Related Experiment Video
Updated: Sep 6, 2025
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
10.3K
A New Approach to Disease, Risk, and Boundaries Based on Emergent Probability
1Lonergan Institute at Boston College, Boston, Massachusetts, USA.
The Journal of Medicine and Philosophy
|July 2, 2022
Summary
This study proposes a new framework to understand disease and risk factors by differentiating biological system levels. It clarifies disease boundaries by examining biological dysfunction and genetic risk factors.
Area of Science:
- Philosophy of Medicine
- Systems Biology
- Biostatistics
Background:
- Theories of disease and risk factors lack clear definitions and boundaries.
- Existing medical frameworks struggle to integrate diverse levels of biological organization.
Purpose of the Study:
- To present a novel framework for understanding disease and risk factors based on emergent probability.
- To differentiate distinct levels of biological function and investigation methods.
- To redefine disease boundaries based on biological dysfunction and systemic integration.
Main Methods:
- Utilizing Bernard Lonergan's theory of emergent probability.
- Differentiating generic levels of systematic function within and between organisms.
- Applying functional, genetic, and statistical investigation methods.
Main Results:
- Disease is understood as biological or higher intra-level dysfunction.
- Risk factors, including genetic ones, are viewed as statistical inter-level conditioning.
- Disease boundaries are defined by functional categorization limits and upper-level integration.
Conclusions:
- The proposed framework offers a unified approach to understanding disease etiology and boundaries.
- It reconciles functional, genetic, and statistical perspectives in medical science.
- This model provides a clearer ontological and epistemic understanding of health and disease.
Related Concept Videos
01:18Steps in Outbreak Investigation
182
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
182
01:21Causality in Epidemiology
765
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
765
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
169
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
169
01:18Models of Health Promotion and Illness Prevention II
1.7K
The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results...
The agent-host-environment model states that disease results...
1.7K
01:08Prevalence and Incidence
772
In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
772
01:11Hazard Rate
179
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
179