Название: Machine Learning for Healthcare: Handling and Managing DataАвтор: Rashmi Agrawal, Jyotir Moy Chatterjee, Abhishek Kumar
Издательство: Chapman and Hall/CRC
Год: 2021
Страниц: 223
Язык: английский
Формат: pdf (true)
Размер: 15.5 MB
Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them.
Machine Learning (ML) is currently causing quite a buzz, and is having a huge impact on healthcare. Payers, providers, and pharmaceutical companies are today seeing applicability in their spaces and are taking advantage of ML. A machine learning model is created by feeding data into a learning algorithm. The algorithm is the place where the magic happens. There are algorithms to determine a patient’s length of stay based on diagnosis, for example, and that algorithm all began when someone decided to write it and train it with true and reliable data. Over time, the model can be re-trained with newer data, increasing the model’s effectiveness. Machine learning is defined as when a computer has been taught to recognize patterns by providing it with data and an algorithm to help understand that data. We call the process of learning “training” and the output that this process produces is called a “model”. A model can be provided with new data and it can reason with this new information based on what it has previously learned.
Machine learning models determine a set of rules using vast amounts of computing power that a human brain would be incapable of processing. The more data a machine learning model is fed, the more complex the rules – and the more accurate the predictions. Whereas a statistical model is likely to have an inherent logic that can be understood by most people, the rules created by machine learning are often beyond human comprehension because our brains are incapable of digesting and analyzing enormous datasets.
Deep learning is another buzzword we often hear a lot about but it is often misunderstood. In reality, it is just a special case of machine learning algorithm through artificial neural networks. A neural network is an algorithm that was inspired by the ways a brain works and it involves many nodes (or “neurons”) that are often connected together in layers to form a network. A neural network must have at least two layers – a layer of inputs and a layer of outputs. There may be many “hidden” layers between the input layer and output layer, and these are used to extract more information by exploiting structure in the data. A network is considered “deep” if it has more than one hidden layer (see the diagram opposite which illustrates the complexity of a neural network). Neural networks are great at solving problems where the data is highly structured – like an image of a brain scan – but are also “black box” algorithms.
Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector.
The features of this book include:
A unique and complete focus on applications of machine learning in the healthcare sector.
An examination of how data analysis can be done using healthcare data and bioinformatics.
An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values.
An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors.
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