Clinical Data Science Specialization

Start Date: 03/22/2020

Course Type: Specialization Course

Course Link:

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About Course

Are you interested in how to use data generated by doctors, nurses, and the healthcare system to improve the care of future patients? If so, you may be a future clinical data scientist! This specialization provides learners with hands on experience in use of electronic health records and informatics tools to perform clinical data science. This series of six courses is designed to augment learner’s existing skills in statistics and programming to provide examples of specific challenges, tools, and appropriate interpretations of clinical data. By completing this specialization you will know how to: 1) understand electronic health record data types and structures, 2) deploy basic informatics methodologies on clinical data, 3) provide appropriate clinical and scientific interpretation of applied analyses, and 4) anticipate barriers in implementing informatics tools into complex clinical settings. You will demonstrate your mastery of these skills by completing practical application projects using real clinical data. This specialization is supported by our industry partnership with Google Cloud. Thanks to this support, all learners will have access to a fully hosted online data science computational environment for free! Please note that you must have access to a Google account (i.e., gmail account) to access the clinical data and computational environment.

Course Syllabus

Introduction to Clinical Data Science
Clinical Data Models and Data Quality Assessments
Identifying Patient Populations
Clinical Natural Language Processing

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Course Introduction

Launch your career in Clinical Data Science. A six-course introduction to using clinical data to improve the care of tomorrow's patients. Clinical Data Science Specialization The course aims to give you an in-depth view of the practice of clinical data science, providing you with the tools to dissect the data and make sense of the clinical picture. Most of us are experts (professors, doctors, nurses, medical students, administrators, business people, managers, engineers, statisticians, mathematicians, engineers) when it comes to interpreting and interpreting clinical data, but there are some skills and approaches that you might not know about. This course will give you an in-depth view of the clinical data science field, including an understanding of the different data scientists, bioinformatics, data visualization, bioinformatics, and computer aided medicine. The course will also give you a solid base to build upon when it comes to future course work on clinical data science. Through this course you will gain an in-depth overview of the current practice of clinical data science, including a full understanding of the major data scientists and bioinformatics teams (with special emphasis on bioinformatics and computer aided medicine teams). You will gain the skills to differentiate between different data scientists working on the same project and the project of your choice. We will suggest appropriate tasks for each data scientist, including actions necessary for each data scientist to perform the task at hand. The course will also give you a solid base to build upon when it comes to future course work on clinical data science.Clinical Data Science Background Data Science Tools Data Science Processes

Course Tag

Implementation Science Clinical Text Mining R Programming Computational Phenotyping Data Quality Assessment

Related Wiki Topic

Article Example
Clinical data management system A clinical data management system or CDMS is a tool used in clinical research to manage the data of a clinical trial. The clinical trial data gathered at the investigator site in the case report form are stored in the CDMS. To reduce the possibility of errors due to human entry, the systems employ various means to verify the data. Systems for clinical data management can be self-contained or part of the functionality of a CTMS. A CTMS with clinical data management functionality can help with the validation of clinical data as well as helps the site employ for other important activities like building patient registries and assist in patient recruitment efforts.
Clinical data management The clinical data manager plays a key role in the setup and conduct of a clinical trial. The data collected during a clinical trial forms the basis of subsequent safety and efficacy analysis which in turn drive decision making on product development in the pharmaceutical industry. The clinical data manager is involved in early discussions about data collection options and then oversees development of data collection tools based on the clinical trial protocol. Once subject enrollment begins, the data manager ensures that data is collected, validated, complete, and consistent. The clinical data manager liaises with other data providers (e.g. a central laboratory processing blood samples collected) and ensures that such data is transmitted securely and is consistent with other data collected in the clinical trial. At the completion of the clinical trial, the clinical data manager ensures that all data expected to be captured has been accounted for and that all data management activities are complete. At this stage, the data is declared final (terminology varies, but common descriptions are "Database Lock" and "Database Freeze"), and the clinical data manager transfers data for statistical analysis.
Clinical Data, Inc Once the clinical data has been harmonized into a unified subject centric data model, it can be transformed to CDISC compliant data sets. This means that the clinical operations teams at the Bio-Pharma companies can work with standardized data all through the life of the clinical trial than at the very end. Hence a reduced change of FDA rejection and faulty analysis.
Data science he initiated the modern, non-computer science, usage of the term "data science" and advocated that statistics be renamed data science and statisticians data scientists.
Clinical data management Clinical data management (CDM) is a critical phase in clinical research, which leads to generation of high-quality, reliable, and statistically sound data from clinical trials. Clinical data management assures collection, integration and availability of data at appropriate quality and cost. It also supports the conduct, management and analysis of studies across the spectrum of clinical research as defined by the National Institutes of Health (NIH). The ultimate goal of CDM is to assure that data support conclusions drawn from research. Achieving this goal protects public health and confidence in marketed therapeutics.
Clinical data repository A Clinical Data Repository (CDR) or Clinical Data Warehouse (CDW) is a real time database that consolidates data from a variety of clinical sources to present a unified view of a single patient. It is optimized to allow clinicians to retrieve data for a single patient rather than to identify a population of patients with common characteristics or to facilitate the management of a specific clinical department. Typical data types which are often found within a CDR include: clinical laboratory test results, patient demographics, pharmacy information, radiology reports and images, pathology reports, hospital admission, discharge and transfer dates, ICD-9 codes, discharge summaries, and progress notes.
Data science In 2013, the IEEE Task Force on Data Science and Advanced Analytics was launched, and the first international conference: IEEE International Conference on Data Science and Advanced Analytics was launched in 2014. In 2014, the American Statistical Association section on Statistical Learning and Data Mining renamed its journal to "Statistical Analysis and Data Mining: The ASA Data Science Journal" and in 2016 changed its section name to "Statistical Learning and Data Science". In 2015, the International Journal on Data Science and Analytics was launched by Springer to publish original work on data science and big data analytics. 2013 the first "European Conference on Data Analysis (ECDA)" was organised in Luxembourg establishing the European Association for Data Science (EuADS) in August 2015. In September 2015 the Gesellschaft für Klassifikation (GfKl) added to the name of the Society "Data Science Society" at the third ECDA conference at the University of Essex, Colchester, UK.
Clinical data management The Clinical Data Interchange Standards Consortium leads the development of global, system independent data standards which are now commonly used as the underlying data structures for clinical trial data. These describe parameters such as the name, length and format of each data field (variable) in the relational database.
Clinical data management Analysis of clinical trial data may be carried out by laboratories, image processing specialists or other third parties. The clinical data manager liaises with such data providers and agree data formats and transfer schedules. Data may be reconciled against the CRF to ensure consistency.
Data science Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.
Clinical data management system At the end of the clinical trial the data set in the CDMS is extracted and provided to statisticians for further analysis. The analysed data are compiled into clinical study report and sent to the regulatory authorities for approval.
Clinical Data Interchange Standards Consortium The CDISC Operational Data Model (ODM) is designed to facilitate the regulatory-compliant acquisition, archive and interchange of metadata and data for clinical research studies. ODM is a vendor neutral, platform independent format for interchange and archive of clinical study data. The model includes the clinical data along with its associated metadata, administrative data, reference data and audit information.
Clinical Science (journal) The journal was established in 1909 by Thomas Lewis and James Mackenzie under the title "Heart: A Journal for the Study of the Circulation". Lewis was the first editor-in-chief. In 1933, Lewis renamed the journal "Clinical Science" (; 1933–1973), his interests having broadened. It was briefly retitled "Clinical Science and Molecular Medicine" (; 1973–1978), becoming "Clinical Science" again in 1979.
Clinical Data, Inc Large amount of subject centric clinical data is generated throughout the life of a clinical trial / study. However there is no binding mandatory requirement to submit this clinical data in a standardized format, leading to divergent data submission formats to regulatory agencies such as FDA / EMA / PMDA. This lack of uniformity of submitted data, results in longer review time of NDAs and hence more operational costs and longer duration of the trials.
Clinical Data, Inc EDI and related data exchange standards such as X12, EDIFACT, ODETTE have enabled organizations around the world to conduct eCommerce for decades. The Bio-Pharma and Medical Devices industry and the regulatory agencies have agreed to finally adopt global standards to exchange clinical data. Beginning October 2016, FDA mandates that all pre-clinical (Animal studies) and clinical data submissions must conform to CDISC data standards.
Clinical Data, Inc It is possible to mine clinical data to recognize risk patterns that could cause minor or serious adverse events (SEA) in subjects. However it is critical that mined data represent all clinical data across all sites. Housing standardized clinical data in a CDW is a pre-requisite for accurate predictive analytics. This may greatly help clinicians identify subjects at risk of developing SAEs and mitigate that risk, thus improving safety among participating subjects.
Clinical Data, Inc
Clinical Data, Inc
Clinical Science (journal) Clinical Science is a peer-reviewed medical journal that covers all areas of clinical investigation, with a focus on translational science and medicine. The journal is currently published biweekly by Portland Press on behalf of the Biochemical Society.
Clinical data management Typical reports generated and used by the clinical data manager includes: