In the complex world of healthcare, accurately classifying patient conditions is a critical and challenging task. Diagnosing diseases often requires multiple consultations, extensive testing, and collaborative discussions among multidisciplinary teams of highly qualified medical professionals. Despite the wealth of data generated—across such documents as patient records, lab results, clinical notes, and discharge summaries—the path to a definitive diagnosis can be long and uncertain.

Data scientists, however, recognize that the answer is already hidden within the data.

Machine-learning-based disease diagnosis (MLBDD) has long been considered an inexpensive and efficient way to automatically classify conditions, a stance supported by thousands of articles in academic literature and hundreds of adopted applications. These applications include the classification of imagery, such as X-rays and MRIs, often by also incorporating tabular data such as a patient’s age and gender. Neural networks, support vector machines, and logistic regression are the most commonly employed algorithms by MLBDD researchers.

But what if there were a more effective way to classify diseases? We posit that disease classification can be far more effective when it is based on a complete 360-degree view of the patient.

The clues are already there

We build on a compelling hypothesis: that the classification of a condition is already evident somewhere within the patient’s data, but it requires a 360-degree view of both structured and unstructured data. Structured data includes things like test results and coded diagnoses; unstructured data can be physicians' notes, referral letters, and similar things. The challenge also lies in connecting these disparate data points in a meaningful way, and uncovering the conditions they reflect.

This is where Quantexa Knowledge Graphs and graph neural networks (GNNs) come into play.

The power of connected data

Quantexa’s innovation lies in its ability to connect vast volumes of both structured and unstructured data, at scale. By leveraging entity resolution and contextual linking, a Quantexa Knowledge Graph builds a comprehensive, dynamic view of each patient’s healthcare journey. This connected view is not just a data integration exercise—it’s a foundation for advanced analytics and machine learning.

Through Quantexa Knowledge Graphs, healthcare organizations can:

• Resolve entities across siloed systems (e.g., matching patient records across hospitals and general practitioners)

• Link related data such as lab results, prescriptions, and clinical notes

• Create a graph-based representation of patient interactions, symptoms, and outcomes

This graph-based structure is ideal for applying graph neural networks, a class of deep learning models designed to operate on graph data.

Why graph neural networks?

Traditional machine learning models struggle with the complexity and interconnectivity of healthcare data. GNNs, on the other hand, are uniquely suited to this environment. They learn not just from individual data points, but from the relationships and context between them.

For example, in a patient’s knowledge graph:

• Nodes might represent entities like symptoms, diagnoses, medications, and lab results

• Edges represent relationships, such as “prescribed for,” “indicates,” or “co-occurs with”

• Attributes on nodes and edges provide additional context (e.g., test values, time stamps)

GNNs can traverse this graph, learning patterns that are not obvious from the data points in isolation but become clear when viewed in context. This approach is especially powerful when:

• Disease progression is non-linear, involving multiple interacting factors, or different patient pathways

• Early indicators are subtle, buried in unstructured notes or rare combinations of symptoms

• Historical labelled data is available to train the GNN algorithm

Enabling a new era of condition classification

By applying GNNs to Quantexa Knowledge Graphs, Quantexa could enable a new approach to disease classification—one that is data-driven, scalable, and context-aware.

1. Accelerated diagnosis

   With GNNs trained on connected patient data, healthcare providers can identify likely conditions earlier in the patient journey. For instance, subtle patterns in lab results and clinical notes might suggest the early onset of rheumatoid arthritis—long before it becomes clinically obvious.

2. Improved accuracy

   GNNs reduce the risk of misclassification by considering the full context of a patient’s data. This is particularly valuable in complex or overlapping conditions, where traditional systems and processes may fall short.

3. Reduced burden on clinicians

   By surfacing likely diagnoses and relevant supporting evidence, these models can augment clinical decision-making, allowing healthcare professionals to focus on patient care rather than data interpretation.

4. Better resource allocation

   Earlier and more accurate classification enables timely intervention, reducing the need for repeated consultations and unnecessary tests. This has the potential to ease pressure on healthcare systems like the NHS in the UK, while improving patient outcomes.

Real-world application: A glimpse into the future

Imagine a scenario where a patient presents with vague joint pain. Their structured data includes a history of mild inflammation and a recent prescription for NSAIDs. Their unstructured notes mention fatigue and stiffness in the morning. Individually, these data points might be inconclusive. But when connected in a knowledge graph and analyzed through a GNN, a pattern emerges—one that aligns with early-stage rheumatoid arthritis.

The system flags this possibility, prompting the clinician to order specific tests or refer the patient to a specialist sooner than they otherwise might have. The result? Faster diagnosis, earlier treatment, and better outcomes.

A smarter path to patient care

The classification of healthcare conditions is one of the most critical—and most complex—tasks in modern medicine. By harnessing the power of knowledge graphs and GNNs across both structured and unstructured data, Quantexa’s groundbreaking innovations can help healthcare providers to unlock the insights already hidden within patient data.

This is more than a technological breakthrough. It’s a potential step toward a future where conditions are detected earlier, treatments are more targeted, and healthcare systems are more efficient. For patients, clinicians, and healthcare leaders alike, the benefits are clear—and the time to act is now.

Learn more about Quantexa Knowledge Graphs or contact us to see demonstrations and examples of Quantexa Knowledge Graphs.