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Al-Eryani K, Epstein JB, Monreal AV, Villa A. Orofacial complications from immune checkpoint inhibitors: A retrospective analysis from two academic medical centers. Head Neck 2024; 46:1865-1872. [PMID: 38258988 DOI: 10.1002/hed.27646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 12/31/2023] [Accepted: 01/10/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Immune checkpoint inhibitors (ICIs) are FDA-approved for various cancers, yet their orofacial immune-related adverse events (irAEs) remain poorly understood. Our two-center retrospective study aims to better understand the prevalence and nature of these orofacial irAEs. METHODS We retrospectively collected demographics, ICI details, and onset of orofacial irAEs in ICI-treated patients at University of California San Francisco and City of Hope (2013-2021). Orofacial irAEs were identified by ICD-10 codes and data categorized as dry mouth/xerostomia, oral mucosal lesions, and orofacial neuropathies. Patients with pre-existing orofacial conditions resembling the reported irAEs were excluded. RESULTS Among 3768 ICI-treated patients, 408 (10.8%) developed 467 orofacial irAEs: oral mucosal diseases (41.4%), dry mouth/xerostomia (41.0%), and orofacial neuropathies (17.6%). Notably, head and neck cancers had the highest incidence of orofacial irAEs. CONCLUSIONS Orofacial irAEs are relatively common in patients receiving ICIs, necessitating careful monitoring and management of these complications during and after the treatment.
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Affiliation(s)
- Kamal Al-Eryani
- Department of Orofacial Sciences, University of California, San Francisco, California, USA
| | - Joel B Epstein
- Dental Oncology Services, City of Hope Comprehensive Cancer Center, Duarte, California, USA
- Cedars Sinai Health System, Los Angeles, California, USA
| | - Anette Vistoso Monreal
- Herman Ostrow School of Dentistry, University of Southern California, Los Angeles, California, USA
| | - Alessandro Villa
- Department of Orofacial Sciences, University of California, San Francisco, California, USA
- Oral Medicine, Oral Oncology and Dentistry, Miami Cancer Institute, Miami, Florida, USA
- Herbert Wertheim College of Medicine, Florida International University, Miami, Florida, USA
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2
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Tang AS, Woldemariam SR, Miramontes S, Norgeot B, Oskotsky TT, Sirota M. Harnessing EHR data for health research. Nat Med 2024; 30:1847-1855. [PMID: 38965433 DOI: 10.1038/s41591-024-03074-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/17/2024] [Indexed: 07/06/2024]
Abstract
With the increasing availability of rich, longitudinal, real-world clinical data recorded in electronic health records (EHRs) for millions of patients, there is a growing interest in leveraging these records to improve the understanding of human health and disease and translate these insights into clinical applications. However, there is also a need to consider the limitations of these data due to various biases and to understand the impact of missing information. Recognizing and addressing these limitations can inform the design and interpretation of EHR-based informatics studies that avoid confusing or incorrect conclusions, particularly when applied to population or precision medicine. Here we discuss key considerations in the design, implementation and interpretation of EHR-based informatics studies, drawing from examples in the literature across hypothesis generation, hypothesis testing and machine learning applications. We outline the growing opportunities for EHR-based informatics studies, including association studies and predictive modeling, enabled by evolving AI capabilities-while addressing limitations and potential pitfalls to avoid.
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Affiliation(s)
- Alice S Tang
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Sarah R Woldemariam
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Silvia Miramontes
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | | | - Tomiko T Oskotsky
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA.
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3
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Lin S, Tao S, Chou WC, Zhang GQ, Li X. VisualSphere: a Web-based Interactive Visualization System for Clinical Research Data. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2024; 2024:603-612. [PMID: 38827073 PMCID: PMC11141841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Clinical research data visualization is integral to making sense of biomedical research and healthcare data. The complexity and diversity of data, along with the need for solid programming skills, can hinder advances in clinical research data visualization. To overcome these challenges, we introduce VisualSphere, a web-based interactive visualization system that directly interfaces with clinical research data repositories, streamlining and simplifying the visualization workflow. VisualSphere is founded on three primary component modules: Connection, Configuration, and Visualization. An end-user can set up connections to the data repositories, create charts by selecting the desired tables and variables, and render visualization dashboards generated by Plotly and R/Shiny. We performed a preliminary evaluation of VisualSphere, which achieved high user satisfaction. VisualSphere has the potential to serve as a versatile tool for various clinical research data repositories, enabling researchers to explore and interact with clinical research data efficiently and effectively.
