NEW MUSCULOSKELETAL JOINT MODELING PARADIGMS

Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partially linear model is also considered to take into account the non-linear effect of time on the longitudinal count response. The performance of the method is investigated using some simulation studies and its achievement is compared with the usual approach via the Bayesian paradigm of Monte Carlo Markov Chain (MCMC). Also, we apply the proposed method to analyze two real data sets. The first one is the data about a longitudinal study of pregnancy and the second one is a data set obtained of a HIV study.

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Joint modeling of longitudinal change in tumor cell level and time to death of breast cancer patients: In case of Ayder comprehensive specialized Hospital Tigray, Ethiopia

Cogent Medicine ◽

10.1080/2331205x.2021.1874090 ◽

2021 ◽

Vol 8 (1) ◽

pp. 1874090

Author(s):

Bsrat Tesfay ◽

Tewodros Getinet ◽

Endeshaw Assefa Derso

Keyword(s):

Breast Cancer ◽

Tumor Cell ◽

Cancer Patients ◽

Joint Modeling ◽

Longitudinal Change ◽

Breast Cancer Patients ◽

Cell Level ◽

Time To Death ◽

Specialized Hospital

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What You Say or How You Say It? Depression Detection Through Joint Modeling of Linguistic and Acoustic Aspects of Speech

Cognitive Computation ◽

10.1007/s12559-020-09808-3 ◽

2021 ◽

Author(s):

Nujud Aloshban ◽

Anna Esposito ◽

Alessandro Vinciarelli

Keyword(s):

Short Term Memory ◽

Joint Modeling ◽

Joint Analysis ◽

Health Issues ◽

Multimodal Analysis ◽

Sequence Modeling ◽

Depression Detection ◽

Long Short Term Memory ◽

Joint Representation ◽

Better Than

AbstractDepression is one of the most common mental health issues. (It affects more than 4% of the world’s population, according to recent estimates.) This article shows that the joint analysis of linguistic and acoustic aspects of speech allows one to discriminate between depressed and nondepressed speakers with an accuracy above 80%. The approach used in the work is based on networks designed for sequence modeling (bidirectional Long-Short Term Memory networks) and multimodal analysis methodologies (late fusion, joint representation and gated multimodal units). The experiments were performed over a corpus of 59 interviews (roughly 4 hours of material) involving 29 individuals diagnosed with depression and 30 control participants. In addition to an accuracy of 80%, the results show that multimodal approaches perform better than unimodal ones owing to people’s tendency to manifest their condition through one modality only, a source of diversity across unimodal approaches. In addition, the experiments show that it is possible to measure the “confidence” of the approach and automatically identify a subset of the test data in which the performance is above a predefined threshold. It is possible to effectively detect depression by using unobtrusive and inexpensive technologies based on the automatic analysis of speech and language.

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Joint Modeling of Precipitation and Temperature Using Copula Theory for Current and Future Prediction under Climate Change Scenarios in Arid Lands (Case Study, Kerman Province, Iran)

Advances in Meteorology ◽

10.1155/2019/6848049 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

T. Mesbahzadeh ◽

M. M. Miglietta ◽

M. Mirakbari ◽

F. Soleimani Sardoo ◽

M. Abdolhoseini

Keyword(s):

Climate Change ◽

Goodness Of Fit ◽

Joint Modeling ◽

Copula Function ◽

Gaussian Copula ◽

Climate Change Scenarios ◽

Climatic Parameters ◽

Goodness Of Fit Test ◽

Copula Theory ◽

Precipitation And Temperature

Precipitation and temperature are very important climatic parameters as their changes may affect life conditions. Therefore, predicting temporal trends of precipitation and temperature is very useful for societal and urban planning. In this research, in order to study the future trends in precipitation and temperature, we have applied scenarios of the fifth assessment report of IPCC. The results suggest that both parameters will be increasing in the studied area (Iran) in future. Since there is interdependence between these two climatic parameters, the independent analysis of the two fields will generate errors in the interpretation of model simulations. Therefore, in this study, copula theory was used for joint modeling of precipitation and temperature under climate change scenarios. By the joint distribution, we can find the structure of interdependence of precipitation and temperature in current and future under climate change conditions, which can assist in the risk assessment of extreme hydrological and meteorological events. Based on the results of goodness of fit test, the Frank copula function was selected for modeling of recorded and constructed data under RCP2.6 scenario and the Gaussian copula function was used for joint modeling of the constructed data under the RCP4.5 and RCP8.5 scenarios.

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