Establishment of Near-Infrared Model for Glycogen Content in Freeze-Dried Tissues of Jinjiang Oyster Crassostrea ariakensis by Yan WANG, Biao WU, Zhihong LIU, Xi CHEN, Tao YU, Zhenyuan WANG, Xiujun SUN, Liqing ZHOU, Yanxin ZHENG

“…Combined with the least-squares method, the glycogen content prediction model of six tissues and the whole soft body of C. ariakensis was established and verified. …”
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Solid Waste Image Classification Using Deep Convolutional Neural Network by Nonso Nnamoko, Joseph Barrowclough, Jack Procter

“…Another common issue associated with image classification is high computational cost which often results to high development time and prediction model size. Therefore, a lightweight model with high <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>a</mi><mi>c</mi><mi>c</mi><mi>u</mi><mi>r</mi><mi>a</mi><mi>c</mi><mi>y</mi></mrow></semantics></math></inline-formula> and a high level of methodology transparency is of particular importance in this domain. …”
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Prognosis prediction in traumatic brain injury patients using machine learning algorithms by MirMohammad Miri, Jamie Cone

“…The results indicated that while these models were able to develop predictive models successfully, there is insufficient evidence to consider ML algorithms as a dependable tool for clinical decision-making.[3] Alanazi et al., conducted a review in 2017 to assess the effectiveness of ML models in predicting patient outcomes for various disorders. …”
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Interim PET-results for prognosis in adults with Hodgkin lymphoma: a systematic review and meta-analysis of prognostic factor studies by Aldin, A, Umlauff, L, Estcourt, L, Trivella, M, Et al.

“…</p> <p><strong>Data collection and analysis</strong> We developed a data extraction form according to the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Two teams of two review authors independently screened the studies, extracted data on overall survival (OS), progression‐free survival (PFS) and PET‐associated adverse events (AEs), assessed risk of bias (per outcome) according to the Quality in Prognosis Studies (QUIPS) tool, and assessed the certainty of the evidence (GRADE). …”

Interim PET‐results for prognosis in adults with Hodgkin lymphoma: A systematic review and meta‐analysis of prognostic factor studies by Aldin, A, Umlauff, L, Estcourt, LJ, Collins, G, Moons, KGM, Engert, A, Kobe, C, von Tresckow, B, Haque, M, Foroutan, F, Kreuzberger, N, Trivella, M, Skoetz, N

“…</p><br /> <p><strong>Data Collection and Analysis:</strong><br /> We developed a data extraction form according to the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Two teams of two review authors independently screened the studies, extracted data on overall survival (OS), progression‐free survival (PFS) and PET‐associated adverse events (AEs), assessed risk of bias (per outcome) according to the Quality in Prognosis Studies (QUIPS) tool, and assessed the certainty of the evidence (GRADE). …”

Evaluation and Prediction of Land-Use Changes using the CA_Markov Model by Zeynab Karimzadeh Motlagh, Ali Lotfi, Saeid Pourmanafi, Saeedreza Ahmadizadeh

“…In this research, the CA-Markov prediction model was used to simulate and predict land-use change. …”
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高血压性脑出血患者钻孔引流手术后继发性脑梗死的危险因素分析 Risk Factors of Secondary Cerebral Infarction after Trepanation and Drainage in Patients with Hypertensive Intracerebral Hemorrhage... by 王贝, 谭波, 张钺, 刘永东, 杨佳强, 邹学利, 焦阳, 孙海敬

“…The independent risk factors for secondary cerebral infarction in HICH patients were screened by univariate analysis and multivariate logistic regression analysis, and the risk prediction model of secondary cerebral infarction was constructed. …”
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The effect of profit management and business strategies on the company's bankruptcy risk by Morteza Kiei, Mahmoud Samadi Largan

“…Profit management is defined as taking conscious steps within the scope of accepted accounting principles to bring the reported profit to the desired level (Cohen & Malkogianni, 2021). Bankruptcy prediction models are among the tools for estimating the future status of companies (Rezaei Pitenoei, 2019). …”
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Prediction of neurodevelopmental outcome in children born extremely preterm by Linsell, L

“…</p> <p><b>Methods:</b> A systematic review was conducted using MEDLINE, EMBASE and PyscINFO databases to identify studies published between January 1 1990 and June 1 2014 reporting multivariable prediction models for the neurodevelopment of children born ≤32 weeks of gestation or with a birth weight ≤1250 grams (protocol registration number CRD42014006943). …”

MODELO DE ESTUDIO DE LA ESTRUCTURA CONDICIONAL A TRA´VES DE UN ANÁLISIS MULTIVARIANTE ENFOCADO A LA DETECCIÓN DE TALENTOS EN JUGADORES DE BALONMANO by J.J. Fernández, Mª H. Vila, F. A. Rodríguez

“…Variables entering the predictive model using the first discriminant function varied for each age category group, and correct classification percentage significantly decreased at the oldest age category (JUV). …”
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Patterns of antibiotic use, pathogens, and prediction of mortality in hospitalized neonates and young infants with sepsis: A global neonatal sepsis observational cohort study (NeoO... by Neal J. Russell, Wolfgang Stöhr, Nishad Plakkal, Aislinn Cook, James A. Berkley, Bethou Adhisivam, Ramesh Agarwal, Nawshad Uddin Ahmed, Manica Balasegaram, Daynia Ballot, Adrie Bekker, Eitan Naaman Berezin, Davide Bilardi, Suppawat Boonkasidecha, Cristina G. Carvalheiro, Neema Chami, Suman Chaurasia, Sara Chiurchiu, Viviane Rinaldi Favarin Colas, Simon Cousens, Tim R. Cressey, Ana Carolina Dantas de Assis, Tran Minh Dien, Yijun Ding, Nguyen Trong Dung, Han Dong, Angela Dramowski, Madhusudhan DS, Ajay Dudeja, Jinxing Feng, Youri Glupczynski, Srishti Goel, Herman Goossens, Doan Thi Huong Hao, Mahmudul Islam Khan, Tatiana Munera Huertas, Mohammad Shahidul Islam, Daniel Jarovsky, Nathalie Khavessian, Meera Khorana, Angeliki Kontou, Tomislav Kostyanev, Premsak Laoyookhon, Sorasak Lochindarat, Mattias Larsson, Maia De Luca, Surbhi Malhotra-Kumar, Nivedita Mondal, Nitu Mundhra, Philippa Musoke, Marisa M. Mussi-Pinhata, Ruchi Nanavati, Firdose Nakwa, Sushma Nangia, Jolly Nankunda, Alessandra Nardone, Borna Nyaoke, Christina W. Obiero, Maxensia Owor, Wang Ping, Kanchana Preedisripipat, Shamim Qazi, Lifeng Qi, Tanusha Ramdin, Amy Riddell, Lorenza Romani, Praewpan Roysuwan, Robin Saggers, Emmanuel Roilides, Samir K. Saha, Kosmas Sarafidis, Valerie Tusubira, Reenu Thomas, Sithembiso Velaphi, Tuba Vilken, Xiaojiao Wang, Yajuan Wang, Yonghong Yang, Liu Zunjie, Sally Ellis, Julia A. Bielicki, A. Sarah Walker, Paul T. Heath, Mike Sharland

“…Prospective daily observational data was collected on clinical signs, supportive care, antibiotic treatment, microbiology, and 28-day mortality. Two prediction models were developed for (1) 28-day mortality from baseline variables (baseline NeoSep Severity Score); and (2) daily risk of death on IV antibiotics from daily updated assessments (NeoSep Recovery Score). …”
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PREDICTION OF DENSITY AND VISCOSITY OF COLOMBIAN CRUDE OILS FROM CHROMATOGRAPHIC DATA by Giovanni Morales-Medina, Alexander Guzmán

“…Through the inclusion of the chromatographic Distillable Fraction (DF) and some mathematical transformations, the accuracy for the predictive model of Vk was improved. Notwithstanding this, performance of the regression based on GC data was lower compared to the corresponding Nuclear Magnetic Resonance models. …”
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Attraction and Repulsion Factors of Tourism Destinations with Emphasis on Inbound French Cultural Tourists’ Experiences in Iran by AhmadReza Sheikhi, Morteza Kowsari

