Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification

Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review

© 2020 Elsevier Ltd. All rights reserved.Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.Peer reviewe

Predictive Learning from Real-World Medical Data: Overcoming Quality Challenges

Randomized controlled trials (RCTs) are pivotal in medical research, notably as the gold standard, but face challenges, especially with specific groups like pregnant women and newborns. Real-world data (RWD), from sources like electronic medical records and insurance claims, complements RCTs in areas like disease risk prediction and diagnosis. However, RWD's retrospective nature leads to issues such as missing values and data imbalance, requiring intensive data preprocessing. To enhance RWD's quality for predictive modeling, this thesis introduces a suite of algorithms developed to automatically resolve RWD's low-quality issues for predictive modeling. In this study, the AMI-Net method is first introduced, innovatively treating samples as bags with various feature-value pairs and unifying them in an embedding space using a multi-instance neural network. It excels in handling incomplete datasets, a frequent issue in real-world scenarios, and shows resilience to noise and class imbalances. AMI-Net's capability to discern informative instances minimizes the effects of low-quality data. The enhanced version, AMI-Net+, improves instance selection, boosting performance and generalization. However, AMI-Net series initially only processes binary input features, a constraint overcome by AMI-Net3, which supports binary, nominal, ordinal, and continuous features. Despite advancements, challenges like missing values, data inconsistencies, and labeling errors persist in real-world data. The AMI-Net series also shows promise for regression and multi-task learning, potentially mitigating low-quality data issues. Tested on various hospital datasets, these methods prove effective, though risks of overfitting and bias remain, necessitating further research. Overall, while promising for clinical studies and other applications, ensuring data quality and reliability is crucial for these methods' success

PROGRAM and PROCEEDINGS THE NEBRASKA ACADEMY OF SCIENCES: 139th Anniversary Year, One Hundred-Twenty-Ninth Annual Meeting, April 12, 2019, NEBRASKA WESLEYAN UNIVERSITY, LINCOLN, NEBRASKA

PROGRAM AT-A-GLANCE FRIDAY, APRIL 12, 2019 7:30 a.m. REGISTRATION OPENS - Lobby of Lecture Wing, Olin Hall 8:00 Aeronautics and Space Science, Session A – Acklie 109 Aeronautics and Space Science, Session B – Acklie 111 Collegiate Academy; Biology, Session B - Olin B Biological and Medical Sciences, Session A - Olin 112 Biological and Medical Sciences, Session B - Smith Callen Conference Center Chemistry and Physics; Chemistry - Olin A 8:00 “Teaching and Learning the Dynamics of Cellular Respiration Using Interactive Computer Simulations” Workshop – Olin 110 9:30 “Life After College: Building Your Resume for the Future” Workshop – Acklie 218 8:25 Collegiate Academy; Chemistry and Physics, Session A – Acklie 007 8:36 Collegiate Academy; Biology, Session A - Olin 111 9:00 Chemistry and Physics; Physics – Acklie 320 9:10 Aeronautics and Space Science, Poster Session – Acklie 109 & 111 10:30 Aeronautics and Space Science, Poster Session – Acklie 109 & 111 11:00 MAIBEN MEMORIAL LECTURE: Dr David Swanson - OLIN B Scholarship and Friend of Science Award announcements 12:00 p.m. LUNCH – WESLEYAN CAFETERIA Round-Table Discussion – “Assessing the Academy: Current Issues and Avenues for Growth” led by Todd Young – Sunflower Room 12:50 Anthropology – Acklie 109 1:00 Applied Science and Technology - Olin 111 Biological and Medical Sciences, Session C - Olin 112 Biological and Medical Sciences, Session D - Smith Callen Conference Center Chemistry and Physics; Chemistry - Olin A Collegiate Academy; Biology, Session B - Olin B Earth Science – Acklie 007 Environmental Sciences – Acklie 111 Teaching of Science and Math – Acklie 218 1:20 Chemistry and Physics; Physics – Acklie 320 4:30 BUSINESS MEETING - OLIN B NEBRASKA ASSOCIATION OF TEACHERS OF SCIENCE (NATS) The 2019 Fall Conference of the Nebraska Association of Teachers of Science (NATS) will be held at the Younes Conference Center, Kearney, NE, September 19-21, 2019. President: Betsy Barent, Norris Public Schools, Firth, NE President-Elect: Anya Covarrubias, Grand Island Public Schools, Grand Island, NE AFFILIATED SOCIETIES OF THE NEBRASKA ACADEMY OF SCIENCES, INC. 1. American Association of Physics Teachers, Nebraska Section Web site: http://www.aapt.org/sections/officers.cfm?section=Nebraska 2. Friends of Loren Eiseley Web site: http://www.eiseley.org/ 3. Lincoln Gem & Mineral Club Web site: http://www.lincolngemmineralclub.org/ 4. Nebraska Chapter, National Council for Geographic Education 5. Nebraska Geological Society Web site: http://www.nebraskageologicalsociety.org Sponsors of a $50 award to the outstanding student paper presented at the Nebraska Academy of Sciences Annual Meeting, Earth Science /Nebraska Chapter, Nat\u27l Council Sections 6. Nebraska Graduate Women in Science 7. Nebraska Junior Academy of Sciences Web site: http://www.nebraskajunioracademyofsciences.org/ 8. Nebraska Ornithologists’ Union Web site: http://www.noubirds.org/ 9. Nebraska Psychological Association http://www.nebpsych.org/ 10. Nebraska-Southeast South Dakota Section Mathematical Association of America Web site: http://sections.maa.org/nesesd/ 11. Nebraska Space Grant Consortium Web site: http://www.ne.spacegrant.org

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe