Abstract
A fundamental problem in medicine and biology is to assign states, e.g. healthy or diseased, to cells, organs or individuals. State assignment or making a diagnosis is often a nontrivial and challenging process and, with the advent of omics technologies, the diagnostic challenge is becoming more and more serious. The challenge lies not only in the increasing number of measured properties and dynamics of the system (e.g. cell or human body) but also in the co-evolution of multiple states and overlapping properties, and degeneracy of states. We develop, from first principles, a generic rational framework for state assignment in cell biology and medicine, and demonstrate its applicability with a few simple theoretical case studies from medical diagnostics. We show how disease-related statistical information can be used to build a comprehensive model that includes the relevant dependencies between clinical and laboratory findings (signs) and diseases. In particular, we include disease-disease and sign-sign interactions and study how one can infer the probability of a disease in a patient with given signs. We perform comparative analysis with simple benchmark models to check the performances of our models. We find that including interactions can significantly change the statistical importance of the signs and diseases. This first principles approach, as we show, facilitates the early diagnosis of disease by taking interactions into accounts, and enables the construction of consensus diagnostic flow charts. Additionally, we envision that our approach will find applications in systems biology, and in particular, in characterizing the phenome via the metabolome, the proteome, the transcriptome, and the genome.
Highlights
Human body as a whole or in part may adopt various states, like a Rubik’s Cube
As Toward First Principle Medical Diagnostics we describe below, is often a nontrivial and challenging process and in many cases it is hard to “diagnose” the state of a cell or conditions of a patient
There is a huge need in the fields of cell biology, immunology, clinical sciences and pharmaceutical sciences for approaches to identify states, assigning states and characterizing co-emerging or co-existing states
Summary
Human body as a whole or in part may adopt various states, like a Rubik’s Cube. Homeostatic mechanisms, medical interventions and aging all involve evolution from certain body states to others. There are a number of clinical decision support systems and software systems that are used to assign findings (symptoms and signs) to disease conditions. The most commonly used technologies are WebMD Symptom Checker, Isabel Symptom Checker, DXplain and Internist [9] These algorithms compute the most likely disease that is associated with a given set of findings by using only a small part of the existing probabilistic data on findings and diseases. We should mention Bayesian belief networks, which provide a probabilistic framework to study sign-disease dependencies [20,21,22,23]. We construct a probabilistic model of interacting sign and disease variables which goes beyond the assumptions that mentioned in the previous paragraph; here the effects of the diseases on the symptoms can be correlated, and more than one disease can be involved in the study. This study does not involve usage of real medical data, which is by the way fundamentally incomplete at this moment for such modeling; it provides a rationale as to why certain often-neglected statistical information and medical data can be useful in diagnosis and demonstrates that investments in collecting such data will likely pay off
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