?? Unlocking Insights in Healthcare with Knowledge Graphs and LLMs
Healthcare professionals often grapple with efficiently analyzing extensive clinical data to extract meaningful insights that can enhance patient care. Traditional methods of data analysis can struggle to capture the nuanced relationships and patterns within complex medical datasets.
However, emerging technologies like Knowledge Graphs and Neo4j offer a promising solution. These tools provide a structured representation of knowledge, organizing clinical data into interconnected nodes and edges. This approach enables a comprehensive view of patient information, facilitating the discovery of previously unseen correlations and dependencies.
?? Understanding the Power of Knowledge Graphs
Knowledge Graphs serve as a foundational element in this innovative approach. They offer a holistic perspective on patient information by structuring clinical data into entities, attributes, and relationships.
?? Harnessing the Potential of Neo4j
Neo4j, a leading graph database management system, serves as the backbone of our data analysis efforts. Its query language, Cypher, enables seamless traversal of Knowledge Graphs, empowering healthcare professionals to unlock insights with speed and precision.
?? Driving Healthcare Innovation through Data-Driven Insights
Consider a real-world example:
A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus (T2DM), one prior episode of HTG-induced pancreatitis three years prior to presentation, associated with an acute hepatitis, and obesity with a body mass index (BMI) of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, poor appetite, and vomiting. Two weeks prior to presentation, she was treated with a five-day course of amoxicillin for a respiratory tract infection. She was on metformin, glipizide, and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG. She had been on dapagliflozin for six months at the time of presentation. Physical examination on presentation was significant for dry oral mucosa; significantly, her abdominal examination was benign with no tenderness, guarding, or rigidity. Pertinent laboratory findings on admission were: serum glucose 111 mg/dl, bicarbonate 18 mmol/l, anion gap 20, creatinine 0.4 mg/dL, triglycerides 508 mg/dL, total cholesterol 122 mg/dL, glycated hemoglobin (HbA1c) 10%, and venous pH 7.27. Serum lipase was normal at 43 U/L. Serum acetone levels could not be assessed as blood samples kept hemolyzing due to significant lipemia. The patient was initially admitted for starvation ketosis, as she reported poor oral intake for three days prior to admission. However, serum chemistry obtained six hours after presentation revealed her glucose was 186 mg/dL, the anion gap was still elevated at 21, serum bicarbonate was 16 mmol/L, triglyceride level peaked at 2050 mg/dL, and lipase was 52 U/L. The β-hydroxybutyrate level was obtained and found to be elevated at 5.29 mmol/L - the original sample was centrifuged and the chylomicron layer removed prior to analysis due to interference from turbidity caused by lipemia again. The patient was treated with an insulin drip for euDKA and HTG with a reduction in the anion gap to 13 and triglycerides to 1400 mg/dL, within 24 hours. Her euDKA was thought to be precipitated by her respiratory tract infection in the setting of SGLT2 inhibitor use. The patient was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day. It was determined that all SGLT2 inhibitors should be discontinued indefinitely. She had close follow-up with endocrinology post discharge..
?? Unlock the Potential of Your Data
Imagine harnessing the power of Language Model (LLM) technology to transform raw clinical text into a structured Knowledge Graph, rich with interconnected nodes representing medical entities and relationships. With this innovative approach, healthcare professionals can unlock valuable insights hidden within vast amounts of unstructured data.
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Using LLM, we can efficiently analyze clinical narratives and extract pertinent information such as patient demographics, medical history, symptoms, treatments, and outcomes. By structuring this data into a Knowledge Graph, we create a visual representation of complex medical information, enabling comprehensive analysis and discovery of meaningful patterns.
Moreover, LLM empowers us to generate sophisticated Cypher queries tailored to our specific research questions and data exploration needs. Whether we're investigating disease trends, treatment efficacy, or patient outcomes, LLM-guided queries allow us to navigate the Knowledge Graph with precision and efficiency.
By leveraging LLM technology to build and query Knowledge Graphs, we can revolutionize data-driven healthcare innovation. Join us on this transformative journey as we harness the power of Language Models to unlock the full potential of clinical data, driving actionable insights and improving patient care.
2. What was the diagnosis upon admission?
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