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AI and Semantic Data Lakes in Healthcare – ResolveDatas
AI and Semantic Data Lakes in Healthcare

AI and Semantic Data Lakes in Healthcare

Big data offers valuable insights into risk factors for health issues, such as respiratory failure. Yet, the need for a large volume of data has turned out to be an obstacle for several healthcare systems using AI for their projects.

One of the biggest issues of Big Data is managing a large amount of data. Data Lakes store loads of data. It means it lets you store your data and then use it when required. But that is what many health organizations are doing. So, it is resulting in a data swamp. A Data Swamp is a data lake where data is ultimately going to die. Without a proper mechanism to maintain it, you are only getting a pile of data that is almost unusable.

Some of it has to do with Hadoop since support for data governance and metadata has been one of the sore points. Sure, the situation might be getting better, but there are a few problems.

The first one is quite obvious. Even the best tools will not prove to be healthy if you do not use them. Thus, adding metadata to your data doesn’t really mean that everyone is going to do it.

The second is that not all metadata have been created equally. As you talk of descriptive metadata, the requirement for semantics will only be enhanced.

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So, by adding semantics on top of Data Lakes, you get Semantic Data Lakes.

Meeting Challenges with Semantic Data Lakes in Healthcare

There are some healthcare facilities that have implemented semantic data lakes for healthcare. Several healthcare organizations are facing data-related challenges. The challenge where there are thousands of patients that impact the institution at a given point is having adequate details about each patient at the fingertip of the doctor who is interacting with them.

Healthcare organizations are using a large amount of raw data about patients for better analysis for flagging individuals who are at risk. It is also used for helping clinicians identify the best treatment plans. To build advanced analytics solutions, some organizations have used Semantic Data Lakes, with the help of an array of components and technologies.

Semantic analytics is rooted in a similar concept as the most present iterations of big data analytics, the relational database. It is a schema that was created in the 1970s and since then it has been serving the world of data.

The Semantic Data Lake solution offers capabilities including-

  • Machine learning algorithms for integrating the results of all previous outcomes that considerably impact the effects and analysis of future patient objectives.
  • Predictive analytics at scale for anticipating and accounting for different patient outcomes in timeframes where treatments can be directed to affect care.
  • Ontological pipeline for rapidly integrating new data requirements and sources to a model that already exists, and validate the clinical process for targeted patient subsets.
  • Disposable data marts for rapidly provisioning project-specific environments for manipulating analytics results and data without redundancy.

With Semantic Data Lakes, healthcare organizations can enjoy unprecedented flexibility.


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How does Semantic Data Lake Work?

Data Lakes are built using graph database technology. It will soon let clinicians access clinical decision support with the help of natural language queries. This is due to their exclusive ability to synthesize disparate databases and draw conclusions from unrelated details.

The potential for enhancing patient care quality is big as the technology improves to cater to the full spectrum of untold demand for predictive analytics, detailed risk stratification, and patient safety.

However, healthcare providers can’t coordinate care in the community if they don’t have an organized method to keep the house in order. From research results and EHRs to patient demographics and financial data, big data is everywhere in healthcare organizations.

It is both time-consuming and expensive to craft separate infrastructure for every category of details. However, it keeps data scientists from deducing actionable outcomes from cross-pollinated datasets.

A lot of the problems can be solved with graph databases. Other technologies weren’t really equipped to address the long requirement spectrum. A regular relational database will help several organizations cater to their goals. However, they come with some limitations. Relational databases need a fine structure, which you have to plan out before using. Within the frame, you can do a great thing, but you need to pre-coordinate the schema prior to starting with data management and application development.

The issue with this is you will have to predict all future use-cases. If there is a change in mind or requirement, the cost is huge. So, you end up with data silos.

But, adaptability and flexibility are built into the graph database fabric. It uses cognitive computing skills for drawing connections across datasets that might largely be different in detail, size, or scope. Predicting the future is not necessary. It is possible to start with the present situation.

Semantics Data Lakes in healthcare

is structured primarily in a different way than that of relational databases. In this, every element is given a unique identifier that lets the database link different concepts and generates complicated insights just as a human brain does.

Using Semantics Data Lakes in Healthcare

It is only the beginning of this kind of technology. What’s important about semantic data graphics is that each node isn’t just a simple word but is a Unique Resource Identifier that can internationally recognize and contain data on an entire concept.

To use semantic data lake for healthcare, the user should be able to get answers to issues that include multiple events taking place at different times, in certain sequences, and certain locations.

The system should be able to incorporate temporal reasons, which arrange events related to each other. Meaningful inferences can be made if you know how the events have been arranged.

The main idea behind a semantic data lake is to leapfrog conventional healthcare analytics that is hamstrung by relational technologies.

Data scientists might be doing the heavy data lifting behind the scenes for aligning datasets for optimum impact. However, it will be medical practitioners and doctors who are going to use the insights. It is the patients who will ultimately benefit from it.

The semantics data lake system can also be used for monitoring possible drug interaction as the doctors write a new prescription. It is great if the database warns you instantly. The technology will give healthcare professionals an upper hand in the medical field. Many healthcare organizations are looking to fully implement a semantic data lake.

Cloud-Based Data Lakes

Data lakes are a great way to play on the strength of each individual system in an SDL solution, even though the range of technologies used makes it quite complicated. It can be of great help if organizations got access to such solutions in the cloud.

Most hospitals aren’t yet using cloud-based data lakes. However, with HIPAA compliance by Azure, Amazon, and Google Cloud the future is in the cloud.

It is a great solution to develop managed ontologies and taxonomies, which are domain-specific and also on revisiting the offering the following year. It is likely to work well for several organizations that are interested in Semantic Data Lakes for healthcare as it can offload as much of the know-how and workload to the cloud as possible.

How can ResolveData Help with Cloud-Based Data Lakes?

ResolveData offers enterprise-grade products and services that will help in building a cloud-based data lake and then managing, governing, and accessing it. Having a single point of contact has numerous advantages for any organization, i.e. our team of experts will provide an end-to-end solution for your business needs. ResolveData also provides multi-vendor open-source software support. With this, healthcare organizations can connect data from more sources.

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