From Data to Insight: Using Clinical Terminologies and LLMs to Improve Causal Inference in Healthcare

Diabetes is a complex disease influenced by a variety of factors, including lifestyle, genetics, environmental factors, and social determinants of health. However, identifying the exact causal relationships that lead to diabetes in an individual can be challenging. Often, traditional methods miss hidden confounders—variables that influence both the cause and outcome—which can lead to biased conclusions about the risk factors for diabetes.

For instance, a patient might be identified as at risk for diabetes due to factors like obesity and sedentary lifestyle. However, hidden confounders like sleep quality, genetic predispositions, or even socioeconomic status may also contribute but remain unrecognized. Clinical terminologies like SNOMED-CT, ICD-10, and others, when combined with Large Language Models (LLMs), can potentially improve the accuracy of identifying causal relationships and reveal hidden confounders that would otherwise be overlooked.

How Clinical Terminologies and LLMs Can Help in Causal Relationship Analysis?

1.?Standardized, Granular Data with Clinical Terminologies: Clinical terminologies such as SNOMED-CT provide a standardized way to encode a wide range of health concepts, from symptoms and diagnoses to risk factors and lifestyle details. This granular and structured data helps create detailed patient profiles, ensuring that nuanced health information is consistently categorized across different sources. For example, a patient’s lifestyle habits, metabolic indicators, family history, and specific symptoms can all be coded using SNOMED-CT or ICD-10. This standardization helps improve the accuracy and comparability of data when trying to establish causal links to diabetes.

2.?Enhanced Data Mining with LLMs: LLMs trained on large datasets of health information can process unstructured clinical data (like doctor’s notes or patient history) and convert it into structured terminologies. This integration helps capture nuanced health data, even from free-text sources. By encoding complex health information into a standardized format, LLMs make it easier to apply statistical or machine learning techniques for identifying potential causal factors in diabetes, potentially discovering hidden relationships.

3.?Identifying Hidden Confounders: Hidden confounders are variables that impact both the predictor (e.g., lifestyle or obesity) and the outcome (e.g., diabetes) without being explicitly recognized in the analysis. For example:

1. Socioeconomic status (SES) can be a hidden confounder that influences lifestyle and healthcare access, both of which are related to diabetes risk.
2. Sleep quality might influence both stress levels and insulin resistance, indirectly affecting diabetes risk.

LLMs can help by analysing large, diverse datasets to identify patterns that might suggest hidden confounders. By using advanced NLP techniques, LLMs can look for co-occurrences of certain terms, infer relationships, and flag potential confounders for further analysis. For example, they might detect that patients with certain lifestyle behaviors also tend to have poor sleep quality, even if the sleep quality isn't directly reported.

4. Improving Causal Inference with Knowledge Graphs: By linking terminologies like SNOMED-CT and ICD-10 within a knowledge graph, which is a network of connected data points representing entities and their relationships, we can map out potential causal pathways. For example:

1. In a knowledge graph, nodes representing "high blood pressure" and "obesity" might connect to "diabetes" via multiple pathways, illustrating different causal scenarios.
2. Hidden confounders, such as stress or genetic predisposition, could emerge as intermediary nodes that connect lifestyle factors to diabetes.

LLMs can assist in building these knowledge graphs by filling gaps in data and suggesting relationships based on vast amounts of training data. They can help refine and expand the graph by identifying synonyms, related terms, and associations that are implicitly present in the medical literature but not explicitly encoded in the data.

5.?Personalized Risk Analysis with Causal Inference Models: By using causal inference models augmented with terminology-encoded data and LLM-derived insights, healthcare providers can analyse individual patient profiles to determine the likelihood of diabetes and its causes. For instance, a causal model could indicate that the main risk factors for one patient are genetic and environmental, while for another, they are lifestyle-related. LLMs can automate much of this analysis by rapidly processing large amounts of information and offering probabilistic interpretations, which healthcare providers can validate.

Advantages of proposed approach

• Reduced Bias and Human Error: Integrating terminologies with LLMs reduces the dependence on manual data entry and interpretation, decreasing the chances of missing confounders or incorrectly categorizing risk factors.
• Efficient Discovery of Hidden Patterns: LLMs can process complex relationships and may highlight hidden confounders or unusual causal pathways that human analysts might overlook.
• Scalable Analysis: This approach can be applied to large patient populations, making it useful for public health studies and identifying high-risk groups based on emerging trends.
• Personalized Healthcare: Patients can receive tailored advice based on a comprehensive understanding of the factors contributing to their specific risk profile.

Applying This to a Diabetes Use Case

1. Patient Data Collection: A patient’s clinical history, lifestyle details, and family history are recorded in structured form using SNOMED-CT or ICD-10 codes.
2. Data Enrichment with LLMs: LLMs process unstructured data sources like clinician notes, family history descriptions, and lifestyle narratives, converting them into additional structured information.
3. Causal Analysis with Terminologies and Knowledge Graphs: Using a causal inference model, the healthcare provider identifies the primary risk factors for this patient’s diabetes, with potential hidden confounders suggested by the LLMs.
4. Intervention Planning: Based on the identified causes, the healthcare team develops a personalized intervention plan, targeting specific lifestyle factors or monitoring confounders like sleep quality.

Role of UMLS in Terminology Integration for Causal Analysis

The Unified Medical Language System (UMLS) serves as a "common vocabulary" that integrates various terminologies. UMLS can act as a bridge in this workflow, helping align data from multiple terminologies and ensuring that terminology mappings remain consistent, which is essential for causal analysis. By combining UMLS with LLMs, we can ensure that insights derived from different data sources are unified and can be cross-referenced accurately.

Differences from HL7 and FHIR

• HL7 and FHIR are data exchange standards that allow different health information systems to share data efficiently. They focus more on interoperability and data transmission than on the content and structure of clinical terms.
• Terminologies (SNOMED-CT, ICD-10, LOINC) provide the standardized vocabulary for health information, while HL7/FHIR provide the technical standards to transport this information across systems.