Medical large language models (LLMs) have gained popularity recently due to their significant practical utility. However, most existing research focuses on general medicine, and there is a need for in-depth study of LLMs in specific fields like anesthesiology. To fill the gap, we introduce Hypnos, a Chinese Anesthesia model built upon existing LLMs, e.g., Llama. Hypnos' contributions have three aspects: 1) The data, such as utilizing Self-Instruct, acquired from current LLMs likely includes inaccuracies. Hypnos implements a cross-filtering strategy to improve the data quality. This strategy involves using one LLM to assess the quality of the generated data from another LLM and filtering out the data with low quality. 2) Hypnos employs a general-to-specific training strategy that starts by fine-tuning LLMs using the general medicine data and subsequently improving the fine-tuned LLMs using data specifically from Anesthesiology. The general medical data supplement the medical expertise in Anesthesiology and enhance the effectiveness of Hypnos' generation. 3) We introduce a standardized benchmark for evaluating medical LLM in Anesthesiology. Our benchmark includes both publicly available instances from the Internet and privately obtained cases from the Hospital. Hypnos outperforms other medical LLMs in anesthesiology in metrics, GPT-4, and human evaluation on the benchmark dataset.

Machine learning has been widely used in healthcare applications to approximate complex models, for clinical diagnosis, prognosis, and treatment. As deep learning has the outstanding ability to extract information from time series, its true capabilities on sparse, irregularly sampled, multivariate, and imbalanced physiological data are not yet fully explored. In this paper, we systematically examine the performance of machine learning models for the clinical prediction task based on the EHR, especially physiological time series. We choose Physionet 2019 challenge public dataset to predict Sepsis outcomes in ICU units. Ten baseline machine learning models are compared, including 3 deep learning methods and 7 non-deep learning methods, commonly used in the clinical prediction domain. Nine evaluation metrics with specific clinical implications are used to assess the performance of models. Besides, we sub-sample training dataset sizes and use learning curve fit to investigate the impact of the training dataset size on the performance of the machine learning models. We also propose the general pre-processing method for the physiology time-series data and use Dice Loss to deal with the dataset imbalanced problem. The results show that deep learning indeed outperforms non-deep learning, but with certain conditions: firstly, evaluating with some particular evaluation metrics (AUROC, AUPRC, Sensitivity, and FNR), but not others; secondly, the training dataset size is large enough (with an estimation of a magnitude of thousands).

Qiuchong Chen,1,* Yixue Zhang,1,* Mengjun Zhang,1 Ziying Li,1 Jindong Liu1,2 1Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, People's Republic of China; 2Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical University, Xuzhou, Jiangsu, People's Republic of China *These authors contributed equally to this work Correspondence: Jindong Liu, Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, 99 Huaihai Road West, Quanshan District, Xuzhou, Jiangsu, 221000, People's Republic of China, Email [email protected] Objective: There has been a worldwide increment in acute kidney injury (AKI) incidence among elderly orthopedic operative patients. The AKI prediction model provides patients' early detection a possibility at risk of AKI; most of the AKI prediction models derive, however, from the cardiothoracic operation. The purpose of this study is to predict the risk of AKI in elderly patients after orthopedic surgery based on machine learning algorithm models. Methods: We organized a retrospective study being comprised of 1000 patients with postoperative AKI undergoing orthopedic surgery from September 2016, to June, 2021. They were divided into training (80%;n 799) and test (20%;n 201) sets.We utilized nine machine learning (ML) algorithms and used intraoperative information and preoperative clinical features to acquire models to predict AKI. The performance of the model was evaluated according to the area under the receiver operating characteristic (AUC), sensitivity, specificity and accuracy. Select the optimal model and establish the nomogram to make the prediction model visualization. The concordance statistic (C-statistic) and calibration curve were used to discriminate and calibrate the nomogram respectively. Results: In predicting AKI, nine ML algorithms posted AUC of 0.656– 1.000 in the training cohort, with the randomforest standing out and AUC of 0.674– 0.821 in the test cohort, with the logistic regression model standing out.

The Augusta Webster, MD, Office of Medical Education (AWOME) has begun introducing a new component of the medical education curriculum to current medical students: instruction in Digital Health and Data Science. The curriculum is being co-developed by David Liebovitz, MD, associate vice chair for clinical informatics in the Department of Medicine and co-director of the Center for Medical Education in Data Science and Digital Health, and Mahesh Vaidyanathan, MD, MBA, assistant professor of Anesthesiology. The utilization of large data sets and machine learning is rapidly growing in healthcare. Feinberg is proud to be at the forefront of preparing our students to not only utilize this technology in care delivery and research, but also to critically evaluate its applicability and limitations. I am confident that this curriculum will be the foundation for many of our students to become leaders in the field of data science and augmented intelligence in medicine." The new curriculum component will see students meeting several core competencies and learning outcomes while learning about the health data ecosystem; the health IT regulatory environment; data science methods and research; digital health decision support; bias, ethics and health equity; and the sociotechnical context for digital health and data science. Mahesh Vaidyanathan, MD, MBA, assistant professor of Anesthesiology, is a co-leader of Feinberg's new Digital Health and Data Science curriculum component for medical students. "The tools that data science brings to clinical care enable more effective and personalized care for our patients.

I am delighted to invite you to participate in the first Big DataTLV event in Israel to focus on HealthCare.The event takes place on April 16, 2019, in the Wohl Convention Center in the heart of Innovation Nation, Israel.

James Peters, coauthor of "A Knowledge-Based Model of Audit Risk," is an assistant professor in the Department of Accounting, College of Business Administration, University of Oregon. Glenn D. Rennels coauthor of "Prose Generation from Expert Systems: An Applied Computational Linguistics Thomas Arcidiacono, the author of the review of An Artificial Intelligence Approach, " is a research affiliate in Approach to Legal Reasoning, is affiliated with the Artificial Intelligence Laboratory, the Medical Information Sciences Program, the New York Institute of Technology, Sunburst Center 203, Central Edwina L. Rissland, author of "Artificial Intelligence and Legal Reasoning: R. Peter Bonasso, author of "An Hermann Kaindl, author of "Minimaxing: A Discussion of the Field and Assessment of What AI Can Do for Theory and Practice", is a Gardner's Book," is an associate professor Battle Management--A Report of the software engineer in the position of of Computer and Information First AAAI Workshop on AI Applications "Gruppenleiter" at Siemens AG Science at the University of Massachusetts to Battle Management" is the osterreich, Program and System Engineering at Amherst and lecturer on department head of the Artificial Since 1984, he has been a lecturer law at the Harvard Law School. Operations division, 7525 Colshire research interests include planning Drive, Mclean, VA 22102. Vasant Dhar, coauthor of "A Knowledge-Based Model of Audit Risk," is Model of Audit Risk," is Peat Marwick Professor of Accounting, Kenneth D. Forbus is an assistant professor Perry Miller, coauthor of "Prose Generation of computer science at the University from Expert Systems: An Call toU-free 800-521-3044 Or mail inquiry to: University Microfilms International. Forbus's research interests Program, Yale University include qualitative reasoning, inference School of Medicine, 333 Cedar Street, engine design, analogical reasoning P.O.