Fall Risk Prediction Using Instrumented Footwear in Institutionalized Older Adults by Huanghe Zhang, Chuanyan Wu, Yulong Huang, Rui Song, Damiano Zanotto, Sunil K. Agrawal

“…AdaBoost algorithms are then utilized to develop predictive models based on the selected features. The models are evaluated using leave-one-out cross-validation and 10-fold cross-validation. …”
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Finding a constrained number of predictor phenotypes for multiple outcome prediction by Patrick B Ryan, Egill A Fridgeirsson, Jenna M Reps, Luis H John, Chungsoo Kim, Jenna Wong, Ross D Williams, Renae R Fisher

“…Model performance was evaluated using the constrained set of predictors across eight prediction tasks. …”
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Hybrid deep learning framework for real-time DO prediction in aquaculture by Longqin Xu, Wenjun Liu, Cai Chengqing, Tonglai Liu, Xuekai Gao, Ferdous Sohel, Murtaza Hasan, Mansour Ghorbanpour, Shahbaz Gul Hassan, Shuangyin Liu

“…Moreover, accuracy for short-term prediction was significantly improved, surpassing the performance of existing methods. …”
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A new prediction model for sustained ventricular tachycardia in arrhythmogenic cardiomyopathy by Baowei Zhang, Xin Xie, Jinbo Yu, Yizhang Wu, Jian Zhou, Xiaorong Li, Xiaorong Li, Bing Yang, Bing Yang

“…The calibration curve demonstrated a good consistency between the actual clinical results and the predicted outcomes. Decision curve analysis indicated that the nomogram had higher overall net benefits in predicting sVT.ConclusionWe have developed and internally validated a new prediction model for sVT in ACM. …”
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Bayesian Model Prediction for Breast Cancer Survival: A Retrospective Analysis by Islam Bani Mohammad, Muayyad M. Ahmad

“…The discriminative ability of models was evaluated by accuracy and the area under the curve (AUC) in terms of superior predictive performance for breast cancer outcomes. …”
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Development and validation of a model for predicting myopia in young children in China by Ting Hu, Rong Wu, Xi He, Zi-Yu Hua, Tian-Li Peng, Xiao-Yan Peng, Shi-Ming Li

“…This study aimed to construct a myopia prediction model that included perinatal factors and evaluate the correlation between each factor and myopia. …”
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Development and validation of a nomogram for predicting survival in patients with cardiogenic shock by Dingfeng Fang, Dingfeng Fang, Huihe Chen, Hui Geng, Xiahuan Chen, Meilin Liu

“…This study aims to develop a nomogram for evaluating severity in CS patients regardless of the underlying cause.Methods and resultsThe MIMIC-IV database was used to identify 1,923 CS patients admitted to the ICU. …”
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Incremental clinical value of intraplaque neovascularization in predicting recurrent ischemic stroke by Liuping Cui, Ran Liu, Fubo Zhou, Bing Tian, Ying Chen, Yingqi Xing

“…Herein, we investigated the clinical predictive value of IPN for recurrent ischemic stroke in a real‐world setting. …”
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Use machine learning to predict treatment outcome of early childhood caries by Yafei Wu, Maoni Jia, Ya Fang, Duangporn Duangthip, Chun Hung Chu, Sherry Shiqian Gao

“…The aim of this study is to explore the application of machine learning in predicting the treatment outcome of ECC. Methods This study was a secondary analysis of a recently published clinical trial that recruited 1,070 children aged 3- to 4-year-old with ECC. …”
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A human behavior-based model for respiratory infectious diseases prediction by Zhengwen Ma, Min Zhu, Chen Zhi, Huaguo Zhang, Minye Li, Nan Zhang, Hui Ma, Hui Ma

“…ObjectivesThe research aims to develop a human behavior-based model to predict respiratory infectious diseases.MethodsThis research employs semi-supervised machine learning techniques in conjunction with an RGB-depth camera to collect micro-level data. …”
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Postpartum depression risk prediction using explainable machine learning algorithms by Xudong Huang, Lifeng Zhang, Chenyang Zhang, Jing Li, Chenyang Li

