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Kumar H, Panigrahi M, Seo D, Cho S, Bhushan B, Dutt T. Machine Learning-Aided Ultra-Low-Density Single Nucleotide Polymorphism Panel Helps to Identify the Tharparkar Cattle Breed: Lessons for Digital Transformation in Livestock Genomics. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024. [PMID: 39302202 DOI: 10.1089/omi.2024.0153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2024]
Abstract
Cattle breed identification is crucial for livestock research and sustainable food systems, and advances in genomics and artificial intelligence present new opportunities to address these challenges. This study investigates the identification of the Tharparkar cattle breed using genomics tools combined with machine learning (ML) techniques. By leveraging data from the Bovine SNP 50K chip, we developed a breed-specific panel of single nucleotide polymorphisms (SNPs) for Tharparkar cattle and integrated data from seven other Indian cattle populations to enhance panel robustness. Genome-wide association studies (GWAS) and principal component analysis were employed to identify 500 SNPs, which were then refined using ML models-AdaBoost, bagging tree, gradient boosting machines, and random forest-to determine the minimal number of SNPs needed for accurate breed identification. Panels of 23 and 48 SNPs achieved accuracy rates of 95.2-98.4%. Importantly, the identified SNPs were associated with key productive and adaptive traits, thus attesting to the value and potentials of digital transformation in livestock genomics. The ML-aided ultra-low-density SNP panel approach reported here not only facilitates breed identification but also contributes to preserving genetic diversity and guiding future breeding programs.
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Affiliation(s)
- Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, India
- ICAR-National Research Centre on Mithun, Medziphema, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, India
| | - Dongwon Seo
- Research and Development Center, TNT research Co., Jeonju-si, South Korea
| | - Sunghyun Cho
- Research and Development Center, Insilicogen Inc., Yongin-si, South Korea
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, India
| | - Triveni Dutt
- Animal Genetics & Breeding Section, Indian Veterinary Research Institute, Izatnagar, India
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Farmaki A, Manolopoulos E, Natsiavas P. Will Precision Medicine Meet Digital Health? A Systematic Review of Pharmacogenomics Clinical Decision Support Systems Used in Clinical Practice. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:442-460. [PMID: 39136110 DOI: 10.1089/omi.2024.0131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Digital health, an emerging scientific domain, attracts increasing attention as artificial intelligence and relevant software proliferate. Pharmacogenomics (PGx) is a core component of precision/personalized medicine driven by the overarching motto "the right drug, for the right patient, at the right dose, and the right time." PGx takes into consideration patients' genomic variations influencing drug efficacy and side effects. Despite its potentials for individually tailored therapeutics and improved clinical outcomes, adoption of PGx in clinical practice remains slow. We suggest that e-health tools such as clinical decision support systems (CDSSs) can help accelerate the PGx, precision/personalized medicine, and digital health emergence in everyday clinical practice worldwide. Herein, we present a systematic review that examines and maps the PGx-CDSSs used in clinical practice, including their salient features in both technical and clinical dimensions. Using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines and research of the literature, 29 relevant journal articles were included in total, and 19 PGx-CDSSs were identified. In addition, we observed 10 technical components developed mostly as part of research initiatives, 7 of which could potentially facilitate future PGx-CDSSs implementation worldwide. Most of these initiatives are deployed in the United States, indicating a noticeable lack of, and the veritable need for, similar efforts globally, including Europe.
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Affiliation(s)
- Anastasia Farmaki
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
| | - Evangelos Manolopoulos
- Laboratory of Pharmacology, Medical School, Democritus University of Thrace, Alexandroupoli, Greece
| | - Pantelis Natsiavas
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
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Özdemir V. Pharmacogenomics Clinical Decision Support Systems. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:440-441. [PMID: 39226581 DOI: 10.1089/omi.2024.0170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Affiliation(s)
- Vural Özdemir
- OMICS: A Journal of Integrative Biology, New Rochelle, New York, USA
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Chiroma H, Hashem IAT, Maray M. Bibliometric analysis for artificial intelligence in the internet of medical things: mapping and performance analysis. Front Artif Intell 2024; 7:1347815. [PMID: 39188356 PMCID: PMC11345150 DOI: 10.3389/frai.2024.1347815] [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: 12/01/2023] [Accepted: 06/07/2024] [Indexed: 08/28/2024] Open
Abstract
The development of computer technology has revolutionized how people live and interact in society. The Internet of Things (IoT) has enabled the development of the Internet of Medical Things (IoMT) to transform healthcare delivery. Artificial intelligence has been used to improve the IoMT. Despite the significance of bibliometric analysis in a research area, to the best of the authors' knowledge, based on searches conducted in academic databases, no bibliometric analysis on artificial intelligence (AI) for the IoMT has been conducted. To address this gap, this study proposes performing a comprehensive bibliometric analysis of AI applications in the IoMT. A bibliometric analysis of top literature sources, main disciplines, countries, prolific authors, trending topics, authorship, citations, author-keywords, and co-keywords was conducted. In addition, the structural development of AI in the IoMT highlights its growing popularity. This study found that security and privacy issues are serious concerns hindering the massive adoption of the IoMT. Future research directions on the IoMT, including perspectives on artificial general intelligence, generative artificial intelligence, and explainable artificial intelligence, have been outlined and discussed.
