Related Experiment Video
Updated: May 8, 2026
06:16
Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
Published on: August 9, 2024
Diagnostic probability function for acute coronary heart disease garnered from experts' tacit knowledge
Johann Steurer1, Ulrike Held, Olli S Miettinen
1Horten Centre for Patient-Oriented Research and Knowledge Transfer, Department of Internal Medicine, University of Zurich, Pestalozzistrasse 24, CH-8091 Zurich, Switzerland.
Journal of Clinical Epidemiology
|September 11, 2013
Summary
This study shows it is feasible to capture expert diagnostic knowledge as diagnostic probability functions (DPFs). This method can help develop expert systems for clinical decision-making in diagnosing acute coronary heart disease (ACHD).
Area of Science:
- Medical Informatics
- Clinical Decision Support
- Diagnostic Probability Modeling
Background:
- Diagnostic probability functions (DPFs) are crucial for understanding how diagnostic indicators influence disease probability but are often unavailable.
- Tacit knowledge of diagnostic experts is difficult to quantify and utilize in clinical practice.
Purpose of the Study:
- To demonstrate a method for eliciting and formalizing diagnostic experts' case-specific tacit knowledge into DPFs.
- To illustrate the potential for developing diagnostic expert systems based on this formalized knowledge.
Main Methods:
- Presented experienced emergency department physicians with hypothetical cases for diagnosing acute coronary heart disease (ACHD).
- Collected expert-specific probability estimates for each case.
- Translated median expert probabilities into a logistic DPF for ACHD.
Main Results:
- Successfully generated a DPF representing typical diagnostic probability for ACHD based on diagnostic indicators.
- Observed significant variability in expert probability estimates for individual cases.
Conclusions:
- Garnering expert tacit knowledge into DPFs is a feasible approach.
- This methodology can pave the way for developing advanced, practice-guiding diagnostic expert systems.
Related Concept Videos
01:30Acute Coronary Syndrome III: Diagnostic Studies
Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
01:19Acute Coronary Syndrome II: Pathophysiology and Clinical Manifestations
The pathophysiology of Acute Coronary Syndrome [ACD] involves several key processes:The main underlying cause of ACD is atherosclerosis, a chronic inflammatory disease characterized by the buildup of lipid-laden plaques within the coronary arteries.As the atherosclerotic plaque grows in the coronary artery, it may become unstable due to the formation of a lipid-rich core and a thin fibrous cap. Inflammatory cells within the plaque, such as macrophages, secrete enzymes that degrade the...
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
01:15Receiver Operating Characteristic Plot
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
01:30Acute Coronary Syndrome I: Introduction
Acute Coronary Syndrome (ACS) encompasses a spectrum of heart conditions caused by sudden obstruction of coronary arteries, typically resulting from the rupture of an atherosclerotic plaque and subsequent thrombus (blood clot) formation. This obstruction can lead to partial or complete blockage of blood flow, causing varying degrees of myocardial ischemia or infarction.ACS includes the following clinical entities:Unstable Angina (UA)Non-ST-Elevation Myocardial Infarction (NSTEMI)ST-Elevation...
01:26Acute Coronary Syndrome V: Nursing Management
Nursing Assessment:Nursing management of acute coronary syndrome (ACS) involves taking the patient's history, focusing on primary complaints such as chest pain, dyspnea, and excessive sweating (diaphoresis), as well as other symptoms like back or jaw pain, nausea, vomiting, palpitations, dizziness, and fatigue. The nurse also reviews the patient's history of cardiac events, risk factors such as hypertension, diabetes, smoking, family history, and current medications.In the objective assessment,...