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Quantifying the Severity of Metopic Craniosynostosis Using Unsupervised Machine Learning
Anstadt, Erin E, Tao, Wenzheng, Guo, Ejay, Dvoracek, Lucas, Bruce, Madeleine K, Grosse, Philip J, Wang, Li, Kavan, Ladislav, Whitaker, Ross, Goldstein, Jesse A
Published in Plastic and reconstructive surgery (1963) (01.02.2023)
Published in Plastic and reconstructive surgery (1963) (01.02.2023)
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Unsupervised machine learning highlights the challenges of subtyping disorders of gut‐brain interaction
Dowrick, Jarrah M., Roy, Nicole C., Bayer, Simone, Frampton, Chris M. A., Talley, Nicholas J., Gearry, Richard B., Angeli‐Gordon, Timothy R.
Published in Neurogastroenterology and motility (01.12.2024)
Published in Neurogastroenterology and motility (01.12.2024)
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Supervised and unsupervised machine learning for automated scoring of sleep–wake and cataplexy in a mouse model of narcolepsy
Exarchos, Ioannis, Rogers, Anna A, Aiani, Lauren M, Gross, Robert E, Clifford, Gari D, Pedersen, Nigel P, Willie, Jon T
Published in Sleep (New York, N.Y.) (01.05.2020)
Published in Sleep (New York, N.Y.) (01.05.2020)
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Utilizing echocardiography and unsupervised machine learning for heart failure risk identification
Simonsen, Jakob Øystein, Modin, Daniel, Skaarup, Kristoffer, Djernæs, Kasper, Lassen, Mats Christian Højbjerg, Johansen, Niklas Dyrby, Marott, Jacob Louis, Jensen, Magnus Thorsten, Jensen, Gorm B., Schnohr, Peter, Martínez, Sergio Sánchez, Claggett, Brian Lee, Møgelvang, Rasmus, Biering-Sørensen, Tor
Published in International journal of cardiology (01.01.2025)
Published in International journal of cardiology (01.01.2025)
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104-OR: Identifying Painful Diabetic Neuropathy Subtypes from Resting-State Functional Magnetic Resonance Brain Imaging—A Novel Unsupervised Machine Learning Approach
SELVARAJAH, DINESH, TEH, KEVIN, ANANDHANARAYANAN, APARNA, SLOAN, GORDON P., TESFAYE, SOLOMON
Published in Diabetes (New York, N.Y.) (20.06.2023)
Published in Diabetes (New York, N.Y.) (20.06.2023)
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Unsupervised machine learning algorithm for face morphing
Sharma, Hitesh Kumar, Mahadev, Chadha, Raman, Nagarkoti, Gaurav, Ahlawat, Prashant
Published in AIP conference proceedings (15.06.2023)
Published in AIP conference proceedings (15.06.2023)
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Anomaly detection for atomic clocks using unsupervised machine learning algorithms
Chen, Edwin, Charbonneau, André, Gertsvolf, Marina, Wang, Yunli
Published in Metrologia (01.10.2024)
Published in Metrologia (01.10.2024)
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An unsupervised machine learning approach using passive movement data to understand depression and schizophrenia
Price, George D., Heinz, Michael V., Zhao, Daniel, Nemesure, Matthew, Ruan, Franklin, Jacobson, Nicholas C.
Published in Journal of affective disorders (01.11.2022)
Published in Journal of affective disorders (01.11.2022)
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Unsupervised Machine Learning with Cluster Analysis in Patients Discharged after an Acute Coronary Syndrome: Insights from a 23,270-Patient Study
Mohammadi, Tanya, D'Ascenzo, Fabrizio, Pepe, Martino, Bonsignore Zanghì, Stefano, Bernardi, Marco, Spadafora, Luigi, Frati, Giacomo, Peruzzi, Mariangela, De Ferrari, Gaetano Maria, Biondi-Zoccai, Giuseppe
Published in The American journal of cardiology (15.04.2023)
Published in The American journal of cardiology (15.04.2023)
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Structural and functional brain networks of individual differences in trait anger and anger control: An unsupervised machine learning study
Sorella, Sara, Vellani, Valentina, Siugzdaite, Roma, Feraco, Paola, Grecucci, Alessandro
Published in The European journal of neuroscience (01.01.2022)
Published in The European journal of neuroscience (01.01.2022)
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Differentiating a pachychoroid and healthy choroid using an unsupervised machine learning approach
Mirshahi, Reza, Naseripour, Masood, Shojaei, Ahmad, Heirani, Mohsen, Alemzadeh, Sayyed Amirpooya, Moodi, Farzan, Anvari, Pasha, Falavarjani, Khalil Ghasemi
Published in Scientific reports (29.09.2022)
Published in Scientific reports (29.09.2022)
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Unsupervised Machine Learning Reveals Novel Traumatic Brain Injury Patient Phenotypes with Distinct Acute Injury Profiles and Long-Term Outcomes
Folweiler, Kaitlin A., Sandsmark, Danielle K., Diaz-Arrastia, Ramon, Cohen, Akiva S., Masino, Aaron J.
Published in Journal of neurotrauma (15.06.2020)
Published in Journal of neurotrauma (15.06.2020)
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AD risk score for the early phases of disease based on unsupervised machine learning
Wang, Zheyu, Tang, Zhuojun, Zhu, Yuxin, Pettigrew, Corinne, Soldan, Anja, Gross, Alden, Albert, Marilyn
Published in Alzheimer's & dementia (01.11.2020)
Published in Alzheimer's & dementia (01.11.2020)
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Unsupervised machine-learning algorithms for the identification of clinical phenotypes in the osteoarthritis initiative database
Demanse, David, Saxer, Franziska, Lustenberger, Patrick, Tankó, László B., Nikolaus, Philipp, Rasin, Ilja, Brennan, Damian F., Roubenoff, Ronenn, Premji, Sumehra, Conaghan, Philip G, Schieker, Matthias
Published in Seminars in arthritis and rheumatism (01.02.2023)
Published in Seminars in arthritis and rheumatism (01.02.2023)
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Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance
Saboo, Krishnakant V., Varatharajah, Yogatheesan, Berry, Brent M., Kremen, Vaclav, Sperling, Michael R., Davis, Kathryn A., Jobst, Barbara C., Gross, Robert E., Lega, Bradley, Sheth, Sameer A., Worrell, Gregory A., Iyer, Ravishankar K., Kucewicz, Michal T.
Published in Scientific reports (22.11.2019)
Published in Scientific reports (22.11.2019)
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An Unsupervised Machine Learning-based Algorithm for Detecting Weak Impulsive Narrowband Quiet Sun Emissions and Characterizing Their Morphology
Bawaji, Shabbir, Alam, Ujjaini, Mondal, Surajit, Oberoi, Divya, Biswas, Ayan
Published in The Astrophysical journal (01.09.2023)
Published in The Astrophysical journal (01.09.2023)
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Identification of clinical heterogeneity and construction of a novel subtype predictive model in patients with ankylosing spondylitis: An unsupervised machine learning study
Sun, Xuhua, Zhou, Chenxing, Zhu, Jichong, Wu, Shaofeng, Liang, Tuo, Jiang, Jie, Chen, Jiarui, Chen, Tianyou, Huang, Sheng Sheng, Chen, Liyi, Ye, Zhen, Guo, Hao, Zhan, Xinli, Liu, Chong
Published in International immunopharmacology (01.04.2023)
Published in International immunopharmacology (01.04.2023)
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