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Affiliation(s)
- Shiwei Lin
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030
| | - Shiqiang Tao
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, 77030
| | - Wei-Chun Chou
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, 77030
| | - Guo-Qiang Zhang
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, 77030
| | - Xiaojin Li
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, 77030
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4
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Siqueira do Prado L, Allemann S, Viprey M, Schott AM, Dediu D, Dima AL. Toward an Interdisciplinary Approach to Constructing Care Delivery Pathways From Electronic Health Care Databases to Support Integrated Care in Chronic Conditions: Systematic Review of Quantification and Visualization Methods. J Med Internet Res 2023; 25:e49996. [PMID: 38096009 PMCID: PMC10755664 DOI: 10.2196/49996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/31/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Electronic health care databases are increasingly used for informing clinical decision-making. In long-term care, linking and accessing information on health care delivered by different providers could improve coordination and health outcomes. Several methods for quantifying and visualizing this information into data-driven care delivery pathways (CDPs) have been proposed. To be integrated effectively and sustainably into routine care, these methods need to meet a range of prerequisites covering 3 broad domains: clinical, technological, and behavioral. Although advances have been made, development to date lacks a comprehensive interdisciplinary approach. As the field expands, it would benefit from developing common standards of development and reporting that integrate clinical, technological, and behavioral aspects. OBJECTIVE We aimed to describe the content and development of long-term CDP quantification and visualization methods and to propose recommendations for future work. METHODS We conducted a systematic review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. We searched peer-reviewed publications in English and reported the CDP methods by using the following data in the included studies: long-term care data and extracted data on clinical information and aims, technological development and characteristics, and user behaviors. The data are summarized in tables and presented narratively. RESULTS Of the 2921 records identified, 14 studies were included, of which 13 (93%) were descriptive reports and 1 (7%) was a validation study. Clinical aims focused primarily on treatment decision-making (n=6, 43%) and care coordination (n=7, 50%). Technological development followed a similar process from scope definition to tool validation, with various levels of detail in reporting. User behaviors (n=3, 21%) referred to accessing CDPs, planning care, adjusting treatment, or supporting adherence. CONCLUSIONS The use of electronic health care databases for quantifying and visualizing CDPs in long-term care is an emerging field. Detailed and standardized reporting of clinical and technological aspects is needed. Early consideration of how CDPs would be used, validated, and implemented in clinical practice would likely facilitate further development and adoption. TRIAL REGISTRATION PROSPERO CRD42019140494; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=140494. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1136/bmjopen-2019-033573.
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Affiliation(s)
- Luiza Siqueira do Prado
- INSERM Unit U1290-Research on Healthcare Performance, University Claude Bernard Lyon 1, Lyon, France
| | - Samuel Allemann
- Pharmaceutical Care Research Group, Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
| | - Marie Viprey
- INSERM Unit U1290-Research on Healthcare Performance, University Claude Bernard Lyon 1, Lyon, France
- Pôle de Santé Publique, Hospices Civils de Lyon, Lyon, France
| | - Anne-Marie Schott
- INSERM Unit U1290-Research on Healthcare Performance, University Claude Bernard Lyon 1, Lyon, France
- Pôle de Santé Publique, Hospices Civils de Lyon, Lyon, France
| | - Dan Dediu
- Catalan Institute for Research and Advanced Studies, Barcelona, Spain
| | - Alexandra Lelia Dima
- INSERM Unit U1290-Research on Healthcare Performance, University Claude Bernard Lyon 1, Lyon, France
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5
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Singh A, Paruthy SB, Belsariya V, Chandra J N, Singh SK, Manivasagam SS, Choudhary S, Kumar MA, Khera D, Kuraria V. Revolutionizing Breast Healthcare: Harnessing the Role of Artificial Intelligence. Cureus 2023; 15:e50203. [PMID: 38192969 PMCID: PMC10772314 DOI: 10.7759/cureus.50203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2023] [Indexed: 01/10/2024] Open
Abstract
Breast cancer has the highest incidence and second-highest mortality rate among all cancers. The management of breast cancer is being revolutionized by artificial intelligence (AI), which is improving early detection, pathological diagnosis, risk assessment, individualized treatment recommendations, and treatment response prediction. Nuclear medicine has used artificial intelligence (AI) for over 50 years, but more recent advances in machine learning (ML) and deep learning (DL) have given AI in nuclear medicine additional capabilities. AI accurately analyzes breast imaging scans for early detection, minimizing false negatives while offering radiologists reliable, swift image processing assistance. It smoothly fits into radiology workflows, which may result in early treatments and reduced expenditures. In pathological diagnosis, artificial intelligence improves the quality of diagnostic data by ensuring accurate diagnoses, lowering inter-observer variability, speeding up the review process, and identifying errors or poor slides. By taking into consideration nutritional, genetic, and environmental factors, providing individualized risk assessments, and recommending more regular tests for higher-risk patients, AI aids with the risk assessment of breast cancer. The integration of clinical and genetic data into individualized treatment recommendations by AI facilitates collaborative decision-making and resource allocation optimization while also enabling patient progress monitoring, drug interaction consideration, and alignment with clinical guidelines. AI is used to analyze patient data, imaging, genomic data, and pathology reports in order to forecast how a treatment would respond. These models anticipate treatment outcomes, make sure that clinical recommendations are followed, and learn from historical data. The implementation of AI in medicine is hampered by issues with data quality, integration with healthcare IT systems, data protection, bias reduction, and ethical considerations, necessitating transparency and constant surveillance. Protecting patient privacy, resolving biases, maintaining transparency, identifying fault for mistakes, and ensuring fair access are just a few examples of ethical considerations. To preserve patient trust and address the effect on the healthcare workforce, ethical frameworks must be developed. The amazing potential of AI in the treatment of breast cancer calls for careful examination of its ethical and practical implications. We aim to review the comprehensive role of artificial intelligence in breast cancer management.
