Search Results - "machines"

Prognostication of treatment non-compliance among patients with multidrug-resistant tuberculosis in the course of their follow-up: a logistic regression-based machine learning algorithm by Denekew Tenaw Anley, Temesgen Yihunie Akalu, Temesgen Yihunie Akalu, Temesgen Yihunie Akalu, Anteneh Mengist Dessie, Rahel Mulatie Anteneh, Melkamu Aderajew Zemene, Wubet Alebachew Bayih, Wubet Alebachew Bayih, Yenealem Solomon, Natnael Atnafu Gebeyehu, Gizachew Ambaw Kassie, Misganaw Asmamaw Mengstie, Endeshaw Chekol Abebe, Mohammed Abdu Seid, Molalegn Mesele Gesese, Natnael Moges, Berihun Bantie, Sefineh Fenta Feleke, Tadesse Asmamaw Dejenie, Getachew Asmare Adella, Achenef Asmamaw Muche, Achenef Asmamaw Muche, Achenef Asmamaw Muche

Connect to this object online.

Classification of Children With Autism and Typical Development Using Eye-Tracking Data From Face-to-Face Conversations: Machine Learning Model Development and Performance Evaluation by Zhong Zhao, Haiming Tang, Xiaobin Zhang, Xingda Qu, Xinyao Hu, Jianping Lu

Connect to this object online.

The Effect of Preparation Design on the Fracture Resistance of Zir-conia Crown Copings (Computer Associated Design/Computer Asso-ciated Machine, CAD/CAM System) by E. Jalalian, B. Atashkar, R. Rostami

Connect to this object online.

Comparison of Empirical Mode Decomposition, Wavelets, and Different Machine Learning Approaches for Patient-Specific Seizure Detection Using Signal-Derived Empirical Dictionary Approach by Muhammad Kaleem, Aziz Guergachi, Sridhar Krishnan

Connect to this object online.

Machine learning‐guided covariate selection for time‐to‐event models developed from a small sample of real‐world patients receiving bevacizumab treatment by Eleni Karatza, Apostolos Papachristos, Gregory B. Sivolapenko, Daniel Gonzalez

Connect to this object online.

Investigating Associations Between Depressive Symptoms and Anti-/Pro-Inflammatory Nutrients in an Elderly Population in Northern China: A Bayesian Kernel Machine Regression Approach by Li R, Zhan W, Huang X, Zhang L, Sun Y, Zhang Z, Bao W, Ma Y

Connect to this object online.

Identifying Elevated Risk for Future Pain Crises in Sickle-Cell Disease Using Photoplethysmogram Patterns Measured During Sleep: A Machine Learning Approach by Yunhua Ji, Patjanaporn Chalacheva, Carol L. Rosen, Michael R. DeBaun, Thomas D. Coates, Michael C. K. Khoo

Connect to this object online.

A Machine Learning Algorithm Outperforms Traditional Multiple Regression to Predict Risk of Unplanned Overnight Stay Following Outpatient Medial Patellofemoral Ligament Reconstruction by Chimere O. Ezuma, B.S, Yining Lu, M.D, Ayoosh Pareek, M.D, Ryan Wilbur, B.S, Aaron J. Krych, M.D, Brian Forsythe, M.D, Christopher L. Camp, M.D

Connect to this object online.

Prediction of delayed breastfeeding initiation among mothers having children less than 2 months of age in East Africa: application of machine learning algorithms by Agmasie Damtew Walle, Zenebe Abebe Gebreegziabher, Habtamu Setegn Ngusie, Sisay Yitayih Kassie, Abera Lambebo, Fitsum Zekarias, Tadesse Mamo Dejene, Shimels Derso Kebede

Connect to this object online.

Interoperable and explainable machine learning models to predict morbidity and mortality in acute neurological injury in the pediatric intensive care unit: secondary analysis of the TOPICC study by Neil K. Munjal, Neil K. Munjal, Robert S. B. Clark, Dennis W. Simon, Patrick M. Kochanek, Christopher M. Horvat

Connect to this object online.

Machine Learning for Predicting Risk and Prognosis of Acute Kidney Disease in Critically Ill Elderly Patients During Hospitalization: Internet-Based and Interpretable Model Study by Mingxia Li, Shuzhe Han, Fang Liang, Chenghuan Hu, Buyao Zhang, Qinlan Hou, Shuangping Zhao

Connect to this object online.

Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models by Liu, Dianbo, Clemente, Leonardo, Poirier, Canelle, Ding, Xiyu, Chinazzi, Matteo, Davis, Jessica, Vespignani, Alessandro, Santillana, Mauricio

Connect to this object online.

Correction: Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study by David Amadi, Sylvia Kiwuwa-Muyingo, Tathagata Bhattacharjee, Amelia Taylor, Agnes Kiragga, Michael Ochola, Chifundo Kanjala, Arofan Gregory, Keith Tomlin, Jim Todd, Jay Greenfield

Connect to this object online.

The role of various physiological and bioelectrical parameters for estimating the weight status in infants and juveniles cohort from the Southern Cuba region: a machine learning study by Taira Batista Luna, Jose Luis García Bello, Agustín Garzón Carbonell, Ana de la Caridad Román Montoya, Alcibíades Lara Lafargue, Héctor Manuel Camué Ciria, Yohandys A. Zulueta

Connect to this object online.

Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis by Chunxiang Feng, Zhixian Wang, Zhixian Wang, Chang Liu, Shiliang Liu, Yuxi Wang, Yuanyuan Zeng, Qianqian Wang, Tianming Peng, Xiaoyong Pu, Jiumin Liu

Connect to this object online.

Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study by Hale M Thompson, Brihat Sharma, Dale L Smith, Sameer Bhalla, Ihuoma Erondu, Aniruddha Hazra, Yousaf Ilyas, Paul Pachwicewicz, Neeral K Sheth, Neeraj Chhabra, Niranjan S Karnik, Majid Afshar

Connect to this object online.

Machine learning methods for propensity and disease risk score estimation in high-dimensional data: a plasmode simulation and real-world data cohort analysis by Yuchen Guo, Victoria Y. Strauss, Martí Català, Annika M. Jödicke, Sara Khalid, Daniel Prieto-Alhambra, Daniel Prieto-Alhambra

Connect to this object online.

A five-year (2015 to 2019) analysis of studies focused on breast cancer prediction using machine learning: A systematic review and bibliometric analysis by Zakia Salod, Yashik Singh

Connect to this object online.

Integrating real‐world data and machine learning: A framework to assess covariate importance in real‐world use of alternative intravenous dosing regimens for atezolizumab by Bianca Vora, Ashutosh Jindal, Erick Velasquez, James Lu, Benjamin Wu

Connect to this object online.

K-Means Clustering Machine Learning Approach Reveals Groups of Homogeneous Individuals With Unique Brain Activation, Task, and Performance Dynamics Using fNIRS by Manob Jyoti Saikia

Connect to this object online.