“…This study aimed to develop an explainable machine learning model to predict the risk of PPD and to identify its key predictive factors.MethodsA retrospective analysis was conducted on 1,065 women who attended their 6-week postpartum follow-up visit at a tertiary maternal and child healthcare hospital in Shenyang, China, from January to December 2021. …”
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3D modeling to predict vascular involvement in resectable pancreatic adenocarcinoma by Sguinzi R, Vidal J, Poroes F, Bartolucci DA, Litchinko A, Gossin E, Fingerhut A, Toso C, Buhler L, Egger B

“…Methods: We retrospectively evaluated 16 patients with BR-PDAC near vascular structures who underwent pancreatoduodenectomy (PD) with or without vascular resection between 2015 and 2021. …”
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Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms by Seon Ui Lee, Sae Kyung Choi, Yun Sung Jo, Jeong Ha Wie, Jae Eun Shin, Yeon Hee Kim, Kicheol Kil, Hyun Sun Ko

“…Conclusions: A simplified machine learning model for predicting late FGR may be useful for evaluating individual risks in the early third trimester.…”
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Sendotypes predict worsening renal function in chronic kidney disease patients by Thomas McLarnon, Steven Watterson, Sean McCallion, Eamonn Cooper, Andrew R. English, Ying Kuan, David S. Gibson, Elaine K. Murray, Frank McCarroll, Shu‐Dong Zhang, Anthony J. Bjourson, Taranjit Singh Rai

“…These clusters were evaluated with three independent criteria assessing inter and intra cluster distances. …”
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Dynamic Optimization of Recurrent Networks for Wind Speed Prediction on Edge Devices by Laeeq Aslam, Runmin Zou, Ebrahim Shahzad Awan, Sayyed Shahid Hussain, Muhammad Asim, Samia Allaoua Chelloug, Mohammed A. ELAffendi

“…An adaptive Simulated Annealing algorithm with memory-based rejection (aSAR) navigates the discrete design space, employing nine objective functions that balance Mean Absolute Percentage Error (MAPE) against model compactness. Evaluations on wind datasets from Chile, Kazakhstan and Mongolia demonstrate that aSAR-optimized models reduce prediction errors by up to 54.17% and decrease MS by 98.75% relative to state-of-the-art techniques. …”
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Palliative Performance Scale predicts mortality in cardiac intensive care unit by Federica Frascaro, Gianni Casella, Laura Sofia Cardelli, Lorenzo Gamberini, Beatrice Dal Passo, Silvia Zagnoni, Francesca Sciarra, Alice Vitagliano, Valeria Carinci, Maria Laura Canale

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Nomogram for predicting the 28-day mortality risk of patients with subendocardial infarction by Menglei Li, Menglei Li, Beiping Song, Xianjing Zeng, Xunguo Wang, Ao Ma, Zhichao Meng, Jiehao Zhu, Xiubao Song, Xianwu Lan, Minghui Tan

“…The objective of our study was to develop and evaluate a nomogram for predicting the 28-day risk of mortality among patients with SEMI.MethodsPatients diagnosed with subendocardial infarction were identified from the MIMIC-III database based on ICD-9 codes. …”
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The role of genetic variants in the prediction of hearing loss due to cisplatin chemoradiotherapy by Charlotte W. Duinkerken, Sabrina Chiodo, Katrina Hueniken, Michael Hauptmann, Katarzyna Jóźwiak, Dangxiao Cheng, Andrew Hope, Geoffrey Liu, Charlotte L. Zuur

“…This study aims to enhance the predictive ability of the clinical prediction model for cisplatin‐induced hearing loss (CIHL) in HNSCC patients, as outlined in Theunissen et al., by incorporating significant genetic variants. …”
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Optimising ensemble streamflow predictions with bias correction and data assimilation techniques by M. Tanguy, M. Tanguy, M. Eastman, M. Eastman, A. Chevuturi, E. Magee, E. Cooper, R. H. B. Johnson, K. Facer-Childs, J. Hannaford, J. Hannaford

“…<p>This study evaluates the efficacy of bias correction (BC) and data assimilation (DA) techniques in refining hydrological model predictions. …”
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Thoracoscore: Predicting risk of in-hospital mortality for patients undergoing pulmonary resection by Đurić Dejan, Mališanović Gorica, Gvozdenović Ljiljana

“…Thoracoscore is the first model with multiple variables developed for predicting in-hospital mortality following pulmonary resections. …”
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