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Affiliation(s)
- Arun Singh
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Shivani B Paruthy
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Vivek Belsariya
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Nemi Chandra J
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Sunil Kumar Singh
- Surgical Oncology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | | | - Sushila Choudhary
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - M Anil Kumar
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Dhananjay Khera
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Vaibhav Kuraria
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
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6
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Linhares CDG, Lima DM, Ponciano JR, Olivatto MM, Gutierrez MA, Poco J, Traina C, Traina AJM. ClinicalPath: A Visualization Tool to Improve the Evaluation of Electronic Health Records in Clinical Decision-Making. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2023; 29:4031-4046. [PMID: 35588413 DOI: 10.1109/tvcg.2022.3175626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Physicians work at a very tight schedule and need decision-making support tools to help on improving and doing their work in a timely and dependable manner. Examining piles of sheets with test results and using systems with little visualization support to provide diagnostics is daunting, but that is still the usual way for the physicians' daily procedure, especially in developing countries. Electronic Health Records systems have been designed to keep the patients' history and reduce the time spent analyzing the patient's data. However, better tools to support decision-making are still needed. In this article, we propose ClinicalPath, a visualization tool for users to track a patient's clinical path through a series of tests and data, which can aid in treatments and diagnoses. Our proposal is focused on patient's data analysis, presenting the test results and clinical history longitudinally. Both the visualization design and the system functionality were developed in close collaboration with experts in the medical domain to ensure a right fit of the technical solutions and the real needs of the professionals. We validated the proposed visualization based on case studies and user assessments through tasks based on the physician's daily activities. Our results show that our proposed system improves the physicians' experience in decision-making tasks, made with more confidence and better usage of the physicians' time, allowing them to take other needed care for the patients.
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7
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Presnov D, Kurz J, Willkomm J, Dillmann J, Alt D, Zilke R, Braun V, Schubert C, Kolb A. Anatomically Integrated In-Place Visualization of Patient Data for Cooperative Tasks with a Case Study on a Neurosurgical Ward. Health Informatics J 2023; 29:14604582231171878. [PMID: 37137867 DOI: 10.1177/14604582231171878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
The workflow in modern hospitals entails that the medical treatment of a patient is distributed between several physicians and nurses. This leads to intensive cooperation, which takes place under particular time pressure and requires efficient conveyance of relevant patient-related medical data to colleagues. This requirement is difficult to achieve with traditional data representation approaches. In this paper, we introduce a novel concept of anatomically integrated in-place visualization designed to engage with cooperative tasks on a neurosurgical ward by using a virtual patient's body as spatial representation of visually encoded abstract medical data. Based on the findings of our field studies, we provide a set of formal requirements and procedures for this kind of visual encoding. Moreover, we implemented a prototype on a mobile device that supports the diagnosis of spinal disc herniation and has been evaluated by 10 neurosurgeons. The physicians have assessed the proposed concept as beneficial, especially emphasizing the advantages of the anatomical integration such as intuitiveness and a better data availability due to providing all information at a glance. Particularly, four of nine respondents have stressed solely benefits of the concept, other four have mentioned benefits with some limitations and only one person has seen no benefits.
