The technology landscape is full of solutions that can parse historical data, but modern businesses increasingly want to predict the future. What are the risks? What are the opportunities? Where will the market – and revenue – be five years from now? Using predictive analytics, organizations can take steps toward answering these future-focused questions.
Predictive analytics is a statistical technique that analyzes current and historical data to produce forecasting results. For instance, data analysis can determine associations between various behavior factors. From these associations, a variety of models can then be designed. Using a specific set of conditions for each model, businesses can gain valuable insights that lead to better decision-making.
How can predictive analytics be leveraged in healthcare?
The healthcare sector has a long history of keeping one eye on the horizon. Considering recent advancements in Artificial Intelligence (AI), many people have wondered how predictive analysis can benefit the medical space. With big-data analytics, AI and machine-learning models can be a key resource to improve healthcare services in high-risk areas. For example, a combination of AI and predictive analytics can help data scientists gain deeper insights into disparate factors affecting human health.
Consider just a few areas where AI can improve population healthcare management:
Today, health data comes from multiple sources – medical notes, electronic recordings from medical devices, laboratory images, physical examinations, and even smart devices that capture and track a variety of health data. Leveraging AI, these data can be continuously assessed and analyzed to provide healthcare practitioners and researchers boundless resources to improve diagnoses and treatments.
Machine learning has become a mature technology. Combined with AI, it can provide powerful, actionable insights. This advancement in technology, combined with modern data sets, can be utilized in population health management to achieve the ultimate goal – better health outcomes.
Technical Lead, Data Analytics & AI, Wipro Limited.
Kalyani is an expert in multiple analytical tools and data mining methodologies. She has also worked on various analytics projects in Manufacturing, Technology, Energy and Utilities, and Healthcare domains. Her experience includes data analysis, proposing analytical solutions, generating dynamic reports, and other data preparation components like data pre-processing, profiling, cleansing, validation, and transformation.