Uncovering hidden disease patterns by simulating clinical diagnostic processes

Ramezanpour, Abolfazl; Mashaghi, Alireza

doi:10.1038/s41598-018-20826-y

Cited by 4 publications

(13 citation statements)

References 23 publications

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“…Here the goal could be to reach the right diagnosis with a minimum number of the medical tests [23,24]. Thus, for the objective function we propose an increasing function of the polarizations (deviations from the neutral value) in the posterior disease probabilities.…”

Section: Diagnosis By Simulation Of the Diagnostic Processmentioning

confidence: 99%

“…Then, the (Monte Carlo) optimization algorithm of Ref. [24] is used to maximize an objective functional of T observations,…”

Section: Diagnosis By Simulation Of the Diagnostic Processmentioning

confidence: 99%

“…, j T } is the sequence of observations. One can also add the cost or relevance of the observed signs to this objective function [24]. More advanced and accurate algorithms can of course improve the quality of inference, but at the expense of more computational time [23].…”

Section: Diagnosis By Simulation Of the Diagnostic Processmentioning

confidence: 99%

“…In Refs. [23,24] we proposed probabilistic models of signs and diseases which can systematically take into account the effects of different types of sign-disease, disease-disease, and sign-sign interactions; the models are indeed graphical models of the sign and disease variables with a number of interaction factors, each one connecting a small subset of disease variables to the associated sign variables [22]. We introduced approximate inference and optimization algorithms to deal with such probabilistic models, and studied the effects of the model structure and the objective function on the performances of the diagnostic algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, this objective function is easier to compute than a maximum-likelihood function that is typically taken in these problems. Starting from an initial set of observed signs, we use an approximate (Monte Carlo) optimization algorithm to find a sequence of candidate observations that maximizes the above objective function [24]. However, instead of the true value of the "observed" sign at each step, we assume the outcome is given by the most probable value of the sign obtained from the model by the approximate inference algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Statistical physics of medical diagnostics: Study of a probabilistic model

Mashaghi

Ramezanpour

2018

Phys. Rev. E

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We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases. A Monte Carlo optimization algorithm is then used to maximize an objective function of the sequence of observations, which favors the more decisive observations resulting in more polarized disease probabilities. We see how the observed signs change the nature of the macroscopic (Gibbs) states of the sign and disease probability distributions. The structure of these macroscopic states in the configuration space of the variables affects the quality of any approximate inference algorithm (so the diagnostic performance) which tries to estimate the sign-disease marginal probabilities. In particular, we find that the simulation (or extrapolation) of the diagnostic process is helpful when the disease landscape is not trivial and the system undergoes a phase transition to an ordered phase.

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Section: Diagnosis By Simulation Of the Diagnostic Processmentioning

confidence: 99%

“…Then, the (Monte Carlo) optimization algorithm of Ref. [24] is used to maximize an objective functional of T observations,…”

Section: Diagnosis By Simulation Of the Diagnostic Processmentioning

confidence: 99%

Section: Diagnosis By Simulation Of the Diagnostic Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical physics of medical diagnostics: Study of a probabilistic model

Mashaghi

Ramezanpour

2018

Phys. Rev. E

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The Development of Mobile-Based Symptom Analysis for Early Detection of Diseases Using Hyper-Tuned C-Support Vector Classification Algorithm

Costales,

Tuquero,

Nolia

et al. 2023

2023 5th International Conference on Control and Robotics (ICCR)

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Disease evolution in reaction networks: Implications for a diagnostic problem

Ramezanpour

Mashaghi

2020

PLoS Comput Biol

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We study the time evolution of symptoms (signs) with some defects in the dynamics of a reaction network as a (microscopic) model for the progress of disease phenotypes. To this end, we take a large population of reaction networks and follow the stochastic dynamics of the system to see how the development of defects affects the macroscopic states of the signs probability distribution. We start from some plausible definitions for the healthy and disease states along with a dynamical model for the emergence of diseases by a reverse simulated annealing algorithm. The healthy state is defined as a state of maximum objective function, which here is the sum of mutual information between a subset of signal variables and the subset of assigned response variables. A disease phenotype is defined with two parameters controlling the rate of mutations in reactions and the rate of accepting mutations that reduce the objective function. The model can provide the time dependence of the sign probabilities given a disease phenotype. This allows us to obtain the accuracy of diagnosis as a function of time by using a probabilistic model of signs and diseases. The trade-off between the diagnosis accuracy (increasing in time) and the objective function (decreasing in time) can be used to suggest an optimal time for medical intervention. Our model would be useful in particular for a dynamical (history-based) diagnostic problem, to estimate the likelihood of a disease hypothesis given the temporal evolution of the signs.

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Uncovering hidden disease patterns by simulating clinical diagnostic processes

Cited by 4 publications

References 23 publications

Statistical physics of medical diagnostics: Study of a probabilistic model

Statistical physics of medical diagnostics: Study of a probabilistic model

The Development of Mobile-Based Symptom Analysis for Early Detection of Diseases Using Hyper-Tuned C-Support Vector Classification Algorithm

Disease evolution in reaction networks: Implications for a diagnostic problem

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