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Mechanism of Cardiac Arrhythmias

Mechanism of Cardiac Arrhythmias

Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.

Disturbances in Heart Rhythm

Disturbances in Heart Rhythm

Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...

Pulse rhythm

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...

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Engineering of Generative Artificial Intelligence and Natural Language Processing Models to Accurately Identify

Ruibin Feng¹, Kelly A Brennan¹, Zahra Azizi¹

¹Department of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).

Circulation. Arrhythmia and Electrophysiology

|December 16, 2024

Summary

Prompt engineering significantly improves large language models' (LLMs) accuracy in interpreting electronic health records (EHRs) for diagnostics. This technique enhances LLM performance without requiring specialized medical knowledge, making EHR data more accessible.

Keywords:

artificial intelligence clinical decision support electronic health records natural language processing ventricular tachycardia

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Area of Science:

Artificial Intelligence in Medicine
Natural Language Processing for Healthcare

Background:

Large language models (LLMs) excel with public data but struggle with private sources like electronic health records (EHRs).
Prompt engineering is a potential method to enhance LLM accuracy for EHR data interpretation without domain expertise.

Purpose of the Study:

To systematically test prompt engineering techniques for improving LLM accuracy in interpreting EHRs for nuanced diagnostic questions.
To evaluate if prompt engineering can enable LLMs to identify clinical endpoints from EHRs with expert-level accuracy.

Main Methods:

Designed and tested prompt engineering strategies on GPT-4-turbo using 490 EHR notes from 125 patients with heart rhythm disorders.
Compared LLM performance against rule-based NLP and BERT-based models for identifying recurrent arrhythmias.
Validated findings across GPT-3.5-turbo and Jurassic-2 LLMs.

Main Results:

Out-of-the-box GPT-4-turbo accuracy was 64.3%, increasing to 91.4% with prompt engineering (rationale, structured output, exemplars).
Prompt-engineered LLM performance significantly surpassed traditional NLP and BERT-based models (P<0.05).
Consistent accuracy improvements were observed across different LLMs.

Conclusions:

Prompt engineering enables LLMs to accurately identify clinical endpoints from EHRs, surpassing NLP methods.
LLM accuracy approximated expert performance without requiring domain-specific knowledge.
These prompt engineering strategies can be applied to other domains for non-expert automated data analysis.