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Affiliation(s)
- Haruna Chiroma
- College of Computer Science and Engineering, University of Hafr Al Batin, Hafar Al Batin, Saudi Arabia
| | - Ibrahim Abaker Targio Hashem
- College of Computing and Informatics, Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates
| | - Mohammed Maray
- Department of Computer Science, Department of Information Systems, King Khalid University, Abha, Saudi Arabia
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Moldt JA, Festl-Wietek T, Fuhl W, Zabel S, Claassen M, Wagner S, Nieselt K, Herrmann-Werner A. Assessing AI Awareness and Identifying Essential Competencies: Insights From Key Stakeholders in Integrating AI Into Medical Education. JMIR MEDICAL EDUCATION 2024; 10:e58355. [PMID: 38989834 PMCID: PMC11238140 DOI: 10.2196/58355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/16/2024] [Accepted: 05/07/2024] [Indexed: 07/12/2024]
Abstract
Background The increasing importance of artificial intelligence (AI) in health care has generated a growing need for health care professionals to possess a comprehensive understanding of AI technologies, requiring an adaptation in medical education. Objective This paper explores stakeholder perceptions and expectations regarding AI in medicine and examines their potential impact on the medical curriculum. This study project aims to assess the AI experiences and awareness of different stakeholders and identify essential AI-related topics in medical education to define necessary competencies for students. Methods The empirical data were collected as part of the TüKITZMed project between August 2022 and March 2023, using a semistructured qualitative interview. These interviews were administered to a diverse group of stakeholders to explore their experiences and perspectives of AI in medicine. A qualitative content analysis of the collected data was conducted using MAXQDA software. Results Semistructured interviews were conducted with 38 participants (6 lecturers, 9 clinicians, 10 students, 6 AI experts, and 7 institutional stakeholders). The qualitative content analysis revealed 6 primary categories with a total of 24 subcategories to answer the research questions. The evaluation of the stakeholders' statements revealed several commonalities and differences regarding their understanding of AI. Crucial identified AI themes based on the main categories were as follows: possible curriculum contents, skills, and competencies; programming skills; curriculum scope; and curriculum structure. Conclusions The analysis emphasizes integrating AI into medical curricula to ensure students' proficiency in clinical applications. Standardized AI comprehension is crucial for defining and teaching relevant content. Considering diverse perspectives in implementation is essential to comprehensively define AI in the medical context, addressing gaps and facilitating effective solutions for future AI use in medical studies. The results provide insights into potential curriculum content and structure, including aspects of AI in medicine.
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Affiliation(s)
- Julia-Astrid Moldt
- Tübingen Institute for Medical Education, University of Tübingen, Tübingen, Germany
| | - Teresa Festl-Wietek
- Tübingen Institute for Medical Education, University of Tübingen, Tübingen, Germany
| | - Wolfgang Fuhl
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Susanne Zabel
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Manfred Claassen
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
- Department of Internal Medicine, University Hospital of Tübingen, Tübingen, Germany
| | - Samuel Wagner
- Board of the Faculty of Medicine, University of Tübingen, Tübingen, Germany
| | - Kay Nieselt
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Anne Herrmann-Werner
- Tübingen Institute for Medical Education, University of Tübingen, Tübingen, Germany
- Department of Internal Medicine VI - Psychosomatic Medicine and Psychotherapy, University of Tübingen, Tübingen, Germany
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Cong Y, Endo T. A Quadruple Revolution: Deciphering Biological Complexity with Artificial Intelligence, Multiomics, Precision Medicine, and Planetary Health. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:257-260. [PMID: 38813661 DOI: 10.1089/omi.2024.0110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
A quiet quadruple revolution has been in the making in systems science with convergence of (1) artificial intelligence, machine learning, and other digital technologies; (2) multiomics big data integration; (3) growing interest in the "variability science" of precision/personalized medicine that aims to account for patient-to-patient and between-population differences in disease susceptibilities and responses to health interventions such as drugs, nutrition, vaccines, and radiation; and (4) planetary health scholarship that both scales up and integrates biological, clinical, and ecological contexts of health and disease. Against this overarching background, this article presents and highlights some of the salient challenges and prospects of multiomics research, emphasizing the attendant pivotal role of systems medicine and systems biology. In addition, we emphasize the rapidly growing importance of planetary health research for systems medicine, particularly amid climate emergency, ecological degradation, and loss of planetary biodiversity. Looking ahead, we anticipate that the integration and utilization of multiomics big data and artificial intelligence will drive further progress in systems medicine and systems biology, heralding a promising future for both human and planetary health.