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Affiliation(s)
- Dmitri Presnov
- Computer Graphics Group, University of Siegen, Siegen, Germany
| | - Julia Kurz
- Department of Social Sciences, University of Siegen, Germany
| | - Judith Willkomm
- Department of Literature, Art and Media Studies, University of Konstanz, Konstanz, Germany
| | | | - Daniel Alt
- Neurosurgery Department, Jung-Stilling Hospital, Siegen, Germany
| | - Robert Zilke
- Neurosurgery Department, Jung-Stilling Hospital, Siegen, Germany
| | - Veit Braun
- Neurosurgery Department, Jung-Stilling Hospital, Siegen, Germany
| | | | - Andreas Kolb
- Computer Graphics Group, University of Siegen, Siegen, Germany
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8
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Park K, Cho M, Song M, Yoo S, Baek H, Kim S, Kim K. Exploring the potential of OMOP common data model for process mining in healthcare. PLoS One 2023; 18:e0279641. [PMID: 36595527 PMCID: PMC9810199 DOI: 10.1371/journal.pone.0279641] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 12/09/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Recently, Electronic Health Records (EHR) are increasingly being converted to Common Data Models (CDMs), a database schema designed to provide standardized vocabularies to facilitate collaborative observational research. To date, however, rare attempts exist to leverage CDM data for healthcare process mining, a technique to derive process-related knowledge (e.g., process model) from event logs. This paper presents a method to extract, construct, and analyze event logs from the Observational Medical Outcomes Partnership (OMOP) CDM for process mining and demonstrates CDM-based healthcare process mining with several real-life study cases while answering frequently posed questions in process mining, in the CDM environment. METHODS We propose a method to extract, construct, and analyze event logs from the OMOP CDM for process types including inpatient, outpatient, emergency room processes, and patient journey. Using the proposed method, we extract the retrospective data of several surgical procedure cases (i.e., Total Laparoscopic Hysterectomy (TLH), Total Hip Replacement (THR), Coronary Bypass (CB), Transcatheter Aortic Valve Implantation (TAVI), Pancreaticoduodenectomy (PD)) from the CDM of a Korean tertiary hospital. Patient data are extracted for each of the operations and analyzed using several process mining techniques. RESULTS Using process mining, the clinical pathways, outpatient process models, emergency room process models, and patient journeys are demonstrated using the extracted logs. The result shows CDM's usability as a novel and valuable data source for healthcare process analysis, yet with a few considerations. We found that CDM should be complemented by different internal and external data sources to address the administrative and operational aspects of healthcare processes, particularly for outpatient and ER process analyses. CONCLUSION To the best of our knowledge, we are the first to exploit CDM for healthcare process mining. Specifically, we provide a step-by-step guidance by demonstrating process analysis from locating relevant CDM tables to visualizing results using process mining tools. The proposed method can be widely applicable across different institutions. This work can contribute to bringing a process mining perspective to the existing CDM users in the changing Hospital Information Systems (HIS) environment and also to facilitating CDM-based studies in the process mining research community.
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Affiliation(s)
- Kangah Park
- Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea
| | - Minsu Cho
- School of Information Convergence, Kwangwoon University, Seoul, South Korea
| | - Minseok Song
- Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea
- * E-mail: (MS); (SY)
| | - Sooyoung Yoo
- Healthcare ICT Research Center, Office of eHealth Research and Businesses, Seoul National University Bundang Hospital, Seongnam, South Korea
- * E-mail: (MS); (SY)
| | - Hyunyoung Baek
- Healthcare ICT Research Center, Office of eHealth Research and Businesses, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Seok Kim
- Healthcare ICT Research Center, Office of eHealth Research and Businesses, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Kidong Kim
- Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, South Korea
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9
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Sultanum N, Naeem F, Brudno M, Chevalier F. ChartWalk: Navigating large collections of text notes in electronic health records for clinical chart review. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2023; 29:1244-1254. [PMID: 36166535 DOI: 10.1109/tvcg.2022.3209444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Before seeing a patient for the first time, healthcare workers will typically conduct a comprehensive clinical chart review of the patient's electronic health record (EHR). Within the diverse documentation pieces included there, text notes are among the most important and thoroughly perused segments for this task; and yet they are among the least supported medium in terms of content navigation and overview. In this work, we delve deeper into the task of clinical chart review from a data visualization perspective and propose a hybrid graphics+text approach via ChartWalk, an interactive tool to support the review of text notes in EHRs. We report on our iterative design process grounded in input provided by a diverse range of healthcare professionals, with steps including: (a) initial requirements distilled from interviews and the literature, (b) an interim evaluation to validate design decisions, and (c) a task-based qualitative evaluation of our final design. We contribute lessons learned to better support the design of tools not only for clinical chart reviews but also other healthcare-related tasks around medical text analysis.
