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Mapundu MT, Kabudula CW, Musenge E, Olago V, Celik T. Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing. PLoS One 2024; 19:e0308452. [PMID: 39298425 DOI: 10.1371/journal.pone.0308452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 07/24/2024] [Indexed: 09/21/2024] Open
Abstract
Verbal autopsy (VA) narratives play a crucial role in understanding and documenting the causes of mortality, especially in regions lacking robust medical infrastructure. In this study, we propose a comprehensive approach to extract mortality causes and identify prevalent diseases from VA narratives utilizing advanced text mining techniques, so as to better understand the underlying health issues leading to mortality. Our methodology integrates n-gram-based language processing, Latent Dirichlet Allocation (LDA), and BERTopic, offering a multi-faceted analysis to enhance the accuracy and depth of information extraction. This is a retrospective study that uses secondary data analysis. We used data from the Agincourt Health and Demographic Surveillance Site (HDSS), which had 16338 observations collected between 1993 and 2015. Our text mining steps entailed data acquisition, pre-processing, feature extraction, topic segmentation, and discovered knowledge. The results suggest that the HDSS population may have died from mortality causes such as vomiting, chest/stomach pain, fever, coughing, loss of weight, low energy, headache. Additionally, we discovered that the most prevalent diseases entailed human immunodeficiency virus (HIV), tuberculosis (TB), diarrhoea, cancer, neurological disorders, malaria, diabetes, high blood pressure, chronic ailments (kidney, heart, lung, liver), maternal and accident related deaths. This study is relevant in that it avails valuable insights regarding mortality causes and most prevalent diseases using novel text mining approaches. These results can be integrated in the diagnosis pipeline for ease of human annotation and interpretation. As such, this will help with effective informed intervention programmes that can improve primary health care systems and chronic based delivery, thus increasing life expectancy.
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Affiliation(s)
- Michael Tonderai Mapundu
- Department of Epidemiology and Biostatistics, School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | - Chodziwadziwa Whiteson Kabudula
- Department of Epidemiology and Biostatistics, School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), Johannesburg, South Africa
| | - Eustasius Musenge
- Department of Epidemiology and Biostatistics, School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | - Victor Olago
- National Health Laboratory Service (NHLS), National Cancer Registry, Johannesburg, South Africa
| | - Turgay Celik
- Wits Institute of Data Science, University of The Witwatersrand, Johannesburg, South Africa
- School of Electrical and Information Engineering, University of The Witwatersrand, Johannesburg, South Africa
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Rony MKK, Numan SM, Akter K, Tushar H, Debnath M, Johra FT, Akter F, Mondal S, Das M, Uddin MJ, Begum J, Parvin MR. Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare. Heliyon 2024; 10:e36702. [PMID: 39281626 PMCID: PMC11400963 DOI: 10.1016/j.heliyon.2024.e36702] [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: 03/14/2024] [Revised: 08/08/2024] [Accepted: 08/20/2024] [Indexed: 09/18/2024] Open
Abstract
Background With the increasing integration of artificial intelligence (AI) technologies into healthcare systems, there is a growing emphasis on privacy and ethical considerations. Nurses, as frontline healthcare professionals, are pivotal in-patient care and offer valuable insights into the ethical implications of AI adoption. Objectives This study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings. Methods We employed Van Manen's hermeneutic phenomenology as the qualitative research approach. Data were collected through purposive sampling from the December 7, 2023 to the January 15, 2024, with interviews conducted in Bengali. Thematic analysis was utilized following member checking and an audit trail. Results Six themes emerged from the research findings: Ethical dimensions of AI integration, highlighting complexities in incorporating AI ethically; Privacy challenges in healthcare AI, revealing concerns about data security and confidentiality; Balancing innovation and ethical practice, indicating a need to reconcile technological advancements with ethical considerations; Human touch vs. technological progress, underscoring tensions between automation and personalized care; Patient-centered care in the AI era, emphasizing the importance of maintaining focus on patients amidst technological advancements; and Ethical preparedness and education, suggesting a need for enhanced training and education on ethical AI use in healthcare. Conclusions The findings underscore the importance of addressing privacy and ethical concerns in AI healthcare development. Nurses advocate for patient-centered approaches and collaborate with policymakers and tech developers to ensure responsible AI adoption. Further research is imperative for mitigating ethical challenges and promoting ethical AI in healthcare practice.
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Affiliation(s)
| | - Sharker Md Numan
- School of Science and Technology, Bangladesh Open University, Gazipur, Bangladesh
| | - Khadiza Akter
- Master of Public Health, Daffodil International University, Dhaka, Bangladesh
| | - Hasanuzzaman Tushar
- Department of Business Administration, International University of Business Agriculture and Technology, Dhaka, Bangladesh
| | - Mitun Debnath
- Master of Public Health, National Institute of Preventive and Social Medicine, Dhaka, Bangladesh
| | - Fateha Tuj Johra
- Masters in Disaster Management, University of Dhaka, Dhaka, Bangladesh
| | - Fazila Akter
- Dhaka Nursing College, Affiliated with the University of Dhaka, Bangladesh
| | - Sujit Mondal
- Master of Science in Nursing, National Institute of Advanced Nursing Education and Research Mugda, Dhaka, Bangladesh
| | - Mousumi Das
- Master of Public Health, Leading University, Sylhet, Bangladesh
| | - Muhammad Join Uddin
- Master of Public Health, RTM Al-Kabir Technical University, Sylhet, Bangladesh
| | - Jeni Begum
- Master of Public Health, Leading University, Sylhet, Bangladesh
| | - Mst Rina Parvin
- School of Medical Sciences, Shahjalal University of Science and Technology, Bangladesh
- Bangladesh Army (AFNS Officer), Combined Military Hospital, Dhaka, Bangladesh
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Rony MKK, Kayesh I, Bala SD, Akter F, Parvin MR. Artificial intelligence in future nursing care: Exploring perspectives of nursing professionals - A descriptive qualitative study. Heliyon 2024; 10:e25718. [PMID: 38370178 PMCID: PMC10869862 DOI: 10.1016/j.heliyon.2024.e25718] [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: 11/21/2023] [Revised: 01/29/2024] [Accepted: 02/01/2024] [Indexed: 02/20/2024] Open
Abstract
Background The healthcare landscape is rapidly evolving, with artificial intelligence (AI) emerging as a transformative force. In this context, understanding the viewpoints of nursing professionals regarding the integration of AI in future nursing care is crucial. Aims This study aimed to provide insights into the perceptions of nursing professionals regarding the role of AI in shaping the future of healthcare. Methods A cohort of 23 nursing professionals was recruited between April 7, 2023, and May 4, 2023, for this study. Employing a thematic analysis approach, qualitative data from interviews with nursing professionals were analyzed. Verbatim transcripts underwent rigorous coding, and these codes were organized into themes through constant comparative analysis. The themes were refined and developed through the grouping of related codes, ensuring an authentic representation of participants' viewpoints. Results After careful data analysis, ten key themes emerged including: (I) Perceptions of AI readiness; (II) Benefits and concerns; (III) Enhanced patient outcomes; (IV) Collaboration and workflow; (V) Human-tech balance: (VI) Training and skill development; (VII) Ethical and legal considerations; (VIII) AI implementation barriers; (IX) Patient-nurse relationships; (X) Future vision and adaptation. Conclusion This study provides valuable insights into nursing professionals' perspectives on the integration of AI in future nursing care. It highlights their enthusiasm for AI's potential benefits while emphasizing the importance of ethical and compassionate nursing practice. The findings underscore the need for comprehensive training programs to equip nursing professionals with the skills necessary for successful AI integration. Ultimately, this research contributes to the ongoing discourse on the role of AI in nursing, paving the way for a future where innovative technologies complement and enhance the delivery of patient-centered care.