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Affiliation(s)
- Yi Cong
- Information Biology Laboratory, Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Toshinori Endo
- Information Biology Laboratory, Faculty of Information Science and Technology, Hokkaido University, Sapporo, Japan
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Almeman A. The digital transformation in pharmacy: embracing online platforms and the cosmeceutical paradigm shift. JOURNAL OF HEALTH, POPULATION, AND NUTRITION 2024; 43:60. [PMID: 38720390 PMCID: PMC11080122 DOI: 10.1186/s41043-024-00550-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 04/09/2024] [Indexed: 05/12/2024]
Abstract
In the face of rapid technological advancement, the pharmacy sector is undergoing a significant digital transformation. This review explores the transformative impact of digitalization in the global pharmacy sector. We illustrated how advancements in technologies like artificial intelligence, blockchain, and online platforms are reshaping pharmacy services and education. The paper provides a comprehensive overview of the growth of online pharmacy platforms and the pivotal role of telepharmacy and telehealth during the COVID-19 pandemic. Additionally, it discusses the burgeoning cosmeceutical market within online pharmacies, the regulatory challenges faced globally, and the private sector's influence on healthcare technology. Conclusively, the paper highlights future trends and technological innovations, underscoring the dynamic evolution of the pharmacy landscape in response to digital transformation.
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Affiliation(s)
- Ahmad Almeman
- Department of Pharmacology, College of Medicine, Qassim University, Buraydah, Saudi Arabia.
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Gallardo-Pizarro A, Peyrony O, Chumbita M, Monzo-Gallo P, Aiello TF, Teijon-Lumbreras C, Gras E, Mensa J, Soriano A, Garcia-Vidal C. Improving management of febrile neutropenia in oncology patients: the role of artificial intelligence and machine learning. Expert Rev Anti Infect Ther 2024; 22:179-187. [PMID: 38457198 DOI: 10.1080/14787210.2024.2322445] [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: 07/26/2023] [Accepted: 02/20/2024] [Indexed: 03/09/2024]
Abstract
INTRODUCTION Artificial intelligence (AI) and machine learning (ML) have the potential to revolutionize the management of febrile neutropenia (FN) and drive progress toward personalized medicine. AREAS COVERED In this review, we detail how the collection of a large number of high-quality data can be used to conduct precise mathematical studies with ML and AI. We explain the foundations of these techniques, covering the fundamentals of supervised and unsupervised learning, as well as the most important challenges, e.g. data quality, 'black box' model interpretation and overfitting. To conclude, we provide detailed examples of how AI and ML have been used to enhance predictions of chemotherapy-induced FN, detection of bloodstream infections (BSIs) and multidrug-resistant (MDR) bacteria, and anticipation of severe complications and mortality. EXPERT OPINION There is promising potential of implementing accurate AI and ML models whilst managing FN. However, their integration as viable clinical tools poses challenges, including technical and implementation barriers. Improving global accessibility, fostering interdisciplinary collaboration, and addressing ethical and security considerations are essential. By overcoming these challenges, we could transform personalized care for patients with FN.
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Affiliation(s)
| | - Olivier Peyrony
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Mariana Chumbita
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
| | | | | | | | - Emmanuelle Gras
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Josep Mensa
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Alex Soriano
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
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9
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Sáiz-Manzanares MC, Solórzano Mulas A, Escolar-Llamazares MC, Alcantud Marín F, Rodríguez-Arribas S, Velasco-Saiz R. Use of Digitalisation and Machine Learning Techniques in Therapeutic Intervention at Early Ages: Supervised and Unsupervised Analysis. CHILDREN (BASEL, SWITZERLAND) 2024; 11:381. [PMID: 38671598 PMCID: PMC11048911 DOI: 10.3390/children11040381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 03/15/2024] [Accepted: 03/20/2024] [Indexed: 04/28/2024]
Abstract
Advances in technology and artificial intelligence (smart healthcare) open up a range of possibilities for precision intervention in the field of health sciences. The objectives of this study were to analyse the functionality of using supervised (prediction and classification) and unsupervised (clustering) machine learning techniques to analyse results related to the development of functional skills in patients at developmental ages of 0-6 years. We worked with a sample of 113 patients, of whom 49 were cared for in a specific centre for people with motor impairments (Group 1) and 64 were cared for in a specific early care programme for patients with different impairments (Group 2). The results indicated that in Group 1, chronological age predicted the development of functional skills at 85% and in Group 2 at 65%. The classification variable detected was functional development in the upper extremities. Two clusters were detected within each group that allowed us to determine the patterns of functional development in each patient with respect to functional skills. The use of smart healthcare resources has a promising future in the field of early care. However, data recording in web applications needs to be planned, and the automation of results through machine learning techniques is required.
