stage models
Recently Published Documents


TOTAL DOCUMENTS

153
(FIVE YEARS 24)

H-INDEX

25
(FIVE YEARS 1)

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Md Mahmudul Hasan ◽  
Gary J. Young ◽  
Jiesheng Shi ◽  
Prathamesh Mohite ◽  
Leonard D. Young ◽  
...  

Abstract Background Buprenorphine is a widely used treatment option for patients with opioid use disorder (OUD). Premature discontinuation from this treatment has many negative health and societal consequences. Objective To develop and evaluate a machine learning based two-stage clinical decision-making framework for predicting which patients will discontinue OUD treatment within less than a year. The proposed framework performs such prediction in two stages: (i) at the time of initiating the treatment, and (ii) after two/three months following treatment initiation. Methods For this retrospective observational analysis, we utilized Massachusetts All Payer Claims Data (MA APCD) from the year 2013 to 2015. Study sample included 5190 patients who were commercially insured, initiated buprenorphine treatment between January and December 2014, and did not have any buprenorphine prescription at least one year prior to the date of treatment initiation in 2014. Treatment discontinuation was defined as at least two consecutive months without a prescription for buprenorphine. Six machine learning models (i.e., logistic regression, decision tree, random forest, extreme-gradient boosting, support vector machine, and artificial neural network) were tested using a five-fold cross validation on the input data. The first-stage models used patients’ demographic information. The second-stage models included information on medication adherence during the early phase of treatment based on the proportion of days covered (PDC) measure. Results A substantial percentage of patients (48.7%) who started on buprenorphine discontinued the treatment within one year. The area under receiving operating characteristic curve (C-statistic) for the first stage models varied within a range of 0.55 to 0.59. The inclusion of knowledge regarding patients’ adherence at the early treatment phase in terms of two-months and three-months PDC resulted in a statistically significant increase in the models’ discriminative power (p-value < 0.001) based on the C-statistic. We also constructed interpretable decision classification rules using the decision tree model. Conclusion Machine learning models can predict which patients are most at-risk of premature treatment discontinuation with reasonable discriminative power. The proposed machine learning framework can be used as a tool to help inform a clinical decision support system following further validation. This can potentially help prescribers allocate limited healthcare resources optimally among different groups of patients based on their vulnerability to treatment discontinuation and design personalized support systems for improving patients’ long-term adherence to OUD treatment.


2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Anthony Webster

Abstract Background Since Armitage and Doll's publication of “a multi-stage theory of carcinogenesis” in 1954, the multi-stage model has underpinned our conceptual understanding of cancer. In the last few years the model has been applied to other diseases, such as Amyotrophic Lateral Sclerosis (Motor Neurone Disease), providing new insight into how the disease can arise and progress. Methods The multi-stage model is simplified and generalised. The result is a simple mathematical toolkit to describe carcinogenesis or other multi-stage models of disease, with simple formulae corresponding to pictorial diagrams of the multi-stage process. Important practical issues are described regarding the fitting of data and the use of multi-stage models to interpret results. Results Relationships between established cancer models are clarified, and derivations are simplified. We provide examples and highlight pitfalls of fitting multistage models to data. It is explained how genetic markers and the multi-stage paradigm can provide new insights into mechanisms of disease. Limitations of the model are discussed in the context of recent cancer research. Conclusions A simple mathematical recipe can convert biologically-motivated models for each step in a disease’s progression, into a mathematical model. The framework provides a mathematical toolkit to study the failure of complex systems, biological or otherwise, simplifying the formulation and interpretation of multi-stage models. Key messages Multi-stage models are increasingly easy to use and understand. When combined with big data and genetic markers for stratification, they offer a new tool for epidemiological studies.


2021 ◽  
Vol 13 (16) ◽  
pp. 3241
Author(s):  
Amirhossein Hassanzadeh ◽  
Fei Zhang ◽  
Jan van van Aardt ◽  
Sean P. Murphy ◽  
Sarah J. Pethybridge

Accurate, precise, and timely estimation of crop yield is key to a grower’s ability to proactively manage crop growth and predict harvest logistics. Such yield predictions typically are based on multi-parametric models and in-situ sampling. Here we investigate the extension of a greenhouse study, to low-altitude unmanned aerial systems (UAS). Our principal objective was to investigate snap bean crop (Phaseolus vulgaris) yield using imaging spectroscopy (hyperspectral imaging) in the visible to near-infrared (VNIR; 400–1000 nm) region via UAS. We aimed to solve the problem of crop yield modelling by identifying spectral features explaining yield and evaluating the best time period for accurate yield prediction, early in time. We introduced a Python library, named Jostar, for spectral feature selection. Embedded in Jostar, we proposed a new ranking method for selected features that reaches an agreement between multiple optimization models. Moreover, we implemented a well-known denoising algorithm for the spectral data used in this study. This study benefited from two years of remotely sensed data, captured at multiple instances over the summers of 2019 and 2020, with 24 plots and 18 plots, respectively. Two harvest stage models, early and late harvest, were assessed at two different locations in upstate New York, USA. Six varieties of snap bean were quantified using two components of yield, pod weight and seed length. We used two different vegetation detection algorithms. the Red-Edge Normalized Difference Vegetation Index (RENDVI) and Spectral Angle Mapper (SAM), to subset the fields into vegetation vs. non-vegetation pixels. Partial least squares regression (PLSR) was used as the regression model. Among nine different optimization models embedded in Jostar, we selected the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), and Particle Swarm Optimization (PSO) and their resulting joint ranking. The findings show that pod weight can be explained with a high coefficient of determination (R2 = 0.78–0.93) and low root-mean-square error (RMSE = 940–1369 kg/ha) for two years of data. Seed length yield assessment resulted in higher accuracies (R2 = 0.83–0.98) and lower errors (RMSE = 4.245–6.018 mm). Among optimization models used, ACO and SA outperformed others and the SAM vegetation detection approach showed improved results when compared to the RENDVI approach when dense canopies were being examined. Wavelengths at 450, 500, 520, 650, 700, and 760 nm, were identified in almost all data sets and harvest stage models used. The period between 44–55 days after planting (DAP) the optimal time period for yield assessment. Future work should involve transferring the learned concepts to a multispectral system, for eventual operational use; further attention should also be paid to seed length as a ground truth data collection technique, since this yield indicator is far more rapid and straightforward.


2021 ◽  
Vol 13 (16) ◽  
pp. 8686
Author(s):  
Alessandro Severino ◽  
Larysa Martseniuk ◽  
Salvatore Curto ◽  
Larysa Neduzha

Nowadays, transport systems efficiency plays a key role for communities’ liveability and economy, being in addition an important factor in the economic integration of countries. The purpose of the article is to develop multi-stage models of tourist activities for optimizing the development of operating companies. For the implementation of models, the authors evolved the relevant system of organizational-functional support for the development of railway tourism. The research will enable us to take into consideration risks when planning tourist routes by railway, determine the order of construction or start of routes, and assess their profitability. This will provide to earn the expected incomes of all interested parties in tourist activities for the specified period. The authors created economic-mathematical models of the discrete optimal planning of the railway tourism operations. This takes into account conditions of risks and cooperation, and allows to determine which sets of effective routes are the most profitable ones. The results of the realization of the developed models include the task of the succession of the tourist route introduction according to the present and future infrastructure, availability of the rolling stock, etc. In this, consideration is given to obtaining maximum profit from tourism businesses for every participant during an established period.


2021 ◽  
Vol 13 (15) ◽  
pp. 2956
Author(s):  
Li Wang ◽  
Shuisen Chen ◽  
Dan Li ◽  
Chongyang Wang ◽  
Hao Jiang ◽  
...  

