Most Read (Last 3 years)

  • Original Article2024-11-01 Laboratory Informatics

    Laboratory Preparation for Digital Medicine in Healthcare 4.0: An Investigation Into the Awareness and Applications of Big Data and Artificial Intelligence

    Shinae Yu , M.D., Byung Ryul Jeon , M.D., Ph.D., Changseung Liu , M.D., Dokyun Kim , M.D., Ph.D., Hae-Il Park , M.D., Ph.D., Hyung Doo Park , M.D., Ph.D., Jeong Hwan Shin , M.D., Ph.D., Jun Hyung Lee , M.D., Ph.D., Qute Choi , M.D., Ph.D., Sollip Kim , M.D., Ph.D., Yeo Min Yun , M.D., Ph.D., and Eun-Jung Cho , M.D., Ph.D.; On behalf of the Evidence-Based Laboratory Medicine Committee of the Korean Society for Laboratory Medicine

    Ann Lab Med 2024; 44(6): 562-571

    Abstract : Background: Healthcare 4.0. refers to the integration of advanced technologies, such as artificial intelligence (AI) and big data analysis, into the healthcare sector. Recognizing the impact of Healthcare 4.0 technologies in laboratory medicine (LM), we seek to assess the overall awareness and implementation of Healthcare 4.0 among members of the Korean Society for Laboratory Medicine (KSLM).Methods: A web-based survey was conducted using an anonymous questionnaire. The survey comprised 36 questions covering demographic information (seven questions), big data (10 questions), and AI (19 questions).Results: In total, 182 (17.9%) of 1,017 KSLM members participated in the survey. Thirty-two percent of respondents considered AI to be the most important technology in LM in the era of Healthcare 4.0, closely followed by 31% who favored big data. Approximately 80% of respondents were familiar with big data but had not conducted research using it, and 71% were willing to participate in future big data research conducted by the KSLM. Respondents viewed AI as the most valuable tool in molecular genetics within various divisions. More than half of the respondents were open to the notion of using AI as assistance rather than a complete replacement for their roles.Conclusions: This survey highlighted KSLM members’ awareness of the potential applications and implications of big data and AI. We emphasize the complexity of AI integration in healthcare, citing technical and ethical challenges leading to diverse opinions on its impact on employment and training. This highlights the need for a holistic approach to adopting new technologies.

  • Opinion2024-11-01 Laboratory Informatics

    Why Terminology Standards Matter for Data-driven Artificial Intelligence in Healthcare

    Hyeoun-Ae Park , Ph.D.

    Ann Lab Med 2024; 44(6): 467-471
  • Opinion2024-11-01 Laboratory Informatics

    Customized Quality Assessment of Healthcare Data

    Jieun Shin , Ph.D. and Jong-Yeup Kim , M.D., Ph.D.

    Ann Lab Med 2024; 44(6): 472-477
  • Original Article2025-03-01 Laboratory Informatics

    A Machine Learning Approach for Predicting In-Hospital Cardiac Arrest Using Single-Day Vital Signs, Laboratory Test Results, and International Classification of Disease-10 Block for Diagnosis

    Haeil Park , M.D., Ph.D. and Chan Seok Park , M.D., Ph.D.

    Ann Lab Med 2025; 45(2): 209-217

    Abstract : Background: Predicting in-hospital cardiac arrest (IHCA) is crucial for potentially reducing mortality and improving patient outcomes. However, most models, which rely solely on vital signs, may not comprehensively capture the patients’ risk profiles. We aimed to improve IHCA predictions by combining vital sign indicators with laboratory test results and, optionally, International Classification of Disease-10 block for diagnosis (ICD10BD).Methods: We conducted a retrospective cohort study in the general ward (GW) and intensive care unit (ICU) of a 680-bed secondary healthcare institution. We included 62,061 adults admitted to the Department of Internal Medicine from January 2010 to August 2022. IHCAs were identified based on cardiopulmonary resuscitation prescriptions. Patient-days within three days preceding IHCAs were labeled as case days; all others were control days. The eXtreme Gradient Boosting (XGBoost) model was trained using daily vital signs, 14 laboratory test results, and ICD10BD.Results: In the GW, among 1,299,448 patient-days from 62,038 patients, 1,367 days linked to 713 patients were cases. In the ICU, among 117,190 patient-days from 16,881 patients, 1,119 days from 444 patients were cases. The area under the ROC curve for IHCA prediction model was 0.934 and 0.896 in the GW and ICU, respectively, using the combination of vital signs, laboratory test results, and ICD10BD; 0.925 and 0.878, respectively, with vital signs and laboratory test results; and 0.839 and 0.828, respectively, with only vital signs.Conclusions: Incorporating laboratory test results or combining laboratory test results and ICD10BD with vital signs as predictor variables in the XGBoost model potentially enhances clinical decision-making and improves patient outcomes in hospital settings.

  • Editorial2025-03-01 Laboratory Informatics

    Enhancing Clinical Cardiac Care: Predicting In-Hospital Cardiac Arrest With Machine Learning

    Sollip Kim , M.D., Ph.D.

    Ann Lab Med 2025; 45(2): 117-120
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Journal Information March, 2025
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