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Affiliation(s)
- María Consuelo Sáiz-Manzanares
- DATAHES Research Group, Consolidated Research Unit Nº. 348, Departamento de Ciencias de la Salud, Facultad de Ciencias de la Salud, Universidad de Burgos, 09001 Burgos, Spain;
| | | | - María Camino Escolar-Llamazares
- DATAHES Research Group, Consolidated Research Unit Nº. 348, Departamento de Ciencias de la Salud, Facultad de Ciencias de la Salud, Universidad de Burgos, 09001 Burgos, Spain;
| | - Francisco Alcantud Marín
- Department of Developmental and Educational Psychology, Universitat de València, 46010 València, Spain;
| | - Sandra Rodríguez-Arribas
- BEST-AI Research Group, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, 09006 Burgos, Spain;
| | - Rut Velasco-Saiz
- Facultad de Ciencias de la Salud, Universidad de Burgos, 09001 Burgos, Spain;
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10
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Moldt JA, Festl-Wietek T, Madany Mamlouk A, Nieselt K, Fuhl W, Herrmann-Werner A. Chatbots for future docs: exploring medical students' attitudes and knowledge towards artificial intelligence and medical chatbots. MEDICAL EDUCATION ONLINE 2023; 28:2182659. [PMID: 36855245 PMCID: PMC9979998 DOI: 10.1080/10872981.2023.2182659] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/06/2023] [Accepted: 02/16/2023] [Indexed: 06/18/2023]
Abstract
Artificial intelligence (AI) in medicine and digital assistance systems such as chatbots will play an increasingly important role in future doctor - patient communication. To benefit from the potential of this technical innovation and ensure optimal patient care, future physicians should be equipped with the appropriate skills. Accordingly, a suitable place for the management and adaptation of digital assistance systems must be found in the medical education curriculum. To determine the existing levels of knowledge of medical students about AI chatbots in particular in the healthcare setting, this study surveyed medical students of the University of Luebeck and the University Hospital of Tuebingen. Using standardized quantitative questionnaires and qualitative analysis of group discussions, the attitudes of medical students toward AI and chatbots in medicine were investigated. From this, relevant requirements for the future integration of AI into the medical curriculum could be identified. The aim was to establish a basic understanding of the opportunities, limitations, and risks, as well as potential areas of application of the technology. The participants (N = 12) were able to develop an understanding of how AI and chatbots will affect their future daily work. Although basic attitudes toward the use of AI were positive, the students also expressed concerns. There were high levels of agreement regarding the use of AI in administrative settings (83.3%) and research with health-related data (91.7%). However, participants expressed concerns that data protection may be insufficiently guaranteed (33.3%) and that they might be increasingly monitored at work in the future (58.3%). The evaluations indicated that future physicians want to engage more intensively with AI in medicine. In view of future developments, AI and data competencies should be taught in a structured way during the medical curriculum and integrated into curricular teaching.
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Affiliation(s)
| | | | - Amir Madany Mamlouk
- Institute for Neuro- and Bioinformatics, University of Luebeck, Luebeck, Germany
| | - Kay Nieselt
- Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Germany
| | - Wolfgang Fuhl
- Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Germany
| | - Anne Herrmann-Werner
- University of Tuebingen, Tuebingen, Germany
- Department of Internal Medicine VI/Psychosomatic Medicine and Psychotherapy, University Hospital Tuebingen, Tuebingen, Germany
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11
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Biswas A, Kumari A, Gaikwad DS, Pandey DK. Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2023; 27:550-569. [PMID: 38100404 DOI: 10.1089/omi.2023.0197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
With climate emergency, COVID-19, and the rise of planetary health scholarship, the binary of human and ecosystem health has been deeply challenged. The interdependence of human and nonhuman animal health is increasingly acknowledged and paving the way for new frontiers in integrative biology. The convergence of genomics in health, bioinformatics, agriculture, and artificial intelligence (AI) has ushered in a new era of possibilities and applications. However, the sheer volume of genomic/multiomics big data generated also presents formidable sociotechnical challenges in extracting meaningful biological, planetary health and ecological insights. Over the past few years, AI-guided bioinformatics has emerged as a powerful tool for managing, analyzing, and interpreting complex biological datasets. The advances in AI, particularly in machine learning and deep learning, have been transforming the fields of genomics, planetary health, and agriculture. This article aims to unpack and explore the formidable range of possibilities and challenges that result from such transdisciplinary integration, and emphasizes its radically transformative potential for human and ecosystem health. The integration of these disciplines is also driving significant advancements in precision medicine and personalized health care. This presents an unprecedented opportunity to deepen our understanding of complex biological systems and advance the well-being of all life in planetary ecosystems. Notwithstanding in mind its sociotechnical, ethical, and critical policy challenges, the integration of genomics, multiomics, planetary health, and agriculture with AI-guided bioinformatics opens up vast opportunities for transnational collaborative efforts, data sharing, analysis, valorization, and interdisciplinary innovations in life sciences and integrative biology.