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Affiliation(s)
- Moustaq Karim Khan Rony
- Master of Public Health, Bangladesh Open University, Gazipur, Bangladesh
- Institute of Social Welfare and Research, University of Dhaka, Dhaka, Bangladesh
| | - Ibne Kayesh
- Institute of Social Welfare and Research, University of Dhaka, Dhaka, Bangladesh
| | - Shuvashish Das Bala
- Associate Professor, International University of Business Agriculture and Technology, Dhaka, Bangladesh
| | - Fazila Akter
- Dhaka Nursing College, affiliated with the University of Dhaka, Bangladesh
| | - Mst Rina Parvin
- Afns Major at Bangladesh Army, Combined Military Hospital, Dhaka, Bangladesh
- School of Medical Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
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Pailaha AD. The Impact and Issues of Artificial Intelligence in Nursing Science and Healthcare Settings. SAGE Open Nurs 2023; 9:23779608231196847. [PMID: 37691725 PMCID: PMC10492460 DOI: 10.1177/23779608231196847] [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: 03/07/2023] [Revised: 04/26/2023] [Accepted: 08/06/2023] [Indexed: 09/12/2023] Open
Abstract
Research and development of artificial intelligence (AI)-based technologies systems in healthcare has increased over the past decade, highlighting the strong potential of AI to improve the quality of nursing care. To meet the new demands for nursing care, it is necessary that AI be integrated into nursing science and healthcare setting, especially in nursing care. The current challenge is to transform this expanded set of technology into clinical benefits for patients, through more advanced, accurate, practical, effective, efficient, and economical and personalized care. Along with the potential positive outcomes, AI technology also has unintended consequences that have the potential to negatively impact and adversely affect the nursing profession and the primary purpose of nursing practice in healthcare system. This aimed to explore and discuss the impact of applying AI in nursing science and healthcare system to provide approximate nursing care. Some of the impacts that can be evaluated and seen today in the context of using AI technology systems in the scope of nursing and healthcare are expanding access to quality medical care, improving medical records, and improving the quality of services. The use of AI technology systems also has some issues, such as bias and algorithms, which are drawbacks that need to be considered when evaluating the accuracy of the displayed results.
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Affiliation(s)
- Aprianto Daniel Pailaha
- Inpatient Department Nurse, General Ward Nursing, Department of Nursing, Siloam Hospitals Agora, Jakarta, Indonesia
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Golz C, Aarts S, Hacking C, Hahn S, Zwakhalen S. Health professionals' sentiments towards implemented information technologies in psychiatric hospitals: a text-mining analysis. BMC Health Serv Res 2022; 22:1426. [PMID: 36443845 PMCID: PMC9703739 DOI: 10.1186/s12913-022-08823-4] [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: 02/14/2022] [Accepted: 11/10/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Psychiatric hospitals are increasingly being digitalised. Digitalisation often requires changes at work for health professionals. A positive attitude from health professionals towards technology is crucial for a successful and sustainable digital transformation at work. Nevertheless, insufficient attention is being paid to the health professionals' sentiments towards technology. OBJECTIVE This study aims to identify the implemented technologies in psychiatric hospitals and to describe the health professionals' sentiments towards these implemented technologies. METHODS A text-mining analysis of semi-structured interviews with nurses, physicians and psychologists was conducted. The analysis comprised word frequencies and sentiment analyses. For the sentiment analyses, the SentimentWortschatz dataset was used. The sentiments ranged from -1 (strongly negative sentiment) to 1 (strongly positive sentiment). RESULTS In total, 20 health professionals (nurses, physicians and psychologists) participated in the study. When asked about the technologies they used, the participating health professionals mainly referred to the computer, email, phone and electronic health record. Overall, 4% of the words in the transcripts were positive or negative sentiments. Of all words that express a sentiment, 73% were positive. The discussed technologies were associated with positive and negative sentiments. However, of all sentences that described technology at the workplace, 69.4% were negative. CONCLUSIONS The participating health professionals mentioned a limited number of technologies at work. The sentiments towards technologies were mostly negative. The way in which technologies are implemented and the lack of health professionals' involvement seem to be reasons for the negative sentiments.
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Affiliation(s)
- C. Golz
- grid.424060.40000 0001 0688 6779Department of Health Professions, Bern University of Applied Sciences, Murtenstrasse 10, 3011 Bern, Switzerland
| | - S. Aarts
- grid.5012.60000 0001 0481 6099Department of Health Services Research, Maastricht University, Maastricht, The Netherlands ,grid.5012.60000 0001 0481 6099Living Lab in Ageing and Long-Term Care, Maastricht University, Maastricht, The Netherlands
| | - C. Hacking
- grid.5012.60000 0001 0481 6099Department of Health Services Research, Maastricht University, Maastricht, The Netherlands ,grid.5012.60000 0001 0481 6099Living Lab in Ageing and Long-Term Care, Maastricht University, Maastricht, The Netherlands
| | - S. Hahn
- grid.424060.40000 0001 0688 6779Department of Health Professions, Bern University of Applied Sciences, Murtenstrasse 10, 3011 Bern, Switzerland
| | - S.M.G. Zwakhalen
- grid.5012.60000 0001 0481 6099Department of Health Services Research, Maastricht University, Maastricht, The Netherlands ,grid.5012.60000 0001 0481 6099Living Lab in Ageing and Long-Term Care, Maastricht University, Maastricht, The Netherlands
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