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An example of machine learning is classification software. For example, if you are trying to get a computer to accurately distinguish between pictures of cats and dogs, you would give it a large, varied data set of pictures of both dogs and cats and you would tell it which pictures contain dogs and which pictures contain cats. The hope is that, eventually, you would be able to give it a new picture and it would accurately be able to tell whether it is a picture of a cat or a dog by associating certain features with each group. For example, the algorithm might identify that cats have a specific ear shape or that dogs paws are of a specific colour. However, this is also the reason why we must provide the computer with a varied set. If we only had pictures of black dogs or only ginger cats in the data set, the computer would classify a black cat as a dog. When distinguishing between cats and dogs this doesn’t seem like too big of a deal. However, if we were using this algorithm to recognise people, and we don’t have enough variety in the data set i.e not enough females or people of colour, then the data set would be biased towards these groups of people, and this could have fatal consequences.

Now that I have explained what machine learning is and how it is different from classical programming, I will explain the different types of machine learning and how they work. First, there is supervised learning. The aim of supervised learning is to predict the target variable given predictor variables. An example of this is a machine learning algorithm which predicts house prices (the target variable)  based on location, square footage and condition of the house (the predictor variables). In this instance, the machine learning algorithm would be given a data set with many different houses and it would get told their locations, their square footage and conditions amongst other things. It would also get told the prices of these houses and based on this information it would predict the price for a new house. The two sub-categories of supervised learning are classification and regression. In classification, the target variable has categories (like dog vs cat), whilst in regression, the target variable is continuous (like house prices). 

The second type of machine learning is unsupervised learning. The difference between supervised and unsupervised learning is that supervised learning uses a labelled data set whilst unsupervised learning uses an unlabelled data set. The aim of unsupervised learning is to discover underlying patterns in the data. So, in unsupervised learning, an algorithm might be given images of cats and dogs and be told to separate the images into 2 groups. However, the algorithm will not add labels to the output. 
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The third type of machine learning is reinforcement learning. The aim of reinforcement learning is to learn a series of actions. In reinforcement learning, the machine is not given a predefined data set and thus the machine has to collect its own data. For example, if a machine learning algorithm were to distinguish between cats and dogs using reinforcement learning, it would do so using feedback. So, if it were given an image of a dog and it classified it as a cat, we would give negative feedback so that, eventually, the algorithm would be able to correctly classify the images.

There are platforms on which you can set up end to end machine learning training pipelines. For simpler use cases, you can also use out of box algorithms like IBM Watson and Google Vision AI. These require input data and output data, however, you don’t need to code the backend of the algorithm yourself. This is an easier way to ‘code’ an algorithm and then you can give it new data and see if it can classify the data. 
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This is one of the most innovative and interesting fields at the moment.

The global demographic population is ageing at a rapid pace. In the UK alone, one-fifth of the population, roughly 11.8 million citizens are more than 65 years of age or older. This ever-growing elderly population suffer from multiple chronic diseases (multimorbidity) such as cardiovascular disease, osteoporosis and dementia. Although there has been an increase in life expectancy, the vast majority tend to live in ill health over the course of their lives. As a result, there is increased need for hospital visits and hospitalisation, placing a huge burden on the healthcare system.
 
Polypharmacy, the use of multiple drugs or more than medically appropriate, is a burgeoning concern amongst older patients with multimorbidity. The research undertaken by the National Institutes of Health has shown that polypharmacy has become increasingly and alarmingly common in older adults with the highest number of drugs taken by those residing in nursing homes. Nearly 50% of the elderly population take one or more medications that are not medically necessary. The increased use of polypharmacy by doctors and over-prescription of unnecessary medications leads to drug to drug interactions contributing to the increased risk of falls in the elderly population, delirium and other related healthcare complications. Current evidence in medical literature clearly establishes a strong link between polypharmacy and detrimental clinical consequences in later life. Due to this, hospitals see an increased number of hospital admissions and re-admissions. This increased healthcare demand places undue strain on NHS healthcare workforce and infrastructure leading to a supply and demand mismatch.
 
The advancements in digital health technologies such as Telemedicine and Artificial Intelligence (AI) has contributed to the use remote-monitoring devices in elderly patients. AI technologies and interconnected personal devices has made it possible to audit, analyse and assimilate extensive medical data throughout the elderly population. A research conducted by Professor Arnold Milstein at Stanford University using thermal imaging cameras and AI algorithms has identified patients at risk of falls and injuries in community thereby preventing these by district nurses visiting their homes before an event. The use of thermal imaging and other medical technologies has proven to show the reduction in hospital admissions due to prophylactic interventions beforehand and early treatment of infections such as urinary tract infections. This has also assisted remote monitoring of ageing and vulnerable patients, and has delivered highly targeted and direct diagnostics, healthcare and treatment. The use of technology in healthcare and AI has opened up access to personalised and precision medicine.
 
The ongoing Covid-19 pandemic has pushed digital technology into the forefront of medicine through virtual clinics and telemonitoring of patients who are unable to visit hospital due to self-isolation and distancing measures. Broader use of this technology in the daily lives of elderly patients will help to identify those patients in need of help before they become unwell and needing hospital care. The use of Artificial Intelligence and remote monitoring of patients using advanced digital health technology will undeniably revolutionize healthcare delivery in the future by taking hospital care to the doorstep of communities.