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Affiliation(s)
- Aakanksha Biswas
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
| | - Aditi Kumari
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
| | - D S Gaikwad
- Amity Institute of Organic Agriculture, Amity University, Noida, India
| | - Dhananjay K Pandey
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
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12
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Giacobbe DR, Zhang Y, de la Fuente J. Explainable artificial intelligence and machine learning: novel approaches to face infectious diseases challenges. Ann Med 2023; 55:2286336. [PMID: 38010090 PMCID: PMC10836268 DOI: 10.1080/07853890.2023.2286336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/16/2023] [Indexed: 11/29/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) are revolutionizing human activities in various fields, with medicine and infectious diseases being not exempt from their rapid and exponential growth. Furthermore, the field of explainable AI and ML has gained particular relevance and is attracting increasing interest. Infectious diseases have already started to benefit from explainable AI/ML models. For example, they have been employed or proposed to better understand complex models aimed at improving the diagnosis and management of coronavirus disease 2019, in the field of antimicrobial resistance prediction and in quantum vaccine algorithms. Although some issues concerning the dichotomy between explainability and interpretability still require careful attention, an in-depth understanding of how complex AI/ML models arrive at their predictions or recommendations is becoming increasingly essential to properly face the growing challenges of infectious diseases in the present century.
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Affiliation(s)
- Daniele Roberto Giacobbe
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
- Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Italy
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
- School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - José de la Fuente
- SaBio (Health and Biotechnology), Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ciudad Real, Spain
- Department of Veterinary Pathobiology, Center for Veterinary Health Sciences, Oklahoma State University, Stillwater, OK, USA
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13
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Gellert GA, Rasławska-Socha J, Marcjasz N, Price T, Kuszczyński K, Młodawska A, Jędruch A, Orzechowski PM. How Virtual Triage Can Improve Patient Experience and Satisfaction: A Narrative Review and Look Forward. TELEMEDICINE REPORTS 2023; 4:292-306. [PMID: 37817871 PMCID: PMC10561746 DOI: 10.1089/tmr.2023.0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/21/2023] [Indexed: 10/12/2023]
Abstract
Objective To complete a review of the literature on patient experience and satisfaction as relates to the potential for virtual triage (VT) or symptom checkers to enhance and enable improvements in these important health care delivery objectives. Methods Review and synthesis of the literature on patient experience and satisfaction as informed by emerging evidence, indicating potential for VT to favorably impact these clinical care objectives and outcomes. Results/Conclusions VT enhances potential clinical effectiveness through early detection and referral, can reduce avoidable care delivery due to late clinical presentation, and can divert primary care needs to more clinically appropriate outpatient settings rather than high-acuity emergency departments. Delivery of earlier and faster, more acuity level-appropriate care, as well as patient avoidance of excess care acuity (and associated cost), offer promise as contributors to improved patient experience and satisfaction. The application of digital triage as a front door to health care delivery organizations offers care engagement that can help reduce patient need to visit a medical facility for low-acuity conditions more suitable for self-care, thus avoiding unpleasant queues and reducing microbiological and other patient risks associated with visits to medical facilities. VT also offers an opportunity for providers to make patient health care experiences more personalized.
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14
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Khanna A, Jones G. Toward Personalized Medicine Approaches for Parkinson Disease Using Digital Technologies. JMIR Form Res 2023; 7:e47486. [PMID: 37756050 PMCID: PMC10568402 DOI: 10.2196/47486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 09/03/2023] [Accepted: 09/05/2023] [Indexed: 09/28/2023] Open
Abstract
Parkinson disease (PD) is a complex neurodegenerative disorder that afflicts over 10 million people worldwide, resulting in debilitating motor and cognitive impairment. In the United States alone (with approximately 1 million cases), the economic burden for treating and caring for persons with PD exceeds US $50 billion and myriad therapeutic approaches are under development, including both symptomatic- and disease-modifying agents. The challenges presented in addressing PD are compounded by observations that numerous, statistically distinct patient phenotypes present with a wide variety of motor and nonmotor symptomatic profiles, varying responses to current standard-of-care symptom-alleviating medications (L-DOPA and dopaminergic agonists), and different disease trajectories. The existence of these differing phenotypes highlights the opportunities in personalized approaches to symptom management and disease control. The prodromal period of PD can span across several decades, allowing the potential to leverage the unique array of composite symptoms presented to trigger early interventions. This may be especially beneficial as disease progression in PD (alongside Alzheimer disease and Huntington disease) may be influenced by biological processes such as oxidative stress, offering the potential for individual lifestyle factors to be tailored to delay disease onset. In this viewpoint, we offer potential scenarios where emerging diagnostic and monitoring strategies might be tailored to the individual patient under the tenets of P4 medicine (predict, prevent, personalize, and participate). These approaches may be especially relevant as the causative factors and biochemical pathways responsible for the observed neurodegeneration in patients with PD remain areas of fluid debate. The numerous observational patient cohorts established globally offer an excellent opportunity to test and refine approaches to detect, characterize, control, modify the course, and ultimately stop progression of this debilitating disease. Such approaches may also help development of parallel interventive strategies in other diseases such as Alzheimer disease and Huntington disease, which share common traits and etiologies with PD. In this overview, we highlight near-term opportunities to apply P4 medicine principles for patients with PD and introduce the concept of composite orthogonal patient monitoring.