Remote sensing-based mapping of crop nitrogen (N) status is beneficial for precision N management over large geographic regions. Both leaf/canopy level nitrogen content and accumulation are valuable for crop nutrient diagnosis. However, previous studies mainly focused on leaf nitrogen content (LNC) estimation. The effects of growth stages on the modeling accuracy have not been widely discussed. This study aimed to estimate different paddy rice N traits—LNC, plant nitrogen content (PNC), leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA)—from unmanned aerial vehicle (UAV)-based hyperspectral images. Additionally, the effects of the growth stage were evaluated. Univariate regression models on vegetation indices (VIs), the traditional multivariate calibration method, partial least squares regression (PLSR) and modern machine learning (ML) methods, including artificial neural network (ANN), random forest (RF), and support vector machine (SVM), were evaluated both over the whole growing season and in each single growth stage (including the tillering, jointing, booting and heading growth stages). The results indicate that the correlation between the four nitrogen traits and the other three biochemical traits—leaf chlorophyll content, canopy chlorophyll content and aboveground biomass—are affected by the growth stage. Within a single growth stage, the performance of selected VIs is relatively constant. For the full-growth-stage models, the performance of the VI-based models is more diverse. For the full-growth-stage models, the transformed chlorophyll absorption in the reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI) performs best for LNC, PNC and PNA estimation, while the three band vegetation index (TBVITian) performs best for LNA estimation. There are no obvious patterns regarding which method performs the best of the PLSR, ANN, RF and SVM in either the growth-stage-specific or full-growth-stage models. For the growth-stage-specific models, a lower mean relative error (MRE) and higher R2 can be acquired at the tillering and jointing growth stages. The PLSR and ML methods yield obviously better estimation accuracy for the full-growth-stage models than the VI-based models. For the growth-stage-specific models, the performance of VI-based models seems optimal and cannot be obviously surpassed. These results suggest that building linear regression models on VIs for paddy rice nitrogen traits estimation is still a reasonable choice when only a single growth stage is involved. However, when multiple growth stages are involved or missing the phenology information, using PLSR or ML methods is a better option.


Author(s):  
Cassandra R. Homick ◽  
Lisa F. Platt

Gender and sexual identity play a significant role in the lives of developing youth. The developments of gender and sexual identities are shaped by a variety of factors including, but not limited to, biological, cognitive, and social elements. It is crucial to consider that gender and sexual minority individuals face additional complexities in the two processes of gender identity and sexual identity development. Cisgender identity development is most commonly understood with the help of early cognitive and social theories, although biological components play a part as well. Specifically, the theories of Lawrence Kohlberg, Sandra Bem, Alfred Bandura, and David Buss have made significant contributions to the understanding of cisgender identity development. Modern transgender identity development models are helpful in exploring transgender identity formation with the most popular being the Transgender Emergence Model founded by Arlene Lev. Similar to cisgender identity development, heterosexual identity development is typically understood with the help of early psychosocial theories, namely that of Erik Erikson. Sexual minority identity development is often comprehended using stage models and life-span models. Sexual minority stage models build off the work of Erik Erikson, with one of the most popular being the Cass Model of Gay and Lesbian Identity Development. Offering more flexibility than stage models and allowing for fluid sexual identity, life-span models, like the D’Augelli model, are often more popular choices for modern exploration of sexual minority identity development. As both sexual and gender identity spectrums are continuing to expand, there also comes a need for an exploration of the relationship between sexual and gender identity development, particularly among sexual minority populations.


Agriculture ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 259
Author(s):  
Peiyang Li ◽  
Jacek A. Koziel ◽  
Jeffrey J. Zimmerman ◽  
Jianqiang Zhang ◽  
Ting-Yu Cheng ◽  
...  

