Decision Making with Machine Learning in Our Modern, Data-Rich Health-Care Industry
| Autor: | Lisa Pinheiro, Jimmy Royer, Nick Dadson |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Engineering Artificial neural network Decision engineering business.industry Management science Machine learning computer.software_genre Convolutional neural network Automation R-CAST Complement (complexity) 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Health care Business decision mapping 030212 general & internal medicine Artificial intelligence business computer |
| Zdroj: | Decision Making in a World of Comparative Effectiveness Research ISBN: 9789811032615 |
| DOI: | 10.1007/978-981-10-3262-2_21 |
| Popis: | Recent innovation in the health-care industry has given us an abundance of data with which we can compare the efficacy of alternative treatments, drugs, and other health interventions. Machine learning has proven to be particularly adept at finding intricate relationships within large datasets. In this chapter we emphasize the potential for machine learning to help us digest and use health-care data effectively. We first provide an introduction to machine learning algorithms, particularly neural network and ensemble algorithms. We then discuss machine learning applications in three areas of the health-care industry. Learning algorithms have been used within the lab as a method of automation to complement problem solving and decision making in the workplace. They have been used to compare the effectiveness of alternative interventions, such as drugs taken together. Given the rise in genomic data, they have been used to develop new treatments and drugs. Taken together, these trends suggest there is vast potential for the expanded application of these algorithms in health care. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|