The benefits of digitalisation in healthcare

The benefits of digitalisation in healthcare


Machine learning (ML) enables a computer to adapt to new circumstances and to detect and extrapolate patterns. Machine learning involves training an algorithm to perform tasks by learning from patterns in data rather than performing a task it is explicitly programmed to do. To train a machine learning program, data are typically divided into training sets (where a human indicates whether an outcome of interest is present or absent) and validation sets (where the system uses what it learns to indicate the presence or absence of outcomes of interest).

The health care sector has a particular focus on prediction, thus machine learning is of particular relevance for the detection of disease and for personalised treatment. Machine learning approaches are typically used when the number of patient traits of interest is small.

Machine learning can be divided into traditional machine learning and deep machine learning.


Traditional machine learning uses algorithms designed for features. Hence, these algorithms are “specialised” and cannot easily be re-used for different tasks.


Deep learning (DL) is a type of machine learning that uses multilayer neural networks with multiple hidden layers between the input and outlay not have been recognised put layers. These algorithms can identify relationships, and are being trained and improved by continuously adding high volumes of data, equipping them to keep improving their error rate performance expectations using traditional techniques.


A support vector machine is a type of machine learning that is used mainly to classify subjects into two groups, often used for the diagnosis or prediction of disease.


An artificial neural network is a method of mimicking the way a human brain learns through its connections between neurons by employing a computer model.

Neural networks are used when there is a need to evaluate complex relationships between inputs and outputs through a hidden layer (or layers) of calculations. The network adapts to the information that it is provided (for example, images) and, by executing a series of layered calculations, learns on its own what features can be used to determine specified outputs such as the presence or absence of a health-related condition.


Natural language processing (NLP) is a branch of AI concerned with understanding and interpreting human language. In health care, NLP is being and can be used to analyse the content of electronic medical records or as an automated agent to respond to patient questions.

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