“…R., Nasiri, N., & Rudd, A. G. (2020). A predictive model of tourist destinations based on tourists' comments and interests using text analytics. …”
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Use of ICT to Confront COVID-19 by Yousry Saber El Gamal

“…</p><p style="text-align: justify;"><span>Artificial Intelligence:</span></p><p style="text-align: justify;">With the help of data analytics and predictive models, AI proved to be a highly effective tool against the COVID-19 Pandemic. …”
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Comparison of RW and PW models in profit forecasting for small and medium companies by Arash Elahi Shirvan, Hadi Saeidi, Ghasem Elahi Shirvan

“…Some researches also included basic data in the prediction model based on linear regression or logistic regression (Harris and Wang, 2018).   …”
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Linked Metadata for FAIR Digital Objects Carrying Computable Knowledge by Allen Flynn, Marisa Conte, Peter Boisvert, Rachel Richesson, Zach Landis-Lewis, Charles Friedman

“…An example of an early FDO linked data record appears in Example 1.{ &quot;@context&quot;: { &quot;dcterms&quot;: &quot;http://purl.org/dc/terms/&quot;, &quot;koio&quot;: &quot;http://kgrid.org/koio/&quot;, &quot;fno&quot; : &quot;https://w3id.org/function/ontology/&quot; }, &quot;@id&quot;:&quot;https://library.kgrid.org/#/object/99999%2Ffk4jh3tk9s%2Fv1.0%2Fv1.0&quot;, &quot;@type&quot;: &quot;koio:KnowledgeObject&quot;, &quot;dcterms:title&quot; : &quot; Tammemagi, 6 year Lung Cancer Risk Prediction Model for Screening&quot;, &quot;dcterms:identifier&quot; : &quot; ark:/99999/fk4jh3tk9s&quot;, &quot;dcterms:hasVersion&quot; :&quot;v1.0&quot;, &quot;dcterms:created&quot;:&quot;2016-04-15&quot;, &quot;dcterms:description&quot; : &quot;A 10-factor patient-level logistic regression model for estimating the risk of a future lung cancer diagnosis for a person&quot;, &quot;dcterms:creator&quot; : [&quot;https://kgrid.org/ &quot;,&quot;https://medicine.umich.edu/dept/learning-health-sciences&quot;], &quot;dcterms:source&quot; : [&quot;https://www.nejm.org/doi/pdf/10.1056/NEJMoa1211776&quot;], &quot;dcterms:publisher&quot; : &quot; https://medicine.umich.edu/dept/learning-health-sciences&quot;, &quot;dcterms:rights&quot; : &quot;All rights reserved.…”
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The structural analysis of verbal interaction by Clarke, D, Clarke, David

“…</p><p><em>Chapter 8</em> contains a brief discussion of two remaining issues arising from the linguistic analogy: the nature of the generative process in vivo by which the actor arrives at his performance, and the possible practical applications of a predictive model of social behaviour sequences in determining the outcome of a crucial course of events. …”

Examining the prevention and management of cardiovascular disease risk in Kenyan primary care settings by Muthee, TB

“…This risk modelling was based on the updated CVD risk prediction model for Eastern sub-Saharan Africa which was developed by the WHO with the International Society of Hypertension (ISH). …”

Analysis of the Interaction of Resilience Variables of New Urban Habitations against Earthquake Risk: A Case Study of Isfahan Metropolitan) by Fazlolah Karimi Ghotbabadi, Ali Zangiabadi

“…Isfahan metropolitan resilience predictive model in crisis and unexpected incidents. Journal of Geographical Researchers, 35(1), 19-30.- Liu, B., Chen, X., Zhou, Z., Tang, M., & Li, S. (2020). …”
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Evaluating the effectiveness and calculating the rate of return on investment of training courses using Kirk Patrick and Phillips models by Hossein Ashofteh, Elaheh Orangian

“…In a study entitled presenting the predictive model of the return on investment in training and improvement of the human source based on the organizational culture components and professional competency the concluded that training and improvement of human source and evaluation of the process of expense to benefit and the rate of the return on investment is effective on the training programs (Nouri, et al.2019). …”
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