Machine-Learning Solutions for the Analysis of Single-Particle Diffusion Trajectories

Seckler, Henrik; Szwabiński, Janusz; Metzler, Ralf

doi:10.1021/acs.jpclett.3c01351

Cited by 14 publications

(6 citation statements)

References 146 publications

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“…α < 1, the system is called subdiffusive [56]. There are various processes that can generate either slope, as discussed in literature [57][58][59][60][61][62][63]. Due to the confinement, the TAMSD converges to the constant 2σ 2 for ∆ → ∞ when the full potential is already explored.…”

Section: The Tamsdmentioning

confidence: 99%

Time scales in the dynamics of political opinions and the voter model

Meyer,

Metzler

2024

New J. Phys.

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Opinions in human societies are measured by political polls on time scales ofmonths to years. Such opinion polls do not resolve the effects of individualinteractions but constitute a stochastic process. Voter models with zealots(individuals who do not change their opinions) can describe the mean-fielddynamics in systems where no consensus is reached. We show that for largepopulations, the voter model with zealots is equivalent to the noisy votermodel and it has a single characteristic time scale associated with thenumber of zealots in the population. We discuss which parameters areobservable in real data by analysing time series of approval ratings ofseveral political leaders that match the statistical behaviour of the votermodel using the technique of the time-averaged mean squared displacement.The characteristic time scale of political opinions in societies is around 12months, so it cannot be resolved by analysing election data, for which theresolution is several years. The effective population size in all fitteddata sets is much smaller than the real population size, which indicatespositive correlations of successive voter model steps. We also discussthe heterogeneity of voters as a cause of subdiffusion on long time scales,i.e., slow changes in the society.

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Section: The Tamsdmentioning

confidence: 99%

Time scales in the dynamics of political opinions and the voter model

Meyer,

Metzler

2024

New J. Phys.

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“…These studies show the potential for recommending both diseases and corresponding medications. However, it is worth noting that while the insights from this research could benefit individuals with DCCs, the majority of machine learning models still struggle to accurately estimate uncertainty and furnish well-calibrated predictions [23,24]. This deficiency can result in overly confident recommendations in scenarios involving dataset shifts or distributional changes [25,26].…”

Section: Machine Learning Tools and Algorithms For Healthcarementioning

confidence: 99%

Predicting an Optimal Medication/Prescription Regimen for Patient Discordant Chronic Comorbidities Using Multi-Output Models

Sharma,

Nguyen,

Singh

et al. 2024

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This paper focuses on addressing the complex healthcare needs of patients struggling with discordant chronic comorbidities (DCCs). Managing these patients within the current healthcare system often proves to be a challenging process, characterized by evolving treatment needs necessitating multiple medical appointments and coordination among different clinical specialists. This makes it difficult for both patients and healthcare providers to set and prioritize medications and understand potential drug interactions. The primary motivation of this research is the need to reduce medication conflict and optimize medication regimens for individuals with DCCs. To achieve this, we allowed patients to specify their health conditions and primary and major treatment concerns, for example, costs of medication, interactions with current drugs, and weight gain. Utilizing data gathered from MTurk and Qualtrics, we gained insights into healthcare providers’ strategies for making/customizing medication regimens. We constructed a dataset and subsequently deployed machine learning algorithms to predict optimal medication regimens for DCC patients with specific treatment concerns. Following the benchmarking different models, Random forest emerged as the top performer, achieving an accuracy of 0.93. This research contributes significantly to the enhancement of decision-making processes, empowers patients to take a more active role in their healthcare, and promotes more informed and productive discussions between patients and their care teams.

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“…Over two centuries after the pioneering observation of Ingenhousz, the multidisciplinary field of diffusion is bustling with scientific activity. Examples of recent research include: tracer diffusion [61][62][63][64][65]; polymer diffusion [66][67][68][69][70]; heterogeneous diffusion [71][72][73][74][75]; non-Gaussian diffusion [76][77][78][79][80][81][82][83]; random diffusivity [84][85][86][87][88][89][90][91][92]; diffusion of active particles [93][94][95][96][97][98]; diffusion and Bayesian analysis [99][100][101]; diffusion and machine learning [102][103][104][105][106]; and stochastic resetting of diffusion [107]…”

Section: Introductionmentioning

confidence: 99%

Regular and anomalous diffusion: I. Foundations

Eliazar

2024

J. Phys. A: Math. Theor.

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Diffusion is a generic term for random motions whose positions become more and more diffuse with time. Diffusion is of major importance in numerous areas of science and engineering, and the research of diffusion is vast and profound. This paper is the first in a stochastic `intro series' to the multidisciplinary field of diffusion. The paper sets off from a basic question: how to quantitatively measure diffusivity? Having answered the basic question, the paper carries on to a follow-up question regarding statistical behaviors of diffusion: what further knowledge can the diffusivity measure provide, and when can it do so? The answers to the follow-up question lead to an assortment of notions and topics including: persistence and anti-persistence; aging and anti-aging; spectral densities, white noise, and their generalizations; the Wiener-Khinchin theorem and its generalizations; and colored noises. Then, observing diffusion from a macro level, the paper culminates with: the universal emergence of power-law diffusivity; the three universal diffusion regimes -- one regular, and two anomalous; and the universal emergence of 1/f noise. The paper is entirely self-contained, and its prerequisites are undergraduate mathematics and statistics.

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Machine-Learning Solutions for the Analysis of Single-Particle Diffusion Trajectories

Cited by 14 publications

References 146 publications

Time scales in the dynamics of political opinions and the voter model

Time scales in the dynamics of political opinions and the voter model

Predicting an Optimal Medication/Prescription Regimen for Patient Discordant Chronic Comorbidities Using Multi-Output Models

Regular and anomalous diffusion: I. Foundations

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