Likelihood contrasts: a machine learning algorithm for binary classification of longitudinal data

Клен, Рику; Karhunen, Markku; Elo, Laura L.

doi:10.1038/s41598-020-57924-9

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…e associated user mining problem is transformed into a binary classification model based on node similarity vectors [14]. rough the classification decision function f(x), the node pairs in the network are divided into two types: Related users and nonrelated users, so as to realize the mining task of related users between different network platforms.…”

Section: Auma-mrl Algorithmmentioning

confidence: 99%

“…e above process is shown in Figure 5, where R A and R B respectively represent the node embedding matrices of the two networks, and N A and N B respectively represent the number of nodes of the two networks. e complete process of the AUMA-MRL algorithm is shown in (14) for a given set of networks to be fused, each node is traversed separately, and the neighborhood information of the nodes is sampled and fused to obtain the neighborhood feature z v . Neighborhood feature Z and global feature A are weighted using parameters θ 1 and θ 2 to obtain the complete node embedding R.…”

Section: Auma-mrl Algorithmmentioning

confidence: 99%

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Network Data Mining Algorithm of Associated Users Based on Multi-Information Fusion

Wang¹,

Zhang²,

Zhang³

2022

Security and Communication Networks

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To explore how related users can optimize the network mining algorithm, the author proposes a related user mining algorithm based on the fusion of user attributes and user relationships. This method recommends key technical problems and solutions based on information represented by multi-information fusion and explores research on associated user network data mining algorithms. Research has shown that the associated user network data mining algorithm based on multi-information fusion is 65% higher than previous methods. AUMA-MRL has good performance under different network overlaps. Also, since the node embedding of the AUMA-MRL algorithm is obtained by neighborhood sampling, for new nodes in the network, the algorithm can quickly obtain the new node embedding, as well as the similarity vector between the new node and the rest of the nodes in the network, therefore, the associated users of newly added nodes in the network can be quickly mined, and the robustness of the mining algorithm of associated users in the network is enhanced. Compared with the existing classical algorithms, the recall rate of the proposed algorithm is increased by 17.5% on average, which can effectively mine the associated users in the network.

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Section: Auma-mrl Algorithmmentioning

confidence: 99%

Section: Auma-mrl Algorithmmentioning

confidence: 99%

Network Data Mining Algorithm of Associated Users Based on Multi-Information Fusion

Wang¹,

Zhang²,

Zhang³

2022

Security and Communication Networks

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“…The current common performance evaluation indicators are classified into three categories. The first category is taken to evaluate the degree of approximation between the obtained solution and the global Pareto optimal frontier of the problem and to illustrate the convergence of the algorithm [37]. The second category is taken to evaluate the diversity performance index of the noninferior solution.…”

Section: Evaluation Indicators Of Decision Model Formation and Datase...mentioning

confidence: 99%

Environmental Cost Control of Manufacturing Enterprises via Machine Learning under Data Warehouse

2022

Sustainability

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Environmental cost refers to the cost paid by enterprises to reduce environmental pollution and resource depletion in production and operation. To help enterprises reduce environmental costs, a manufacturing environmental cost control algorithm based on machine learning is proposed. The probabilistic neural network is used to classify the current environmental cost control level of different manufacturing enterprises. Then, the particle swarm optimization (PSO) algorithm is improved to build a multi-objective backbone PSO algorithm for multi-objective decision-making, which is used in the selection of environmental cost control methods. The experimental results show that there is a strong correlation between the original data classification and the proposed probabilistic neural network, and the correlation reaches 96.1%. PSO performance test results show that the algorithm has the best performance, the best stability, and the shortest time needed to find the optimal solution set when the initial particle number is 140 and the number of iterations is 60. Based on the comprehensive experimental results, the following conclusions are drawn. Enterprises should strengthen collaboration and cooperation with customers, suppliers, and waste-profiting enterprises, so as to well control environmental costs. To sum up, the proposed model provides some references for the adoption of machine learning in environmental cost control of manufacturing enterprises.

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“…To address this need, we used the largest and the most geographically extended patient's dataset to date for developing and extensively validating a simple but yet clinically useful machine learning-based online model for doctors to predict mortality in COVID-19 patients at any time during hospitalization. Our experience in complex data analysis 8 and the collaboration with physicians representing 4 different health systems, enabled us to understand the real clinical needs during different pandemic scenarios and to answer these necessities by offering two predictor subtypes suited for undertriage and overtriage situations (https://gomezvarelalab.em.mpg.de/codop/). The collective effort presented here unveils the power of machine learning for helping clinicians and patients in this pandemic.…”

Section: Introductionmentioning

confidence: 99%

Development and evaluation of a machine learning-based in-hospital COvid-19 Disease Outcome Predictor (CODOP): a multicontinental retrospective study

Клен

Purohit

Gómez‐Huelgas

et al. 2021

Preprint

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Summary: Background More contagious SARS-CoV-2 virus variants, breakthrough infections, waning immunity, and differential access to COVID-19 vaccines account for the worst yet numbers of hospitalization and deaths during the COVID-19 pandemic, particularly in resource-limited countries. There is an urgent need for clinically valuable, generalizable, and parsimonious triage tools assisting the appropriate allocation of hospital resources during the pandemic. We aimed to develop and extensively validate a machine learning-based tool for accurately predicting the clinical outcome of hospitalized COVID-19 patients. Methods: CODOP was built using modified stable iterative variable selection and linear regression with lasso regularisation. To avoid generalization problems, CODOP was trained and tested with three time-sliced and geographically distinct cohorts encompassing 40 511 blood-based analyses of COVID-19 patients from more than 110 hospitals in Spain and the USA during 2020-21. We assessed the discriminative ability of the model using the Area Under the Receiving Operative Curve (AUROC) as well as horizon and Kaplan-Meier risk stratification analyses. To reckon the fluctuating pressure levels in hospitals through the pandemic, we offer two online CODOP calculators suited for undertriage or overtriage scenarios. We challenged their generalizability and clinical utility throughout an evaluation with datasets gathered in five hospitals from three Latin American countries. Findings: CODOP uses 12 clinical parameters commonly measured at hospital admission and associated with the pathophysiology of COVID-19. CODOP reaches high discriminative ability up to nine days before clinical resolution (AUROC: 0.90-0.96, 95% CI 0.879-0.970), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. The two CODOP online calculators predicted the clinical outcome of the majority of patients (73-100% sensitivity and 84-100% specificity) from the distinctive Latin American evaluation cohort. Interpretation: The high predictive performance of CODOP in geographically disperse patient cohorts and the easiness-of-use, strongly suggest its clinical utility as a global triage tool, particularly in resource-limited countries.

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Likelihood contrasts: a machine learning algorithm for binary classification of longitudinal data

Abstract: In this section, we introduce the method of likelihood contrasts (LC). We also describe other methods used in comparison.

Cited by 6 publications

References 36 publications

Network Data Mining Algorithm of Associated Users Based on Multi-Information Fusion

Network Data Mining Algorithm of Associated Users Based on Multi-Information Fusion

Environmental Cost Control of Manufacturing Enterprises via Machine Learning under Data Warehouse

Development and evaluation of a machine learning-based in-hospital COvid-19 Disease Outcome Predictor (CODOP): a multicontinental retrospective study

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