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Medical Databases and Clinical Data Analysis
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Video
This screencast will help the student identify normal blood cells and their functions. This will include the identification of red blood cells, five types of white blood cells, and platelets.
- Subjects:
- Health Sciences and Medical Laboratory Science
- Keywords:
- Blood -- Analysis Blood cells -- Physiology
- Resource Type:
- Video
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MOOC
This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language)
- Resource Type:
- MOOC
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Others
PubMed is a free resource supporting the search and retrieval of biomedical and life sciences literature with the aim of improving health–both globally and personally. The PubMed database contains more than 32 million citations and abstracts of biomedical literature. It does not include full text journal articles; however, links to the full text are often present when available from other sources, such as the publisher's website or PubMed Central (PMC). Available to the public online since 1996, PubMed was developed and is maintained by the National Center for Biotechnology Information (NCBI), at the U.S. National Library of Medicine (NLM), located at the National Institutes of Health (NIH).
- Course related:
- RS3731 Neurological Physiotherapy II
- Subjects:
- Rehabilitation Sciences, Medical Imaging, Biology, Health Sciences, Nursing, and Medicine
- Keywords:
- Dentistry Clinical medicine Biology Nursing Medicine
- Resource Type:
- Others
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Others
TRIP is a clinical search engine designed to allow users to quickly and easily find and use high-quality research evidence to support their practice and/or care. However, the FREE access only supports Simple search and PICO search functions; the 'Pro" access is on subscription base.
- Subjects:
- Nursing, Medicine, Rehabilitation Sciences, and Health Sciences
- Keywords:
- Evidence-based medicine Databases
- Resource Type:
- Others
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e-book
I never seemed to find the perfect data-oriented Python book for my course, so I set out to write just such a book. Luckily at a faculty meeting three weeks before I was about to start my new book from scratch over the holiday break, Dr. Atul Prakash showed me the Think Python book which he had used to teach his Python course that semester. It is a well-written Computer Science text with a focus on short, direct explanations and ease of learning.The overall book structure has been changed to get to doing data analysis problems as quickly as possible and have a series of running examples and exercises about data analysis from the very beginning. Chapters 2–10 are similar to the Think Python book, but there have been major changes. Number-oriented examples and exercises have been replaced with data- oriented exercises. Topics are presented in the order needed to build increasingly sophisticated data analysis solutions. Some topics like try and except are pulled forward and presented as part of the chapter on conditionals. Functions are given very light treatment until they are needed to handle program complexity rather than introduced as an early lesson in abstraction. Nearly all user-defined functions have been removed from the example code and exercises outside of Chapter 4. The word “recursion”1 does not appear in the book at all. In chapters 1 and 11–16, all of the material is brand new, focusing on real-world uses and simple examples of Python for data analysis including regular expressions for searching and parsing, automating tasks on your computer, retrieving data across the network, scraping web pages for data, object-oriented programming, using web services, parsing XML and JSON data, creating and using databases using Structured Query Language, and visualizing data. The ultimate goal of all of these changes is a shift from a Computer Science to an Informatics focus is to only include topics into a first technology class that can be useful even if one chooses not to become a professional programmer.
- Subjects:
- Computing
- Keywords:
- Computer programming Programming languages (Electronic computers) Textbooks Python (Computer program language)
- Resource Type:
- e-book
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Others
In this page, it covers various health topics, for example, addictive behaviours, blood products, clinical trials, disability, and Ebola virus disease etc.
- Subjects:
- Health Sciences
- Keywords:
- Public health Health
- Resource Type:
- Others
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Video
Statistics, Machine Learning and Data Science can sometimes seem like very scary topics, but since each technique is really just a combination of small and simple steps, they are actually quite simple. My goal with StatQuest is to break down the major methodologies into easy to understand pieces. That said, I don't dumb down the material. Instead, I build up your understanding so that you are smarter.
- Course related:
- HTI34016 Introduction to Clinical Research
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Statistics Mathematical analysis Data mining Machine learning
- Resource Type:
- Video
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Others
SPSS means “Statistical Package for the Social Sciences” and was first launched in 1968. Since SPSS was acquired by IBM in 2009, it's officially known as IBM SPSS Statistics but most users still just refer to it as “SPSS”. SPSS is software for editing and analyzing all sorts of data. These data may come from basically any source: scientific research, a customer database, Google Analytics or even the server log files of a website.
- Keywords:
- Statistics -- Data processing Statistics SPSS (Computer file)
- Resource Type:
- Others
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MOOC
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
- Course related:
- EIE6207 Theoretical Fundamental and Engineering Approaches for Intelligent Signal and. Information Processing and COMP4434 Big Data Analytics
- Subjects:
- Computing
- Keywords:
- Artificial intelligence Machine learning
- Resource Type:
- MOOC
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Others
This database is constructed on the basis of two earlier databases developed by the Research Centre for the Humanities Computing (formerly the Humanities Computing Programme). Since the appearance of the Chinese Syllabary Pronounced according to the Dialect of Canton in 1996 and the Chinese Talking Syllabary of the Cantonese Dialect: An Electronic Repository in 1998, we have been receiving notes of gratitude from users from all over the Internet. Out of the many suggestions they made, the crucial one was the expansion of our databases from a pure syllabary structure into one which covers semantic information of the characters. In response to this suggestion of our users, in particular their concern for the semantic disambiguation of Chinese polyphonic characters, a database carrying the current title was planned. Being functionally versatile and user-friendly like its two predecessors, the current new database excels further in the following respects: This fully revised and expanded database covering the complete Big5 Chinese character set is now the most comprehensive syllabary of the Cantonese dialect on the Internet. It covers in the first place the syllabric data of four major works, namely, 1) S. L. Wong's A Chinese Syllabary Pronounced according to the Dialect of Canton, 2) Li Chomin's Lishi Zhongwen Zidian, 3) Zhou Wuji and Rao Bingcai's Guangzhou Hua Biaozunyin Zihui and 4) Richard Ho and Chu Kwok-fan's Yuehyin Zhengdu Zihui. To make up what is still missing, linguistic information of nine other major works are consulted. To take into account the linguistic reality of the Hong Kong society, vernacular pronunciation data provided by the Linguistic Society of Hong Kong are also included. Besides pronunciations, typical word-forms or vocabularies are provided for every character in this database. These word-forms are grouped with respect to the proper pronunciation(s) of the respective head characters so that users can disambiguate polyphonic characters that are phonologically ambiguous. In cases where common vocabularies are not readily available, brief remarks or explanations will be given. It supports up to seven transciption (romanization) schemes of the Cantonese dialect. Users can switch from one scheme to the other wherever necessary. When a certain head character is being featured, basic information such as pronunciation(s), homophones, vocabularies etc. are tabulated. In addition to these, further lexical information related to that particular character will also be provided for easy reference, as, for instances, internal codes (Big5 and Unicode), Cangjie input code, radical belonging , number of strokes, basic English translation, pagination of important references and hyperlinks pointing to various online resources. We would like to extend our sincere thanks to Ms. Ginny Chan, former instructor of Yale-China Chinese Language Center, CUHK, for her courtesy in demonstrating 1,900 unique Cantonese pronunciations on a volunteer basis.
- Course related:
- CBS 3407 Chinese Academic Writing in Language and Speech Science, CBS532 Description of Chinese I: Words and Sentences, CBS4901 Contrastive Analysis of Chinese and English, and CBS514 Introduction to Cantonese studies
- Subjects:
- Chinese Language
- Keywords:
- Chinese language -- Dialects Dictionaries Cantonese dialects -- Pronunciation
- Resource Type:
- Others