Advanced Data Analytics for Clinical Research Part II: Application to Cardiothoracic Surgery
| Autor: | Shida Jin, Erin M. Corsini, Mara B. Antonoff, Michael H. Antonoff, Trey Kell, Nicolas Zhou, Gregory Barbosa |
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| Rok vydání: | 2020 |
| Předmět: |
Pulmonary and Respiratory Medicine
Big Data Male Big data Internet of Things Health Care Sector 030204 cardiovascular system & hematology computer.software_genre Medical Order Entry Systems 03 medical and health sciences 0302 clinical medicine Clinical Protocols Medicine Data Mining Humans Monitoring Physiologic Natural Language Processing Surgeons Digital Technology Information retrieval business.industry Communication Data Science General Medicine Optical character recognition Standard query language Thoracic Surgical Procedures Data warehouse Equipment Failure Analysis 030220 oncology & carcinogenesis Data analysis Surgery Female Cardiology and Cardiovascular Medicine business computer |
| Zdroj: | Innovations (Philadelphia, Pa.). 15(2) |
| ISSN: | 1559-0879 |
| Popis: | In the first part of this series, we introduced the tools of Big Data, including Not Only Standard Query Language data warehouse, natural language processing (NLP), optical character recognition (OCR), and Internet of Things (IoT). There are nuances to the utilization of these analytics tools, which must be well understood by clinicians seeking to take advantage of these innovative research strategies. One must recognize technical challenges to NLP, such as unintended search outcomes and variability in the expression of human written texts. Other caveats include dealing written texts in image formats, which may ultimately be handled with transformation to text format by OCR, though this technology is still under development. IoT is beginning to be used in cardiac monitoring, medication adherence alerts, lifestyle monitoring, and saving traditional labs from equipment failure catastrophes. These technologies will become more prevalent in the future research landscape, and cardiothoracic surgeons should understand the advantages of these technologies to propel our research to the next level. Experience and understanding of technology are needed in building a robust NLP search result, and effective communication with the data management team is a crucial step in successful utilization of these technologies. In this second installment of the series, we provide examples of published investigations utilizing the advanced analytic tools introduced in Part I. We will explain our processes in developing the research question, barriers to achieving the research goals using traditional research methods, tools used to overcome the barriers, and the research findings. |
| Databáze: | OpenAIRE |
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