bims-arines 2024-09-29 papers

Issue of 2024–09–29
six papers selected by
Farhad Shokraneh

J Clin Epidemiol. 2024 Sep 19. pii: S0895-4356(24)00294-4. [Epub ahead of print] 111538

Machine Learning Approaches to Evaluate Heterogeneous Treatment Effects in Randomized Controlled Trials: A Scoping Review.

Kosuke Inoue, Motohiko Adomi, Orestis Efthimiou, Toshiaki Komura, Kenji Omae, Akira Onishi, Yusuke Tsutsumi, Tomoko Fujii, Naoki Kondo, Toshi A Furukawa.

BACKGROUND: Estimating heterogeneous treatment effects (HTEs) in randomized controlled trials (RCTs) has received substantial attention recently. This has led to the development of several statistical and machine learning (ML) algorithms to assess HTEs through identifying individualized treatment effects. However, a comprehensive review of these algorithms is lacking. We thus aimed to catalog and outline currently available statistical and ML methods for identifying HTEs via effect modeling using clinical RCT data and summarize how they have been applied in practice.
STUDY DESIGN AND SETTING: We performed a scoping review using pre-specified search terms in MEDLINE and Embase, aiming to identify studies that assessed HTEs using advanced statistical and ML methods in RCT data published from 2010 to 2022.
RESULTS: Among a total of 32 studies identified in the review, 17 studies applied existing algorithms to RCT data, and 15 extended existing algorithms or proposed new algorithms. Applied algorithms included penalized regression, causal forest, Bayesian causal forest, and other meta-learner frameworks. Of these methods, causal forest was the most frequently used (7 studies) followed by Bayesian causal forest (4 studies). Most applications were in cardiology (6 studies), followed by psychiatry (4 studies). We provide example R codes to illustrate how to implement these algorithms.
CONCLUSION: This review identified and outlined various algorithms currently used to identify HTEs and individualized treatment effects in RCT data. Given the increasing availability of new algorithms, analysts should carefully select them after examining model performance and considering how the models will be used in practice.

Keywords: Heterogeneous treatment effect; Individualized treatment effect; Machine learning; Randomized controlled trial; personalized medicine

DOI: https://doi.org/10.1016/j.jclinepi.2024.111538

Zhonghua Liu Xing Bing Xue Za Zhi. 2024 Sep 10. 45(9): 1321-1326

[Progress in application of machine learning in epidemiology].

K T Mai, X T Liu, X Y Lin, S Y Liu, C K Zhao, J B Du.

Population based health data collection and analysis are important in epidemiological research. In recent years, with the rapid development of big data, Internet and cloud computing, artificial intelligence has gradually attracted attention of epidemiological researchers. More and more researchers are trying to use artificial intelligence algorithms for genome sequencing and medical image data mining, and for disease diagnosis, risk prediction and others. In recent years, machine learning, a branch of artificial intelligence, has been widely used in epidemiological research. This paper summarizes the key fields and progress in the application of machine learning in epidemiology, reviews the development history of machine learning, analyzes the classic cases and current challenges in its application in epidemiological research, and introduces the current application scenarios and future development trends of machine learning and artificial intelligence algorithms for the better exploration of the epidemiological research value of massive medical health data in China.

DOI: https://doi.org/10.3760/cma.j.cn112338-20240322-00148

Nucl Med Mol Imaging. 2024 Oct;58(6): 323-331

How to Harness the Power of GPT for Scientific Research: A Comprehensive Review of Methodologies, Applications, and Ethical Considerations.

Ki-Seong Park, Hongyoon Choi.

The rapid advancements in natural language processing, particularly with the development of Generative Pre-trained Transformer (GPT) models, have opened up new avenues for researchers across various domains. This review article explores the potential of GPT as a research tool, focusing on the core functionalities, key features, and real-world applications of the GPT-4 model. We delve into the concept of prompt engineering, a crucial technique for effectively utilizing GPT, and provide guidelines for designing optimal prompts. Through case studies, we demonstrate how GPT can be applied at various stages of the research process, including literature review, data analysis, and manuscript preparation. The utilization of GPT is expected to enhance research efficiency, stimulate creative thinking, facilitate interdisciplinary collaboration, and increase the impact of research findings. However, it is essential to view GPT as a complementary tool rather than a substitute for human expertise, keeping in mind its limitations and ethical considerations. As GPT continues to evolve, researchers must develop a deep understanding of this technology and leverage its potential to advance their research endeavors while being mindful of its implications.

Keywords: Generative Pre-trained Transformer (GPT); Interdisciplinary Collaboration; Natural Language Processing; Prompt Engineering; Research Efficiency

DOI: https://doi.org/10.1007/s13139-024-00876-z

J Med Libr Assoc. 2024 Jul 01. 112(3): 275-280

Large-scale systematic review support for guideline development in diabetes precision medicine.

Maria Björklund, Krister Aronsson.

Background: Involving librarians as team members can lead to better quality in reviews. To improve their search results, an international diabetes project involved two medical librarians in a large-scale project planning of a series of systematic reviews for clinical guidelines in diabetes precision medicine.
Case Presentation: The precision diabetes project was divided into teams. Four diabetes mellitus types (type 1, type 2, gestational, and monogenic) were divided into teams focusing on diagnostics, prevention, treatment, or prognostics. A search consultation plan was set up for the project to help organize the work. We performed searches in Embase and PubMed for 14 teams, building complex searches that involved non-traditional search strategies. Our search strategies generated very large amounts of records that created challenges in balancing sensitivity with precision. We also performed overlap searches for type 1 and type 2 diabetes search strategies; and assisted in setting up reviews in the Covidence tool for screening.
Conclusions: This project gave us opportunities to test methods we had not used before, such as overlap comparisons between whole search strategies. It also gave us insights into the complexity of performing a search balancing sensitivity and specificity and highlights the need for a clearly defined communication plan for extensive evidence synthesis projects.

Keywords: Systematic review methodology; online collaboration; project management; role of information specialist; search strategy development; teamwork

DOI: https://doi.org/10.5195/jmla.2024.1863

Future Healthc J. 2024 Sep;11(3): 100182

Bibliometric analysis of artificial intelligence in healthcare research: Trends and future directions.

Renganathan Senthil, Thirunavukarasou Anand, Chaitanya Sree Somala, Konda Mani Saravanan.

Objective: The presence of artificial intelligence (AI) in healthcare is a powerful and game-changing force that is completely transforming the industry as a whole. Using sophisticated algorithms and data analytics, AI has unparalleled prospects for improving patient care, streamlining operational efficiency, and fostering innovation across the healthcare ecosystem. This study conducts a comprehensive bibliometric analysis of research on AI in healthcare, utilising the SCOPUS database as the primary data source.
Methods: Preliminary findings from 2013 identified 153 publications on AI and healthcare. Between 2019 and 2023, the number of publications increased exponentially, indicating significant growth and development in the field. The analysis employs various bibliometric indicators to assess research production performance, science mapping techniques, and thematic mapping analysis.
Results: The study reveals insights into research hotspots, thematic focus, and emerging trends in AI and healthcare research. Based on an extensive examination of the Scopus database provides a brief overview and suggests potential avenues for further investigation.
Conclusion: This article provides valuable contributions to understanding the current landscape of AI in healthcare, offering insights for future research directions and informing strategic decision making in the field.

Keywords: Artificial intelligence; Bibliometric analysis; COVID-19; Emerging trends; Scientific production

DOI: https://doi.org/10.1016/j.fhj.2024.100182

World J Methodol. 2024 Sep 20. 14(3): 94071

Ethical framework for artificial intelligence in healthcare research: A path to integrity.

Ahmad A Abujaber, Abdulqadir J Nashwan.

The integration of Artificial Intelligence (AI) into healthcare research promises unprecedented advancements in medical diagnostics, treatment personalization, and patient care management. However, these innovations also bring forth significant ethical challenges that must be addressed to maintain public trust, ensure patient safety, and uphold data integrity. This article sets out to introduce a detailed framework designed to steer governance and offer a systematic method for assuring that AI applications in healthcare research are developed and executed with integrity and adherence to medical research ethics.

Keywords: Artificial intelligence; Ethical Principles; Ethical framework; Healthcare research; Integrity; Patient safety

DOI: https://doi.org/10.5662/wjm.v14.i3.94071