Machine Learning and Government Data Analytics

 

Machine Learning and Government Data Analytics

The term “machine learning” might invoke visions of futuristic robots or sentient machines, but in reality machine learning is much more practical – and common – than its sci-fi reputation. Machine learning refers to computer systems that can learn to perform a specific task without explicitly be programmed to do so. Through algorithms and statistical models, the systems learn to analyze and interpret data to identify patterns and predict outcomes.

In the world of government data analytics, machine learning can help government leaders get exponentially more value out of their data. From forecasting weather events to predicting surges in COVID-19 cases, machine learning enables government agencies better serve the public.

Like any business intelligence or data analytics initiative, the first step in developing machine learning solutions is clearly defining the business objectives. When working with our government customers on machine learning solutions, we dive in with a  Discovery Session and start by asking these two questions: “What would you like your data to do?” and “What can your data do?” Based on the quality and characteristics of the data, as well as the desired outcome, we can determine the best machine learning approach.

Types of Machine Learning Solutions

There are a variety of machine learning approaches, but a government agency’s objective will inevitably be achieved by one (or some combination) of the following ML tasks:

Supervised Machine Learning

In supervised machine learning, the human developer acts as the trainer and teaches the system by feeding it training data. Essentially we show the system the “answer key” with inputs (questions) and outputs (correct answers) so it can learn the patterns and eventually infer the right answers on its own.

This approach is used when we want to better understand the relationship between two or more variables so that we can more accurately predict future outcomes. Use cases may include predicting whether a patient has a specific medical diagnosis, financial forecasts, weather forecasts, or population growth prediction.

Qlarion built a solution for the Centers for Medicare and Medicaid Services (CMS) that modeled the operational costs and call volume of their contact center, delivering an accurate prediction of future savings, service levels, and resource needs.

Unsupervised Machine Learning Tasks

Unsupervised machine learning tasks are applied to a more challenging situation – when the human behind the system doesn’t know exactly what they are looking for. In this case, there are many possible relationships and associations within the data, and we need the machine learning solution to help us identify patterns.

Examples of unsupervised machine learning tasks include audience segmentation for targeting communications, anomaly/fraud detection, gene clustering, big data visualization, data compression, and recommendation systems.

Reinforcement Machine Learning

The objective of reinforcement machine learning is to develop a system that will, over time, learn to choose a set of actions that will maximize the likelihood of the desired outcome. The system learns by trial-and-error and will eventually be able to accurately predict which actions will lead to the optimal result.

Reinforcement machine learning can help organizations assess policies and programs – which addiction treatment methods are the most effective, which education programs result in higher student performance, which public safety initiatives actually decrease crime, etc.

Qlarion created a Deep Learning Framework and Statistical Model in order to produce a 7-day prediction of inventory needs and staffing levels for a clinical center within NIH.

To learn more about Qlarion’s machine learning services and solutions, contact us.