Healthcare

A graph database stores the relationship of data as connections, eliminating the disconnection between the application and the actual database.

With the inception of artificial intelligence, every sector is getting advanced. Recognizing the potential of AI, businesses are heavily investing in deploying it for better performance and operations. The integration of AI exceeds the boundary of how businesses used to operate. AI has made it comparatively easier for businesses to understand customer preferences and existing trends and draw insights from the available data. Though the technology is yet evolving, it has relieved organizations from performing redundant tasks and providing more personalized service to the customers.

Data Challenges of AI

The pre-existing biases and its data drawing capacity to manipulate an outcome is one of the heavily criticized areas of AI amongst the tech leaders. Since it is heavily data-driven, often the data accumulated from various sources doesn’t suit the datasets in which AI-models are trained. The scarce data also adds to the concerns regarding AI. Though the data team invests huge time in cleaning data to test AI-algorithms, they cannot draw the right connection between data collected from different sources. Additionally, turning data into actionable insights and the accurate relationship of data is a must. This implies that the data team must draw the dots about the relationship between different data sources for a better outcome. Henceforth, researchers and organizations are exploring the possibility of graph technology to address the data challenges in AI.

Understanding Graph Technology

Graph databases store relationships between data as connections, eliminating the disconnection between the application and the actual database. Additionally, it also secures evidence of such connections, thus strengthening the database. The queries of the customers and clients can get addressed using graph technology, compared to the traditional AI model. Companies like Amazon and eBay have successfully deployed graph technology across businesses to provide more customized and personalized information to the user. Research firm Gartner has included Graph database as the data analytics trends over the coming years. The global graph database market is expected to reach US$4.7 billion by 2027, at a CAGR of 24.3% between 2019 and 2027.

In healthcare, the graph database will play a key role in drug discovery, disease-drug interaction, and drug delivery, amongst others. Additionally, it will benefit from drawing out insights from consolidated unstructured and structured data available in real-time.

Precision Medicine

Integrated with a graph database, precision medicine will be amongst the highly lucrative domain of healthcare. It involves interactions between disease, drugs, genes, and patients. As graph database is an interconnected nodal structure, the four mentioned entities, i.e., disease, drugs, gene, and patients, represent different nodes. A disease node represents different diseases and their associated symptoms whereas a drug node signify the connection between different proteins and the target organs. A gene node draws a connection between the human body and protein, and a patient node displays the connection between their social interaction.

Through this interconnected network, it will be comparatively easier to identify the adverse effects of drugs and the interaction of drugs across the drug-gene network. Moreover, it will aid researchers in drawing insights regarding some unexplored areas of medicine.

Predicting the risk between Gene and Disease

Certain diseases are genetic proves detrimental across generations if not managed early, such as cancer. Integration of graph database for predicting the risk across gene and disease will assist in rapid diagnosis and treatment of diseases. It will also establish a connection between rare diseases, gene-related diseases, and the new diseases that can possibly be affected by the disease. Through this knowledge, researchers and scientists can then develop medicines associated with attributes and symptoms of diseases.

Repurposing of Drugs for Orphan diseases

With COVID 19, it became evident that the development of drugs is amongst the most unexplored area in medicine. Since no particular COVID vaccine is available, healthcare institutes are relying on hydroxychloroquine is used in the form of the repurposed drug to be the best possible treatment for COVID.

Due to huge demand and a lengthy procedure, pharma companies do not test one drug against multiple diseases, specifically the diseases impacting only a small fraction, such as Down’s syndrome. By drawing connections across such diseases and available drugs, a possible treatment gets formulated. Henceforth, the integration of graph database will enable rapid drug repurposing procedures and enable new treatment of multiple diseases.