Introduction to Clinical Data

Name: Introduction to Clinical Data
Rating: 4.6 (517 reviews)

4.6517 ratings at Coursera

Overview

This course introduces you to a framework for successful and ethical medical data mining. We will explore the variety of clinical data collected during the delivery of healthcare. You will learn to construct analysis-ready datasets and apply computational procedures to answer clinical questions. We will also explore issues of fairness and bias that may arise when we leverage healthcare data to make decisions about patient care.

In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

Syllabus 8

Asking and answering questions via clinical data mining 1.5 hours
Data available from Healthcare systems 2 hours
Representing time, and timing of events, for clinical data mining 1.5 hours
Creating analysis ready datasets from patient timelines 1.5 hours
Handling unstructured healthcare data: text, images, signals 2 hours
Putting the pieces together: Electronic phenotyping 1.5 hours
Ethics 55 minutes
Course Conclusion 1.5 hours

Skills you'll practise

Analysis Data Analysis Data Mining Machine Learning

Advantages and disadvantages

Advantages

Structured courses with graded quizzes, assignments and deadlines you can reset.
A shareable certificate from the university or company when you pay.
Financial aid is often approved for students in India (apply 15 days before you need it).
The actual Stanford lectures (CS229, CS224N, CS231N…): the deepest free material on AI anywhere.
Taught by Stanford University, one of the strongest names in its field.
Rated 4.6 out of 5 by 517 learners.
Self-paced: start any time.
A clear syllabus (8 parts) you can see before you start.

Disadvantages

Paid after a 7-day free trial (Coursera Plus or per course).
Some courses can be audited for free, but graded work and certificates need payment.
Graduate level: needs linear algebra, probability and programming first.

Some points apply to every course of this kind; see how we rank.

This course is paid. Here is how to take it for free

Free trials: You can start a 7-day free trial for many individual courses, Specializations, or a Coursera Plus subscription to test full course features. Cancel before the seventh day if you do not want to be charged.
Financial aid: If you cannot afford the fee for a certificate, you can apply for financial aid through the link on the course home page by filling out an application about your background and goals.

Taught by

Nigam ShahAcademic Director, AI in Healthcare Specialization; Associate Professor, Medicine (Biomedical Informatics)
Steven BagleySenior research engineer, Stanford's Department of Medicine
David MagnusThomas A. Raffin Professor, Medicine and Biomedical Ethics

Stanford University is in Tier 1: world-leading universities and India's top institutes of our institution ranking (97/100).