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Bennett N, Plečko D, Ukor IF, Meinshausen N, Bühlmann P. ricu: R's interface to intensive care data. Gigascience 2022; 12:giad041. [PMID: 37318234 PMCID: PMC10268223 DOI: 10.1093/gigascience/giad041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 03/07/2023] [Accepted: 05/18/2023] [Indexed: 06/16/2023] Open
Abstract
OBJECTIVE To develop a unified framework for analyzing data from 5 large publicly available intensive care unit (ICU) datasets. FINDINGS Using 3 American (Medical Information Mart for Intensive Care III, Medical Information Mart for Intensive Care IV, electronic ICU) and 2 European (Amsterdam University Medical Center Database, High Time Resolution ICU Dataset) databases, we constructed a mapping for each database to a set of clinically relevant concepts, which are grounded in the Observational Medical Outcomes Partnership Vocabulary wherever possible. Furthermore, we performed synchronization in the units of measurement and data type representation. On top of this, we built functionality, which allows the user to download, set up, and load data from all of the 5 databases, through a unified Application Programming Interface. The resulting ricu R-package represents the computational infrastructure for handling publicly available ICU datasets, and its latest release allows the user to load 119 existing clinical concepts from the 5 data sources. CONCLUSION The ricu R-package (available on GitHub and CRAN) is the first tool that enables users to analyze publicly available ICU datasets simultaneously (datasets are available upon request from respective owners). Such an interface saves researchers time when analyzing ICU data and helps reproducibility. We hope that ricu can become a community-wide effort, so that data harmonization is not repeated by each research group separately. One current limitation is that concepts were added on a case-to-case basis, and therefore the resulting dictionary of concepts is not comprehensive. Further work is needed to make the dictionary comprehensive.
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Affiliation(s)
- Nicolas Bennett
- Seminar for Statistics, ETH Zürich, 8092 Zürich (Rämistrasse 101), Switzerland
| | - Drago Plečko
- Seminar for Statistics, ETH Zürich, 8092 Zürich (Rämistrasse 101), Switzerland
| | - Ida-Fong Ukor
- Department of Anaesthesiology and Perioperative Medicine, Monash Health, Clayton VIC 3168, Australia
| | - Nicolai Meinshausen
- Seminar for Statistics, ETH Zürich, 8092 Zürich (Rämistrasse 101), Switzerland
| | - Peter Bühlmann
- Seminar for Statistics, ETH Zürich, 8092 Zürich (Rämistrasse 101), Switzerland
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11
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Dileep G, Gianchandani Gyani SG. Artificial Intelligence in Breast Cancer Screening and Diagnosis. Cureus 2022; 14:e30318. [PMID: 36381716 PMCID: PMC9650950 DOI: 10.7759/cureus.30318] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 10/15/2022] [Indexed: 11/05/2022] Open
Abstract
Cancer is a disease that continues to plague our modern society. Among all types of cancer, breast cancer is now the most common type of cancer occurring in women worldwide. Various factors, including genetics, lifestyle, and the environment, have contributed to the rise in the prevalence of breast cancer among women of all socioeconomic strata. Therefore, proper screening for early diagnosis and treatment becomes a major factor when fighting the disease. Artificial intelligence (AI) continues to revolutionize various spheres of our lives with its numerous applications. Using AI in the existing screening process makes obtaining results even easier and more convenient. Faster, more accurate results are some of the benefits of AI methods in breast cancer screening. Nonetheless, there are many challenges in the process of the integration of AI that needs to be addressed systematically. The following is a review of the application of AI in breast cancer screening.
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12
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Linden SVD, Sevastjanova R, Funk M, El-Assady M. MediCoSpace
: Visual Decision-Support for Doctor-Patient Consultations using Medical Concept Spaces from EHRs. ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2022. [DOI: 10.1145/3564275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
Healthcare systems are under pressure from an aging population, rising costs, and increasingly complex conditions and treatments. Although data are determined to play a bigger role in how doctors diagnose and prescribe treatments, they struggle due to a lack of time and an abundance of structured and unstructured information. To address this challenge, we introduce
MediCoSpace
, a visual decision-support tool for more efficient doctor-patient consultations. The tool links patient reports to past and present diagnoses, diseases, drugs, and treatments, both for the current patient and other patients in comparable situations.
MediCoSpace
uses textual medical data, deep-learning supported text analysis and concept spaces to facilitate a visual discovery process. The tool is evaluated with five medical doctors. The results show that
MediCoSpace
facilitates a promising, yet complex way to discover unlikely relations and thus suggests a path toward the development of interactive visual tools to provide physicians with more holistic diagnoses and personalized, dynamic treatments for patients.
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13
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El-Kareh R, Sittig DF. Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, and Beyond. Crit Care Clin 2022; 38:129-139. [PMID: 34794627 PMCID: PMC8608279 DOI: 10.1016/j.ccc.2021.08.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care unit environments. The field needs well-designed studies to identify the most effective CDS approaches. Evolving artificial intelligence and machine learning models may reduce information-overload and enable teams to take better advantage of the large volume of patient data available to them. It is vital to effectively integrate new CDS into clinical workflows and to align closely with the cognitive processes of frontline clinicians.