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Affiliation(s)
- Amit Khanna
- Neuroscience Global Drug Development, Novartis Pharma AG, Basel, Switzerland
| | - Graham Jones
- GDD Connected Health and Innovation Group, Novartis Pharmaceuticals, East Hanover, NJ, United States
- Clinical and Translational Science Institute, Tufts University Medical Center, Boston, MA, United States
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15
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García-Closas M, Ahearn TU, Gaudet MM, Hurson AN, Balasubramanian JB, Choudhury PP, Gerlanc NM, Patel B, Russ D, Abubakar M, Freedman ND, Wong WSW, Chanock SJ, Berrington de Gonzalez A, Almeida JS. Moving Toward Findable, Accessible, Interoperable, Reusable Practices in Epidemiologic Research. Am J Epidemiol 2023; 192:995-1005. [PMID: 36804665 PMCID: PMC10505418 DOI: 10.1093/aje/kwad040] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 11/28/2022] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
Data sharing is essential for reproducibility of epidemiologic research, replication of findings, pooled analyses in consortia efforts, and maximizing study value to address multiple research questions. However, barriers related to confidentiality, costs, and incentives often limit the extent and speed of data sharing. Epidemiological practices that follow Findable, Accessible, Interoperable, Reusable (FAIR) principles can address these barriers by making data resources findable with the necessary metadata, accessible to authorized users, and interoperable with other data, to optimize the reuse of resources with appropriate credit to its creators. We provide an overview of these principles and describe approaches for implementation in epidemiology. Increasing degrees of FAIRness can be achieved by moving data and code from on-site locations to remote, accessible ("Cloud") data servers, using machine-readable and nonproprietary files, and developing open-source code. Adoption of these practices will improve daily work and collaborative analyses and facilitate compliance with data sharing policies from funders and scientific journals. Achieving a high degree of FAIRness will require funding, training, organizational support, recognition, and incentives for sharing research resources, both data and code. However, these costs are outweighed by the benefits of making research more reproducible, impactful, and equitable by facilitating the reuse of precious research resources by the scientific community.
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Affiliation(s)
- Montserrat García-Closas
- Correspondence to Dr. Montserrat García-Closas, Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20850 (e-mail: )
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16
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Schwamm LH, Silva GS. Advances in Digital Health. Stroke 2023; 54:870-872. [PMID: 36848430 DOI: 10.1161/strokeaha.123.042098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Affiliation(s)
- Lee H Schwamm
- Mass General Hospital, Harvard Medical School, Boston, MA (L.H.S.)
| | - Gisele Sampaio Silva
- Federal University of São Paulo, and Albert Einstein Hospital, SP, Brazil (G.S.S.)
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17
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Aguilera-Cobos L, García-Sanz P, Rosario-Lozano MP, Claros MG, Blasco-Amaro JA. An innovative framework to determine the implementation level of personalized medicine: A systematic review. Front Public Health 2023; 11:1039688. [PMID: 36817923 PMCID: PMC9936069 DOI: 10.3389/fpubh.2023.1039688] [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: 09/08/2022] [Accepted: 01/16/2023] [Indexed: 02/05/2023] Open
Abstract
Background Personalized medicine (PM) is now the new frontier in patient care. The application of this new paradigm extends to various pathologies and different patient care phases, such as diagnosis and treatment. Translating biotechnological advances to clinical routine means adapting health services at all levels is necessary. Purpose This article aims to identify the elements for devising a framework that will allow the level of PM implementation in the country under study to be quantitatively and qualitatively assessed and that can be used as a guideline for future implementation plans. Methods A systematic review was conducted per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The research question was: What are the domains for determining the level of implementation of PM at the national level? The domains for assessing the degree of PM implementation, which would form the framework, were established. Results 19 full-text studies that met the inclusion criteria were peer-selected in the systematic review. From all the studies that were included, 37 elements-encompassed in 11 domains-were extracted for determining the degree of PM implementation. These domains and their constituent elements comprise the qualitative and quantitative assessment framework presented herein. Each of the elements can be assessed individually. On the other hand, the domains were standardized to all have the same weight in an overall assessment. Conclusions A framework has been developed that takes a multi-factorial approach to determine the degree of implementation of PM at the national level. This framework could also be used to rank countries and their implementation strategies according to the score they receive in the application of the latter. It could also be used as a guide for developing future national PM implementation strategies. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022338611, Identifier: CRD42022338611.
