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Understanding and Building a Skill Set for Machine Learning with Drug Dosing

machine learning-ml-drug dosing
machine learning-ml-drug dosing

This is an excerpt from an article recently published in the Journal of AHIMA in which AHIMA22 presenter Shannon H. Houser, Ph.D., M.P.H., RHIA, FAHIMA, and co-author R. Jeffrey Harris, RPH, MSHI, take a closer look at the role of machine learning as this technology evolves.

Machine learning (ML) is a subset of artificial intelligence (AI) that uses reinforcement learning with human-like levels of intelligence to predict outcomes and improve task performance. ML is widely applied in healthcare, including pharmacy practices. It can be used to calculate dosages in special populations that are difficult for traditional human computing.

There are, however, challenges in data management and analytics, and special skill sets are needed when working with ML in drug dosing applications.

Skills needed for machine learning

Some basic technical skills are needed when working with ML. Those skills include applied mathematics, computer science fundamentals and programming, data modeling and evaluation, algorithms used in ML, and natural language processing. Soft skills are also necessary characteristics for delivering your technical skills more effectively.

Mathematics, such as linear algebra, probability, statistics, and multivariate calculus, are fundamental skills in ML. When selecting the appropriate ML algorithm, mathematical formulas, parameters, approximate confidence levels, and statistical modeling procedures are all basic concepts to apply. Fundamental computer science, such as data structures, algorithms, space and time complexity, and different programming languages like R and Python for ML and SQL for database management, is another important skill for ML.

Data in general are fundamental constructs for ML. When building data modeling, it involves data structures and the patterns of the data. For example, the drug dosing discussed above requires an understanding of regression, classification, clustering, dimension reduction, and clinical terminology and concepts. A basic understanding of algorithms, such as decision tree, linear regression, and neural networks is also needed for ML. Soft skills in ML, such as communication, problem-solving, and critical thinking, are equally important when working with ML to convey your technical skills to non-technical people, solve ongoing problems, and make better decisions.

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