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Affiliation(s)
- Robert El-Kareh
- University of California, San Diego, 9500 Gilman Drive, #0881 La Jolla, CA 92093-0881, USA.
| | - Dean F Sittig
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, UT-Memorial Hermann Center for Healthcare Quality & Safety, Houston, TX 77030, USA. https://twitter.com/DeanSittig
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Leckenby E, Dawoud D, Bouvy J, Jónsson P. The Sandbox Approach and its Potential for Use in Health Technology Assessment: A Literature Review. APPLIED HEALTH ECONOMICS AND HEALTH POLICY 2021; 19:857-869. [PMID: 34254275 PMCID: PMC8545721 DOI: 10.1007/s40258-021-00665-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/26/2021] [Indexed: 05/09/2023]
Abstract
BACKGROUND The concept of the regulatory sandbox-a safe space for testing new regulatory processes-was first used within the financial technologies (FinTech) sector, but has since expanded into other sectors, including healthcare. OBJECTIVES This review aims to describe the extent of use of sandboxes in healthcare and assess the potential for the sandbox approach to be used to test and develop emerging health technology assessment (HTA) methods, policies and processes for innovative technologies. METHODS A systematic literature review was undertaken to identify published papers and reports that described and/or assessed the use of sandboxes in the healthcare sector. Searches were conducted in Medline, Embase, Econlit, Social Policy and Practice, and Health Management Information Consortium databases from inception to March 2020. Free-text Google search was also conducted to identify relevant grey literature. Only papers and reports discussing or evaluating the use of sandboxes in healthcare settings and published in English were included. Included studies were qualitatively summarised using a thematic analysis approach. RESULTS Overall, 46 papers and reports were included. The topics covered were classified into 4 major themes: history of the regulatory sandbox, the sandbox as a testing environment, the sandbox as a regulatory approach, examples of using sandboxes in healthcare. Findings show that the use of regulatory sandboxes in healthcare is relatively new and primarily used in high-income countries to support the adoption of new technologies, particularly those related to digital health. Recommendations are made based on these findings to guide its use in HTA policy and methods development. CONCLUSIONS Sandboxes are increasingly used within healthcare regulation. Despite its potential, this approach has not been used in HTA policy and methodological developments to date. HTA agencies should consider this approach to facilitate developing policies, methods and processes for innovative and disruptive health technologies. Transferability to low- and middle-income countries' settings, however, should be assessed.
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Affiliation(s)
- Emily Leckenby
- Centre for Health Technology Evaluation, National Institute for Health and Care Excellence (NICE), Manchester, UK
| | - Dalia Dawoud
- Science Policy and Research Programme, Science, Evidence and Analytics Directorate, National Institute for Health and Care Excellence (NICE), 2nd Floor, 2 Redman Place, London, E20 1JQ, UK.
| | - Jacoline Bouvy
- NICE Scientific Advice, Centre for Health Technology Evaluation, National Institute for Health and Care Excellence (NICE), London, UK
| | - Páll Jónsson
- Data and Analytics, Science, Evidence and Analytics Directorate, National Institute for Health and Care Excellence (NICE), Manchester, UK
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Datta S, Sachs JP, FreitasDa Cruz H, Martensen T, Bode P, Morassi Sasso A, Glicksberg BS, Böttinger E. FIBER: enabling flexible retrieval of electronic health records data for clinical predictive modeling. JAMIA Open 2021; 4:ooab048. [PMID: 34350388 PMCID: PMC8327378 DOI: 10.1093/jamiaopen/ooab048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 05/12/2021] [Accepted: 06/20/2021] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVES The development of clinical predictive models hinges upon the availability of comprehensive clinical data. Tapping into such resources requires considerable effort from clinicians, data scientists, and engineers. Specifically, these efforts are focused on data extraction and preprocessing steps required prior to modeling, including complex database queries. A handful of software libraries exist that can reduce this complexity by building upon data standards. However, a gap remains concerning electronic health records (EHRs) stored in star schema clinical data warehouses, an approach often adopted in practice. In this article, we introduce the FlexIBle EHR Retrieval (FIBER) tool: a Python library built on top of a star schema (i2b2) clinical data warehouse that enables flexible generation of modeling-ready cohorts as data frames. MATERIALS AND METHODS FIBER was developed on top of a large-scale star schema EHR database which contains data from 8 million patients and over 120 million encounters. To illustrate FIBER's capabilities, we present its application by building a heart surgery patient cohort with subsequent prediction of acute kidney injury (AKI) with various machine learning models. RESULTS Using FIBER, we were able to build the heart surgery cohort (n = 12 061), identify the patients that developed AKI (n = 1005), and automatically extract relevant features (n = 774). Finally, we trained machine learning models that achieved area under the curve values of up to 0.77 for this exemplary use case. CONCLUSION FIBER is an open-source Python library developed for extracting information from star schema clinical data warehouses and reduces time-to-modeling, helping to streamline the clinical modeling process.
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Affiliation(s)
- Suparno Datta
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jan Philipp Sachs
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Harry FreitasDa Cruz
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Tom Martensen
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - Philipp Bode
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - Ariane Morassi Sasso
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Erwin Böttinger
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Oh S, Sung M, Rhee Y, Hong N, Park YR. Evaluation of the Privacy Risks of Personal Health Identifiers and Quasi-Identifiers in a Distributed Research Network: Development and Validation Study. JMIR Med Inform 2021; 9:e24940. [PMID: 34057426 PMCID: PMC8204238 DOI: 10.2196/24940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 12/27/2020] [Accepted: 04/11/2021] [Indexed: 11/23/2022] Open
Abstract
Background Privacy should be protected in medical data that include patient information. A distributed research network (DRN) is one of the challenges in privacy protection and in the encouragement of multi-institutional clinical research. A DRN standardizes multi-institutional data into a common structure and terminology called a common data model (CDM), and it only shares analysis results. It is necessary to measure how a DRN protects patient information privacy even without sharing data in practice. Objective This study aimed to quantify the privacy risk of a DRN by comparing different deidentification levels focusing on personal health identifiers (PHIs) and quasi-identifiers (QIs). Methods We detected PHIs and QIs in an Observational Medical Outcomes Partnership (OMOP) CDM as threatening privacy, based on 18 Health Insurance Portability and Accountability Act of 1996 (HIPPA) identifiers and previous studies. To compare the privacy risk according to the different privacy policies, we generated limited and safe harbor data sets based on 16 PHIs and 12 QIs as threatening privacy from the Synthetic Public Use File 5 Percent (SynPUF5PCT) data set, which is a public data set of the OMOP CDM. With minimum cell size and equivalence class methods, we measured the privacy risk reduction with a trust differential gap obtained by comparing the two data sets. We also measured the gap in randomly sampled records from the two data sets to adjust the number of PHI or QI records. Results The gaps averaged 31.448% and 73.798% for PHIs and QIs, respectively, with a minimum cell size of one, which represents a unique record in a data set. Among PHIs, the national provider identifier had the highest gap of 71.236% (71.244% and 0.007% in the limited and safe harbor data sets, respectively). The maximum size of the equivalence class, which has the largest size of an indistinguishable set of records, averaged 771. In 1000 random samples of PHIs, Device_exposure_start_date had the highest gap of 33.730% (87.705% and 53.975% in the data sets). Among QIs, Death had the highest gap of 99.212% (99.997% and 0.784% in the data sets). In 1000, 10,000, and 100,000 random samples of QIs, Device_treatment had the highest gaps of 12.980% (99.980% and 87.000% in the data sets), 60.118% (99.831% and 39.713%), and 93.597% (98.805% and 5.207%), respectively, and in 1 million random samples, Death had the highest gap of 99.063% (99.998% and 0.934% in the data sets). Conclusions In this study, we verified and quantified the privacy risk of PHIs and QIs in the DRN. Although this study used limited PHIs and QIs for verification, the privacy limitations found in this study could be used as a quality measurement index for deidentification of multi-institutional collaboration research, thereby increasing DRN safety.
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Affiliation(s)
- SeHee Oh
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - MinDong Sung
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yumie Rhee
- Department of Internal Medicine, Endocrine Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Namki Hong
- Department of Internal Medicine, Endocrine Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yu Rang Park
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
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Olex AL, McInnes BT. Review of Temporal Reasoning in the Clinical Domain for Timeline Extraction: Where we are and where we need to be. J Biomed Inform 2021; 118:103784. [PMID: 33862232 DOI: 10.1016/j.jbi.2021.103784] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 03/07/2021] [Accepted: 04/08/2021] [Indexed: 11/16/2022]
Abstract
Understanding a patient's medical history, such as how long symptoms last or when a procedure was performed, is vital to diagnosing problems and providing good care. Frequently, important information regarding a patient's medical timeline is buried in their Electronic Health Record (EHR) in the form of unstructured clinical notes. This results in care providers spending time reading notes in a patient's record in order to become familiar with their condition prior to developing a diagnosis or treatment plan. Valuable time could be saved if this information was readily accessible for searching and visualization for fast comprehension by the medical team. Clinical Natural Language Processing (NLP) is an area of research that aims to build computational methods to automatically extract medically relevant information from unstructured clinical texts. A key component of Clinical NLP is Temporal Reasoning, as understanding a patient's medical history relies heavily on the ability to identify, assimilate, and reason over temporal information. In this work, we review the current state of Temporal Reasoning in the clinical domain with respect to Clinical Timeline Extraction. While much progress has been made, the current state-of-the-art still has a ways to go before practical application in the clinical setting will be possible. Areas such as handling relative and implicit temporal expressions, both in normalization and in identifying temporal relationships, improving co-reference resolution, and building inter-operable timeline extraction tools that can integrate multiple types of data are in need of new and innovative solutions to improve performance on clinical data.
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Affiliation(s)
- Amy L Olex
- Virginia Commonwealth University, 401 S. Main St., Richmond, VA 23284, USA.
| | - Bridget T McInnes
- Virginia Commonwealth University, 401 S. Main St., Richmond, VA 23284, USA
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Bhinder B, Gilvary C, Madhukar NS, Elemento O. Artificial Intelligence in Cancer Research and Precision Medicine. Cancer Discov 2021; 11:900-915. [PMID: 33811123 DOI: 10.1158/2159-8290.cd-21-0090] [Citation(s) in RCA: 178] [Impact Index Per Article: 59.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 02/06/2021] [Accepted: 02/08/2021] [Indexed: 11/16/2022]
Abstract
Artificial intelligence (AI) is rapidly reshaping cancer research and personalized clinical care. Availability of high-dimensionality datasets coupled with advances in high-performance computing, as well as innovative deep learning architectures, has led to an explosion of AI use in various aspects of oncology research. These applications range from detection and classification of cancer, to molecular characterization of tumors and their microenvironment, to drug discovery and repurposing, to predicting treatment outcomes for patients. As these advances start penetrating the clinic, we foresee a shifting paradigm in cancer care becoming strongly driven by AI. SIGNIFICANCE: AI has the potential to dramatically affect nearly all aspects of oncology-from enhancing diagnosis to personalizing treatment and discovering novel anticancer drugs. Here, we review the recent enormous progress in the application of AI to oncology, highlight limitations and pitfalls, and chart a path for adoption of AI in the cancer clinic.
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Affiliation(s)
- Bhavneet Bhinder
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York.,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | | | | | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York. .,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York.,OneThree Biotech, New York, New York
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19
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Glicksberg BS, Burns S, Currie R, Griffin A, Wang ZJ, Haussler D, Goldstein T, Collisson E. Blockchain-Authenticated Sharing of Genomic and Clinical Outcomes Data of Patients With Cancer: A Prospective Cohort Study. J Med Internet Res 2020; 22:e16810. [PMID: 32196460 PMCID: PMC7125440 DOI: 10.2196/16810] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Revised: 12/09/2019] [Accepted: 12/15/2019] [Indexed: 12/21/2022] Open
Abstract
Background Efficiently sharing health data produced during standard care could dramatically accelerate progress in cancer treatments, but various barriers make this difficult. Not sharing these data to ensure patient privacy is at the cost of little to no learning from real-world data produced during cancer care. Furthermore, recent research has demonstrated a willingness of patients with cancer to share their treatment experiences to fuel research, despite potential risks to privacy. Objective The objective of this study was to design, pilot, and release a decentralized, scalable, efficient, economical, and secure strategy for the dissemination of deidentified clinical and genomic data with a focus on late-stage cancer. Methods We created and piloted a blockchain-authenticated system to enable secure sharing of deidentified patient data derived from standard of care imaging, genomic testing, and electronic health records (EHRs), called the Cancer Gene Trust (CGT). We prospectively consented and collected data for a pilot cohort (N=18), which we uploaded to the CGT. EHR data were extracted from both a hospital cancer registry and a common data model (CDM) format to identify optimal data extraction and dissemination practices. Specifically, we scored and compared the level of completeness between two EHR data extraction formats against the gold standard source documentation for patients with available data (n=17). Results Although the total completeness scores were greater for the registry reports than those for the CDM, this difference was not statistically significant. We did find that some specific data fields, such as histology site, were better captured using the registry reports, which can be used to improve the continually adapting CDM. In terms of the overall pilot study, we found that CGT enables rapid integration of real-world data of patients with cancer in a more clinically useful time frame. We also developed an open-source Web application to allow users to seamlessly search, browse, explore, and download CGT data. Conclusions Our pilot demonstrates the willingness of patients with cancer to participate in data sharing and how blockchain-enabled structures can maintain relationships between individual data elements while preserving patient privacy, empowering findings by third-party researchers and clinicians. We demonstrate the feasibility of CGT as a framework to share health data trapped in silos to further cancer research. Further studies to optimize data representation, stream, and integrity are required.
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Affiliation(s)
- Benjamin Scott Glicksberg
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, United States.,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Shohei Burns
- Division of Hematology and Oncology, Department of Medicine, University of California, San Francisco, CA, United States
| | - Rob Currie
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United States
| | - Ann Griffin
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, United States
| | - Zhen Jane Wang
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States
| | - David Haussler
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United States.,Howard Hughes Medical Institute, Santa Cruz, CA, United States
| | - Theodore Goldstein
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, United States.,UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United States
| | - Eric Collisson
- Division of Hematology and Oncology, Department of Medicine, University of California, San Francisco, CA, United States
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Cho J, You SC, Lee S, Park D, Park B, Hripcsak G, Park RW. Application for Epidemiological Geographic Information System: An Open-Source Spatial Analysis Tool based on the Common Data Model (Preprint). JMIR Public Health Surveill 2019. [DOI: 10.2196/15804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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