A comprehensive review of predictive analytics models for mental illness using machine learning algorithms by Md. Monirul Islam, Shahriar Hassan, Sharmin Akter, Ferdaus Anam Jibon, Md. Sahidullah

“…This study reviews the machine learning models, algorithms, and applications for the early detection of mental disease, particularly emphasizing the data modalities. …”
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Risk prediction models for pregnancy outcomes in recurrent pregnancy loss: a narrative systematic review by Qiliang Jian, Fangxiang Mu, Kexin Wang, Fang Wang

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Risk prediction models for mortality in patients with severe pneumonia: a systematic review and meta-analysis by Xiaoyu Wang, Xiaoyu Wang, Zhenzhen Feng, Zhenzhen Feng, Zhenzhen Feng, Lu Wang, Lu Wang, Wenrui Liu, Wenrui Liu, Jiansheng Li, Jiansheng Li, Jiansheng Li

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The importance of degradation mode analysis in parameterising lifetime prediction models of lithium-ion battery degradation by Ruihe Li, Niall D. Kirkaldy, Fabian F. Oehler, Monica Marinescu, Gregory J. Offer, Simon E. J. O’Kane

“…Recent years have witnessed a surge in lifetime prediction papers using physics-based, empirical, or data-driven models, most of which have been validated against the remaining capacity (capacity fade) and sometimes resistance (power fade). …”
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Stage-based colorectal cancer prediction on uncertain dataset using rough computing and LSTM models by K. Supriya, A. Anitha

“…This paper has made an effort, to handle uncertainty using a rough set of fuzzy approximation space as pre-processing and utilized Unidirectional and Bidirectional LSTM for the classification and prediction process. Thus, to demonstrate improved predictive accuracy, the proposed model adapted the optimizers and evaluated using benchmarking techniques in predicting stage-based survival rate. …”
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Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis by Bianca Buurman, Patricia Jepma, Bastiaan Van Grootven, Koen Milisen, Mieke Deschodt, Mariska Leeflang, Joost Daams, Corinne Rijpkema, Lotte Verweij

“…Objective To describe the discrimination and calibration of clinical prediction models, identify characteristics that contribute to better predictions and investigate predictors that are associated with unplanned hospital readmissions.Design Systematic review and meta-analysis.Data source Medline, EMBASE, ICTPR (for study protocols) and Web of Science (for conference proceedings) were searched up to 25 August 2020.Eligibility criteria for selecting studies Studies were eligible if they reported on (1) hospitalised adult patients with acute heart disease; (2) a clinical presentation of prediction models with c-statistic; (3) unplanned hospital readmission within 6 months.Primary and secondary outcome measures Model discrimination for unplanned hospital readmission within 6 months measured using concordance (c) statistics and model calibration. …”
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Assessment of Maintenance Strategies and Performance Prediction for Urban Roads Using IRI and HDM-4 Models by Harinder D, Yugendar Poojari, Venkatesh Noolu, Vamsi Dachepalli

“…In HDM-4, pavement distress initiation and progression can be predicted using HDM-4 pavement deterioration models using different parameters like traffic, climate, pavement structure, and composition combinations. …”
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Prediction of digestible energy requirement in growing finishing stage of pigs using machine learning models by Nibas Chandra Deb, Jayanta Kumar Basak, Sijan Karki, Elanchezhian Arulmozhi, Dae Yeong Kang, Niraj Tamrakar, Eun Wan Seo, Junghoo Kook, Myeong Yong Kang, Hyeon Tae Kim

“…Most importantly, the sensitivity analysis revealed that BW exerted the most significant impact on predicting DER among other variables. In conclusion, the study suggested that RFR model can precisely predict DER, offering valuable insights for pig farmers to enhance their understanding of DE management.…”
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Leveraging In Silico and Artificial Intelligence Models to Advance Drug Disposition and Response Predictions Across the Lifespan by Kyunghee Yang, Daniel Gonzalez, Jeffrey L. Woodhead, Pallavi Bhargava, Murali Ramanathan

“…These models integrate clinical trial and real‐world data (RWD) to predict drug exposure, efficacy, and safety. …”
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Development of Smart Models to Accurately Predict Dynamic Viscosity of CO2-Saturated Polyethylene Glycol by Ayat Hussein Adhab, Morug Salih Mahdi, Bhavesh Kanabar, Anupam Yadav, Ranganathaswamy M K, Rishabh Thakur, Parveen Kumar, Braj Krishna, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

