December 2021 for literature focused on consumer health informatics and racial and ethnic minoritized populations. We extracted information about the study population, geographic location, stage of the design lifecycle, culturally tailored approaches, community engagement strategies, and considerations for the social determinants of health.
Results: Twenty articles were included in the review. Most of the included literature were original research articles that tested health management interventions focused on one racial or ethnic minoritized population primarily within a confined geographic area within the United States. Seven studies described the extent to which an intervention was culturally tailored, including modifying the content, interface, functionality, and platform. Community engagement strategies varied, but few articles employed robust approaches. Lastly, seven studies detailed considerations for the social determinants of health, including providing hardware to access interventions and incorporating information about community-based resources within an intervention.
Conclusions: There has been moderate progress in consumer health informatics focused on racial and ethnic minoritized populations and many opportunities remain for these technologies to be used as an approach to address health disparities. Future research should utilize community engagement strategies to design interventions that are attune to multiple racial and ethnic minoritized populations across geographic regions in addition to numerous intersectional identities and multiple co-morbidities.
Cancer Informatics 2022: Real-World Data Yields Important Insights into the Conduct of Clinical Trials and Registries
Authors: Warner Jeremy L. , Rooney Michael K. , Patt Debra 
Published: December 2022

Objective: To summarize significant research contributions on cancer informatics published in 2021.
Methods: An extensive search using PubMed/MEDLINE and Altmetric scores was conducted to identify the scientific contributions published in 2021 that address topics in cancer informatics. The selection process comprised three steps: (i) 15 candidate best papers were first selected by the two section editors, (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper, and (iii) the final selection of two best papers was conducted by the editorial board of the IMIA Yearbook.
Results: The two selected best papers demonstrate some of the promises and shortcomings of real-world data.
Conclusion: Cancer informatics is a maturing subfield of biomedical informatics. Applications of informatics methods to real-world data are especially notable in 2021.
Towards an Interoperable Ecosystem of Research Cohort and Real-world Data Catalogues Enabling Multi-center Studies
Authors: Swertz Morris , van Enckevort Esther , Oliveira José Luis , Fortier Isabel , Bergeron Julie , Thurin Nicolas H. , Hyde Eleanor , Kellmann Alexander , Pahoueshnja Romin , Sturkenboom Miriam , Cunnington Marianne , Nybo Andersen Anne-Marie , Marcon Yannick , Gonçalves Gonçalo , Gini Rosa 
Published: December 2022

Objectives: Existing individual-level human data cover large populations on many dimensions such as lifestyle, demography, laboratory measures, clinical parameters, etc. Recent years have seen large investments in data catalogues to FAIRify data descriptions to capitalise on this great promise, i.e. make catalogue contents more Findable, Accessible, Interoperable and Reusable. However, their valuable diversity also created heterogeneity, which poses challenges to optimally exploit their richness.
Methods: In this opinion review, we analyse catalogues for human subject research ranging from cohort studies to surveillance, administrative and healthcare records.
Results: We observe that while these catalogues are heterogeneous, have various scopes, and use different terminologies, still the underlying concepts seem potentially harmonizable. We propose a unified framework to enable catalogue data sharing, with catalogues of multi-center cohorts nested as a special case in catalogues of real-world data sources. Moreover, we list recommendations to create an integrated community of metadata catalogues and an open catalogue ecosystem to sustain these efforts and maximise impact.
Conclusions: We propose to embrace the autonomy of motivated catalogue teams and invest in their collaboration via minimal standardisation efforts such as clear data licensing, persistent identifiers for linking same records between catalogues, minimal metadata ‘common data elements’ using shared ontologies, symmetric architectures for data sharing (push/pull) with clear provenance tracks to process updates and acknowledge original contributors. And most importantly, we encourage the creation of environments for collaboration and resource sharing between catalogue developers, building on international networks such as OpenAIRE and research data alliance, as well as domain specific ESFRIs such as BBMRI and ELIXIR.
Novelty in Public Health and Epidemiology Informatics
Authors: Diallo Gayo , Bordea Georgeta 
Published: December 2022

Objectives: To highlight novelty studies and current trends in Public Health and Epidemiology Informatics (PHEI).
Methods: Similar to last year’s edition, a PubMed search of 2021 scientific publications on PHEI has been conducted. The resulting references were reviewed by the two section editors. Then, 11 candidate best papers were selected from the initial 782 references. These papers were then peer-reviewed by selected external reviewers. They included at least two senior researchers, to allow the Editorial Committee of the 2022 IMIA Yearbook edition to make an informed decision for selecting the best papers of the PHEI section.
Results: Among the 782 references retrieved from PubMed, two were selected as the best papers. The first best paper reports a study which performed a comprehensive comparison of traditional statistical approaches (e.g., Cox Proportional Hazards models) vs. machine learning techniques in a large, real-world dataset for predicting breast cancer survival, with a focus on explainability. The second paper describes the engineering of deep learning models to establish associations between ocular features and major hepatobiliary diseases and to advance automated screening and identification of hepatobiliary diseases from ocular images.
Conclusion: Overall, from this year edition, we observed that the number of studies related to PHEI has decreased. The findings of the two studies selected as best papers on the topic suggest that a significant effort is still being made by the community to compare traditional learning methods with deep learning methods. Using multimodality datasets (images, texts) could improve approaches for tackling public health issues