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Affiliation(s)
- Lorena Aguilera-Cobos
- Health Technology Assessment Area-AETSA, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain,Department of Molecular Biology and Biochemistry, Universidad de Málaga, Málaga, Spain,*Correspondence: Lorena Aguilera-Cobos ✉
| | - Patricia García-Sanz
- Health Technology Assessment Area-AETSA, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain,Patricia García-Sanz ✉
| | - María Piedad Rosario-Lozano
- Health Technology Assessment Area-AETSA, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
| | - M. Gonzalo Claros
- Department of Molecular Biology and Biochemistry, Universidad de Málaga, Málaga, Spain,Institute of Biomedical Research in Málaga (IBIMA), Málaga, Spain,Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Málaga, Spain,Institute for Mediterranean and Subtropical Horticulture “La Mayora”, Universidad de Málaga-Consejo Superior de Investigaciones Científicas (IHSM-UMA-CSIC), Málaga, Spain
| | - Juan Antonio Blasco-Amaro
- Health Technology Assessment Area-AETSA, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
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18
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Inteligencia artificial al servicio de la salud del futuro. REVISTA MÉDICA CLÍNICA LAS CONDES 2023. [DOI: 10.1016/j.rmclc.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023] Open
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19
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Xinxian C, Jianhui C. Digital Transformation and Financial Risk Prediction of Listed Companies. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7211033. [PMID: 36131896 PMCID: PMC9484935 DOI: 10.1155/2022/7211033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/07/2022] [Accepted: 07/22/2022] [Indexed: 11/18/2022]
Abstract
Digitalization is a revolution, a frontal battleground in the new global competitive landscape, and a long-distance race for which all employees must be prepared, and organizations must actively embrace the resulting changes. The article begins by analyzing three characteristics of digital transformation and enterprise growth: the heterogeneity of digital transformation's impact on enterprise growth and the process by which digital transformation influences enterprise growth. In addition, this article develops a convolutional neural network-based financial early warning model to aid businesses' digital transformation initiatives.
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Affiliation(s)
- Chen Xinxian
- School of Accounting, Guangdong University of Finance, Guangzhou, Guangdong 510521, China
| | - Cai Jianhui
- School of Tourism Management, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China
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20
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Lin B, Ma Y, Wu S. Multi-Omics and Artificial Intelligence-Guided Data Integration in Chronic Liver Disease: Prospects and Challenges for Precision Medicine. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:415-421. [PMID: 35925812 DOI: 10.1089/omi.2022.0079] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Chronic liver disease (CLD) is a significant planetary health burden. CLD includes a broad range of liver pathologies from different causes, for example, hepatitis B virus infection, fatty liver disease, hepatocellular carcinoma, and nonalcoholic fatty liver disease or the metabolic associated fatty liver disease. Biomarker and diagnostic discovery, and new molecular targets for precision treatments are timely and sorely needed in CLD. In this context, multi-omics data integration is increasingly being facilitated by artificial intelligence (AI) and attendant digital transformation of systems science. While the digital transformation of multi-omics integrative analyses is still in its infancy, there are noteworthy prospects, hope, and challenges for diagnostic and therapeutic innovation in CLD. This expert review aims at the emerging knowledge frontiers as well as gaps in multi-omics data integration at bulk tissue levels, and those including single cell-level data, gut microbiome data, and finally, those incorporating tissue-specific information. We refer to AI and related digital transformation of the CLD research and development field whenever possible. This review of the emerging frontiers at the intersection of systems science and digital transformation informs future roadmaps to bridge digital technology discovery and clinical omics applications to benefit planetary health and patients with CLD.
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Affiliation(s)
- Biaoyang Lin
- Zhejiang California International Nanosystems Institute (ZCNI) Proprium Research Center, Zhejiang University, Hangzhou, China
- Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Department of Urology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Yingying Ma
- Zhejiang California International Nanosystems Institute (ZCNI) Proprium Research Center, Zhejiang University, Hangzhou, China
- Hangzhou Proprium Biotech Co. Ltd., Hangzhou, China
| | - ShengJun Wu
- Department of Clinical Laboratories, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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21
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Geanta M, Tanwar AS, Lehrach H, Satyamoorthy K, Brand A. Horizon Scanning: Rise of Planetary Health Genomics and Digital Twins for Pandemic Preparedness. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:93-100. [PMID: 34851750 DOI: 10.1089/omi.2021.0062] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The Covid-19 pandemic accelerated research and development not only in infectious diseases but also in digital technologies to improve monitoring, forecasting, and intervening on planetary and ecological risks. In the European Commission, the Destination Earth (DestinE) is a current major initiative to develop a digital model of the Earth (a "digital twin") with high precision. Moreover, omics systems science is undergoing digital transformation impacting nearly all dimensions of the field, including real-time phenotype capture to data analytics using machine learning and artificial intelligence, to name but a few emerging frontiers. We discuss the ways in which the current ongoing digital transformation in omics offers synergies with digital twins/DestinE. Importantly, we note here the rise of a new field of scholarship, planetary health genomics. We conclude that digital transformation in public and private sectors, digital twins/DestinE, and their convergence with omics systems science are poised to build robust capacities for pandemic preparedness and resilient societies in the 21st century.