“…Ultimately, multilayer perceptron artificial neural network model is found to be the most accurate method for predicting CO2-saturated PEG viscosity.…”
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Development of machine learning models to predict clinical outcome and recovery time in dogs with parvovirus enteritis by Negin Sanaei, Mohamad Zamani-Ahmadmahmudi, Seyed Mahdi Nassiri

“…A model was developed using four variables (SIRS, deworming, vaccination and crying) to predict clinical outcome. …”
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New insight into viscosity prediction of imidazolium-based ionic liquids and their mixtures with machine learning models by Amir Hossein Sheikhshoaei, Ali Sanati

“…Machine learning (ML) models have been implemented, and their performance in viscosity prediction for IL mixtures was compared with a molecular-based model, ePC-SAFT-FVT (ePC-FVT-MB), and an ion-based model, ePC-SAFT-FVT (ePC-FVT-MB). …”
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Improving drug-drug interaction prediction via in-context learning and judging with large language models by He Qi, He Qi, Xiaoqiang Li, Chengcheng Zhang, Tianyi Zhao, Tianyi Zhao

“…To further refine predictions, we employ GPT-4 as a discriminator to assess the relevance of predictions generated by multiple LLMs.ResultsDDI-JUDGE achieves the best performance among all models in both zero-shot and few-shot settings, with an AUC of 0.642/0.788 and AUPR of 0.629/0.801, respectively. …”
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PERFORMANCE OF SIX PREDICTIVE MODELS OF DEATH OF PATIENTS HOSPITALIZED FOR DECOMPENSATED CIRRHOSIS: A MULTICENTER STUDY by Ajácio Bandeira de Mello BRANDÃO, Isadora Zanotelli BOMBASSARO, Gabriela Perdomo CORAL, Jonathan SOLDERA, Carlos KUPSKI

“…Several models have been developed and validated. Objective: To analyze and compare the performance of models in predicting 90-day mortality among patients hospitalized with decompensated cirrhosis. …”
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Impact of a multiple oversampling technique-based assessment framework on shallow rockburst prediction models by Guozhu Rao, Guozhu Rao, Yunzhang Rao, Yunzhang Rao, Yangjun Xie, Qiang Huang, Jiazheng Wan, Jiyong Zhang

“…The occurrence of class-imbalanced datasets is a frequent observation in natural science research, emphasizing the paramount importance of effectively harnessing them to construct highly accurate models for rockburst prediction. Initially, genuine rockburst incidents within a burial depth of 500 m were sourced from literature, revealing a small dataset imbalance issue. …”
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LPBSA: Pre-clinical data analysis using advanced machine learning models for disease prediction by Dana R. Hamad, Tarik A. Rashid

“…These findings indicate the effectiveness of LPBSA in enhancing predictive modeling for critical health conditions.…”
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Adaptability of prognostic prediction models for patients with acute coronary syndrome during the COVID-19 pandemic by Takeshi Nakamura, Satoaki Matoba, Masahiro Nishi, Kenji Yanishi

“…Background The detrimental repercussions of the COVID-19 pandemic on the quality of care and clinical outcomes for patients with acute coronary syndrome (ACS) necessitate a rigorous re-evaluation of prognostic prediction models in the context of the pandemic environment. …”
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Hyperspectral Prediction Models of Chlorophyll Content in <i>Paulownia</i> Leaves under Drought Stress by Yamei Zhang, Guangxin Ru, Zhenli Zhao, Decai Wang

“…Based on the prediction accuracy and the uniformity of different leaf positions, the optimal model was systematically explored. …”
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Deflection Predictions of Tapered Cellular Steel Beams Using Analytical Models and an Artificial Neural Network by Amine Osmani, Rabee Shamass, Konstantinos Daniel Tsavdaridis, Felipe Piana Vendramell Ferreira, Abdelwahhab Khatir

“…The deflections predicted by the analytical and ANN models were compared with finite element results, showing good agreement.…”
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Air quality prediction based on factor analysis combined with Transformer and CNN-BILSTM-ATTENTION models by Shuyuan Liu, Yang Hu

“…Abstract This study presents an innovative air quality prediction framework that integrates factor analysis with deep learning models for precise prediction of original variables. …”
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