Artificial intelligence (AI) and machine learning (ML) are becoming ever more ingrained in our day-to-day lives. These algorithms are used for targeted advertising, GPS navigation systems like Google Maps, as well as by Alexa and Siri. It is no surprise that machine learning algorithms are also finding their way into the healthcare sector. This integration has resulted in a significant amount of contention, specifically surrounding the ethics of using computers in a deeply ‘human’ industry.
ML algorithms have a wide range of applications in the healthcare sector. These include, but are not limited to:
Managing personal finances refers to the process of planning and managing the financial aspect of one’s life. Most people struggle with managing their personal finances efficiently due to lack of financial knowledge and the complexities associated with it. The financial aspect includes 5 main activities which are: income generation, saving, investment, spending, and protection. Previously, one needed to have a comprehensive budget and extensive knowledge of investing and saving. This made managing personal finances a daunting task. However, the rise of AI-powered financial management applications has simplified the process.
Artificial Intelligence (AI) is being used to improve various aspects of our lives, even if we might not always be aware of it. The Spotify playlist that got recommended to you today. Siri providing you with the weather outlook for the week. Using your face to unlock your iPhone. These are all examples of how AI is making our lives easier and more productive. The success of AI in areas such as image recognition, natural language processing, personalisation, etc. has inspired us to investigate the full breadth of its applications. One area of application relates to an art form that…
Machine learning has found its place in various applications in the healthcare sector. These applications include medical imaging and diagnosis, natural language processing (NLP) as well as deep learning in clinical genomics. NLP, specifically, is expected to exhibit significant growth in the industry. This is due to the large amount of unstructured narrative text information that is generated in the healthcare sector. This plethora of data introduces significant opportunity for the development of NLP solutions.
NLP is a field of artificial intelligence that deals with the interaction between computers and humans using the natural language. The ultimate goal of NLP…
In the year 1997, it became more efficient to store information digitally, as opposed to on paper.
Additionally, the rise of sensory devices (IoT) has led to the “datafication” of whole industries. This has led to the dominance of the “big data industry”, with the main players in this field being Google and its likes.
Services that map user behaviour with data (like Facebook, Google Search and WhatsApp) have transformed the face of business operations and the means of generating value.
This has created a whole new brand of capitalism. Soshana Zuboff has dubbed this new species of power: “surveillance…
According to Allied Market Research, the global health insurance market size was valued at $1.98 trillion in 2020 and claims cost health insurers upwards of $30 billion in that same year.
Furthermore, in a recent survey conducted by the Property Casualty Insurers Association of America and FICO, insurers stated that fraud constitutes between 5 and 10% of claims costs. This means that up to $3 billion was spent by health insurers on fraudulent claims in 2020.
A data engineer works in the field of data science, which revolves around designing and building pipelines. These pipelines are used for transforming data into a format where it can be easily accessed and used by the end user. They perform different kinds of ETL processes to achieve this goal. This data can come from one or more disparate sources.
Data engineering has become quite essential because there is more data than ever before. This increase is due to more businesses looking to use data to be more innovative and more effective. Most of this data exists in various systems…
It is well known that class imbalance is a common problem that plagues many of the machine learning datasets that are used in practice. While the problem of class imbalance and the effect it has on predictive models is well understood, a practitioner’s toolkit to handle this issue is somewhat limited.
Across the set of the most common techniques for handling class imbalance, there is significant variation in the efficacy and complexity of the methods. On the simpler end of the spectrum, there are techniques such as undersampling the majority class or oversampling the minority class. …
An API is a set of programming code that enables data transmission between one software product and another. When people speak of “an API”, they sometimes generalize and actually mean “a publicly available web-based API that returns data, likely in JSON or XML”. An API is not the database or even the server; it is the code that governs the access point(s) for the server.
Private APIs: These application software interfaces are designed for improving solutions and services within an organization. In-house developers or contractors may use these APIs to integrate a company’s IT systems or applications, build new systems…
There is no question that machine learning has revolutionised our world and the way that we interact with it. Most of us interact with it every day and struggle to remember a time before we could ask Alexa to “set an alarm for 6 AM tomorrow morning”. From GPS traffic predictions and spam filtering to security surveillance and fraud detection, machine learning has made our lives faster and more convenient than ever before.
Leveraging ideas in data to generate value.