The purpose of the study is to provide implications for multi-cultural education in Korea. So we comparatively analyze how multi-cultural education programs for student teachers are implemented in the teacher training institutions in Finland and South Korea. In order to achieve this purpose, the study collected and analyzed primary and secondary data of each educational institution’s programs by intentionally selecting primary teacher training institutions in two countries. The data from 3 institutions were analyzed according to the criteria based on previous studies. This study is meaningful in that it conducted a comparative analysis considering multi-cultural context and perspective in two countries, and suggested practical and progressive perspective to student teachers program.
Abstract Aim The introduction of biosimilars is expected to reduce the cost of biologic drugs, but the actual cost savings have not yet been quantified in Korea. The aim of this study was to estimate the annual cost savings attributed to the introduction of infliximab biosimilar. Methods We conducted a retrospective analysis using data from the Health Insurance Review and Assessment Service‐National Patients Sample (HIRA‐NPS) between 2011 and 2014. The study subjects were patients who were treated with infliximab, adalimumab or etanercept. We compared the drug costs before and after the introduction of infliximab biosimilar in December 2012 (2011–2012 and 2013–2014) to estimate the annual drug cost savings attributed to this and the number of patients who could additionally benefit from the biosimilar in 2013 and 2014. Results A total of 10 986 prescriptions were identified: 2620 for infliximab. The cost savings were estimated at $262 270 for 133 patients in 2013 and $395 220 for 174 patients in 2014. Among the patients who underwent a 1‐year maintenance course of infliximab therapy, the annual expenditure on infliximab was lower in 2014 than in 2011. If the cost savings were used to treat additional patients, 13.3%–38.6% more patients per year could be treated by indication. Conclusion The introduction of infliximab biosimilar reduced direct medical costs for both patients and the payer, which could then be used to increase patient access to biologic medicines. The entry of infliximab biosimilar could result in further reductions in healthcare costs.
This study introduces EHRNoteQA, a novel patient-specific question answering benchmark tailored for evaluating Large Language Models (LLMs) in clinical environments. Based on MIMIC-IV Electronic Health Record (EHR), a team of three medical professionals has curated the dataset comprising 962 unique questions, each linked to a specific patient's EHR clinical notes. What makes EHRNoteQA distinct from existing EHR-based benchmarks is as follows: Firstly, it is the first dataset to adopt a multi-choice question answering format, a design choice that effectively evaluates LLMs with reliable scores in the context of automatic evaluation, compared to other formats. Secondly, it requires an analysis of multiple clinical notes to answer a single question, reflecting the complex nature of real-world clinical decision-making where clinicians review extensive records of patient histories. Our comprehensive evaluation on various large language models showed that their scores on EHRNoteQA correlate more closely with their performance in addressing real-world medical questions evaluated by clinicians than their scores from other LLM benchmarks. This underscores the significance of EHRNoteQA in evaluating LLMs for medical applications and highlights its crucial role in facilitating the integration of LLMs into healthcare systems. The dataset will be made available to the public under PhysioNet credential access, promoting further research in this vital field.
The development of large language models tailored for handling patients' clinical notes is often hindered by the limited accessibility and usability of these notes due to strict privacy regulations. To address these challenges, we first create synthetic large-scale clinical notes using publicly available case reports extracted from biomedical literature. We then use these synthetic notes to train our specialized clinical large language model, Asclepius. While Asclepius is trained on synthetic data, we assess its potential performance in real-world applications by evaluating it using real clinical notes. We benchmark Asclepius against several other large language models, including GPT-3.5-turbo and other open-source alternatives. To further validate our approach using synthetic notes, we also compare Asclepius with its variants trained on real clinical notes. Our findings convincingly demonstrate that synthetic clinical notes can serve as viable substitutes for real ones when constructing high-performing clinical language models. This conclusion is supported by detailed evaluations conducted by both GPT-4 and medical professionals. All resources including weights, codes, and data used in the development of Asclepius are made publicly accessible for future research.
연구 목적: 본 연구는 예비유아교사가 학교현장실습 기간 중 실행하는 수업실습 과정에서 실습지도교사가 중점적으로 지도한 내용을 분석함으로써 수업능력을 향상시킬 수 있는 방안을 모색하고자 실시되었다. 연구 방법: 연구대상은 N시의 4년제 대학 유아교육학과에서 「학교현장실습」을 수강한 4학년 예비유아교사들의 실습지도교사들이다. 연구 내용: 실습지도교사가 예비유아교사들의 수업실습을 지도하면서 <교수방법>과 <수업운영> 영역에서 중점지도한 것으로 나타났다. 결론 및 제언: 예비유아교사의 수업능력 향상을 위해 대학-유아교육현장-실습지도교사와의 유기적인 상호협력이 필요하며, 예비유아교사들에게 실천적 경험을 제공할 수 있는 유아교육현장과의 다양한 연계방안을 논의하였다.
ABSTRACT In this study, we propose a method of categorizing relations between a headword and its aliases using Korean Wikipedia data. We propose orthographically similar types and orthographically different types. Orthographically similar types are divided into 5 types: Word space, Pronunciation difference, Omission, Abbreviation, and Word order change while different types are also classified into 4 types: Foreign word, Acronym, Paraphrased expression and Call name. We show examples to verify the proposed method.
Despite the remarkable progress in the development of predictive models for healthcare, applying these algorithms on a large scale has been challenging. Algorithms trained on a particular task, based on specific data formats available in a set of medical records, tend to not generalize well to other tasks or databases in which the data fields may differ. To address this challenge, we propose General Healthcare Predictive Framework (GenHPF), which is applicable to any EHR with minimal preprocessing for multiple prediction tasks. GenHPF resolves heterogeneity in medical codes and schemas by converting EHRs into a hierarchical textual representation while incorporating as many features as possible. To evaluate the efficacy of GenHPF, we conduct multi-task learning experiments with single-source and multi-source settings, on three publicly available EHR datasets with different schemas for 12 clinically meaningful prediction tasks. Our framework significantly outperforms baseline models that utilize domain knowledge in multi-source learning, improving average AUROC by 1.2%P in pooled learning and 2.6%P in transfer learning while also showing comparable results when trained on a single EHR dataset. Furthermore, we demonstrate that self-supervised pretraining using multi-source datasets is effective when combined with GenHPF, resulting in a 0.6%P AUROC improvement compared to models without pretraining. By eliminating the need for preprocessing and feature engineering, we believe that this work offers a solid framework for multi-task and multi-source learning that can be leveraged to speed up the scaling and usage of predictive algorithms in healthcare.
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