Computer science

Here are some of the significant computer science domains that comprise healthcare analytics:

• Artificial intelligence: At the center of healthcare analytics is artificial intelligence or the study of systems that interact with their environment. Machine learning is a subarea within artificial intelligence, in which predictions are made about future events using information from previous events. The models that we will study in the later parts of this book are machine learning models.
• Databases and information management: Healthcare data is often accessed using relational databases, which can often be dumped by electronic medical record (EMR) systems on demand, or which are located in the cloud. SQL (short for Structured Query Language) can be used to select the specific data in which we are interested and to make transformations on that data.
• Programming languages: A programming language provides an interface between the human programmer and the ones and zeros inside of a computer. A programming language allows a programmer to provide instructions to the computer to make calculations on data that humans cannot practically do. In this book, we will use Python, a popular and emerging programming language that is open source, comprehensive, and features plenty of machine learning libraries.
• Software engineering: Many of you are presumably learning about healthcare analytics because you are interested in deploying production-grade healthcare applications in your workplace. Software engineering is the study of the effective and efficient building of software systems that satisfy user and customer requirements.
• Human-computer interaction: The end users of healthcare analytics applications usually don't use programming to obtain their results, but instead rely on visual interfaces. Human-computer interaction is the study of how humans interact with computers and how such interfaces can be designed. A current hot topic in medicine is how EMR applications can be made more intuitive and palatable to physicians, rather than increasing the number of mouse clicks they must make per patient while writing notes.

Computer science is so pervasive in healthcare analytics that almost every chapter in this book deals with it.