Proper treatment of infectious air could potentially mitigate the spread of airborne viruses such as porcine reproductive and respiratory syndrome virus (PRRSV). The objective of this research is to test the effectiveness of ultraviolet (UV) in inactivating aerosolized PRRSV, specifically, four UV lamps, UV-A (365 nm, both fluorescent and LED-based), “excimer” UV-C (222 nm), and germicidal UV-C (254 nm), were tested. The two UV-C lamps effectively irradiated fast-moving PRRSV aerosols with short treatment times (<2 s). One-stage and two-stage UV inactivation models estimated the UV doses needed for target percentage (%) reductions on PRRSV titer. UV-C (254 nm) dose needed for 3-log (99.9%) reduction was 0.521 and 0.0943 mJ/cm2, respectively, based on one-stage and two-stage models. An order of magnitude lower UV-C (222 nm) doses were needed for a 3-log reduction, i.e., 0.0882 and 0.048 mJ/cm2, based on one-stage and two-stage models, respectively. However, the cost of 222 nm excimer lamps is still economically prohibitive for scaling-up trials. The UV-A (365 nm) lamps could not reduce PRRSV titers for tested doses up to 4.11 mJ/cm2. Pilot-scale or farm-scale testing of UV-C on PRRSV aerosols simulating barn ventilation rates are recommended based on its effectiveness and reasonable costs comparable to HEPA filtration.


2021 ◽  
Author(s):  
Ruth Walker ◽  
Lesley Stewart ◽  
Mark Simmonds

Abstract Medical interventions may be more effective in some types of individuals than others and identifying characteristics that modify the effectiveness of an intervention is a cornerstone of precision or stratified medicine. The opportunity for detailed examination of treatment-covariate interactions can be an important driver for undertaking an individual participant data (IPD) meta-analysis, rather than a meta-analysis using aggregate data. A number of recent modelling approaches are available. We apply these methods to the Perinatal Antiplatelet Review of International Studies (PARIS) Collaboration IPD dataset and compare estimates between them. We discuss the practical implications of applying these methods, which may be of interest to aid meta-analysists in the use of these, often complex models. Models compared included the two-stage meta-analysis of interaction terms and one-stage models which fit multiple random effects and separate within and between trial information. Models were fitted for nine covariates and five binary outcomes and results compared. Interaction terms produced by the methods were generally consistent. We show that where data are sparse and there is low heterogeneity in the covariate distributions across trials, the meta-analysis of interactions may produce unstable estimates and have issues with convergence. In this IPD dataset, varying assumptions by using multiple random effects in one-stage models or using only within trial information made little difference to the estimates of treatment-covariate interaction. Method choice will depend on datasets characteristics and individual preference.


Author(s):  
Peiyang Li ◽  
Jacek A. Koziel ◽  
Jeffrey J. Zimmerman ◽  
Jianqiang Zhang ◽  
Ting-Yu Cheng ◽  
...  

Proper treatment of infectious air could potentially mitigate the spread of airborne viruses such as porcine reproductive and respiratory syndrome virus (PRRSV). The objective of this research is to test the effectiveness of ultraviolet (UV) in inactivating aerosolized PRRSV, specifically, four UV lamps, UV-A (365 nm, both fluorescent and LED-based), "excimer" UV-C (222 nm), and germicidal UV-C (254 nm), were tested. The two UV-C lamps effectively irradiated fast-moving PRRSV aerosols with short treatment times (&lt;2 s). One-stage and two-stage UV inactivation models estimated the UV doses needed for target percentage (%) reductions on PRRSV titer. UV-C (254 nm) dose needed for 3-log (99.9%) reduction was 0.521 and 0.0943 mJ/cm2, respectively, based on one-stage and two-stage models. An order of magnitude lower UV-C (222 nm) doses were needed for a 3-log reduction, i.e., 0.0882 and 0.048 mJ/cm2, based on one-stage and two-stage models, respectively. However, the cost of 222-nm excimer lamps is still economically prohibitive for scaling-up trials. The UV-A (365 nm) lamps could not reduce PRRSV titers for tested doses up to 4.11 mJ/cm2. Pilot-scale or farm-scale testing of UV-C on PRRSV aerosols simulating barn ventilation rates are recommended based on its effectiveness and reasonable costs comparable to HEPA filtration.


Sign in / Sign up

Export Citation Format

Share Document