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Affiliation(s)
- Marius Geanta
- Centre for Innovation in Medicine, Bucharest, Romania
- KOL Medical Media, Bucharest, Romania
- United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology, Maastricht, The Netherlands
| | - Ankit Singh Tanwar
- United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology, Maastricht, The Netherlands
- Department of Public Health Genomics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Hans Lehrach
- Max Planck Institute for Molecular Genetics (MPIMG), Berlin, Germany
- Alacris Theranostics GmbH, Berlin, Germany
| | - Kapaettu Satyamoorthy
- Department of Cell and Molecular Biology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Angela Brand
- United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology, Maastricht, The Netherlands
- Department of Public Health Genomics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
- Dr. TMA Pai Endowment Chair in Public Health Genomics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
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22
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Probst-Hensch N, Bochud M, Chiolero A, Crivelli L, Dratva J, Flahault A, Frey D, Kuenzli N, Puhan M, Suggs LS, Wirth C. Swiss Cohort & Biobank - The White Paper. Public Health Rev 2022; 43:1605660. [PMID: 36619237 PMCID: PMC9817110 DOI: 10.3389/phrs.2022.1605660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- Nicole Probst-Hensch
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Allschwil, Switzerland
- University of Basel, Basel, Switzerland
- Swiss School of Public Health (SSPH+), Zürich, Switzerland
- Swiss Society for Public Health, Bern, Switzerland
- *Correspondence: Nicole Probst-Hensch,
| | - Murielle Bochud
- Swiss School of Public Health (SSPH+), Zürich, Switzerland
- Swiss Society for Public Health, Bern, Switzerland
- Department of Epidemiology and Health Systems (DESS), University Center for General Medicine and Public Health (Unisanté), Lausanne, Switzerland
| | - Arnaud Chiolero
- Swiss School of Public Health (SSPH+), Zürich, Switzerland
- Swiss Society for Public Health, Bern, Switzerland
- Population Health Laboratory (#PopHealthLab), University of Fribourg, Fribourg, Switzerland
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
| | - Luca Crivelli
- Swiss School of Public Health (SSPH+), Zürich, Switzerland
- Swiss Society for Public Health, Bern, Switzerland
- Department of Business Economics, Health and Social Care, University of Applied Sciences and Arts of Southern Switzerland, Manno, Switzerland
- Institute of Public Health Università della Svizzera Italiana, Lugano, Switzerland
| | - Julia Dratva
- Swiss School of Public Health (SSPH+), Zürich, Switzerland
- Swiss Society for Public Health, Bern, Switzerland
- Institute of Public Health, Department of Health Sciences, ZHAW Zürich University of Applied Sciences, Winterthur, Switzerland
| | - Antoine Flahault
- Swiss School of Public Health (SSPH+), Zürich, Switzerland
- Swiss Society for Public Health, Bern, Switzerland
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Daniel Frey
- Swiss Society for Public Health, Bern, Switzerland
| | - Nino Kuenzli
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Allschwil, Switzerland
- University of Basel, Basel, Switzerland
- Swiss School of Public Health (SSPH+), Zürich, Switzerland
- Swiss Society for Public Health, Bern, Switzerland
| | - Milo Puhan
- Swiss School of Public Health (SSPH+), Zürich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute (EBPI), University of Zurich, Zurich, Switzerland
| | - L. Suzanne Suggs
- Swiss School of Public Health (SSPH+), Zürich, Switzerland
- Swiss Society for Public Health, Bern, Switzerland
- Institute of Public Health Università della Svizzera Italiana, Lugano, Switzerland
| | - Corina Wirth
- Swiss Society for Public Health, Bern, Switzerland
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23
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Biçer Ş, Yıldırım A. Digital Death and Thanatechnology: New Ways of Thinking About Data (Im)Mortality and Digital Transformation. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2021; 26:88-92. [PMID: 34171977 DOI: 10.1089/omi.2021.0096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Digital technologies such as the Internet of Things and artificial intelligence are changing how we live and do research, for example, the ways in which patient-reported outcomes and phenomics big data are curated and analyzed. Digital transformation is everywhere and is reshaping data (im)mortality in a wide range of sectors in medicine, engineering, journalism, and beyond. In this context, thanatechnology is a term introduced by Carla Sofka over two decades ago, referring to "any kind of technology that can be used to deal with death, dying, grief, loss, and illness." The field of thanatechnology has become relevant in the digital age as social media is full of accounts from dead individuals, whereas digital media is often harnessed as a source of data and metadata, and in times of pandemics and normalcy. Emerging macroscale analyses forecast billions of social media user accounts from deceased persons in the current century. What happens to digital remains of persons once they cease to exist physically? Digital death, or its absence in the case of deceased individuals, becomes a challenge for both data availability and veracity, and confound research and public health services. Data (im)mortality and digital death are also relevant for research on past events of significance for public health, for example, to discern the history of pandemics and ecological threats. This article examines and calls for new ways of thinking about digital death and thanatechnology as integral dimensions of digital transformation in medicine, new media studies, and society in the 21st century.
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Affiliation(s)
- Şehmus Biçer
- Department of Media and Cultural Studies, School of Graduate Studies, Çanakkale On Sekiz Mart University, Çanakkale, Turkey
| | - Arif Yıldırım
- Department of Journalism, Faculty of Communication, Çanakkale On Sekiz Mart University, Çanakkale, Turkey
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