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Cited by in F6Publishing
For: Aich S, Youn J, Chakraborty S, Pradhan PM, Park JH, Park S, Park J. A Supervised Machine Learning Approach to Detect the On/Off State in Parkinson's Disease Using Wearable Based Gait Signals. Diagnostics (Basel) 2020;10:E421. [PMID: 32575764 DOI: 10.3390/diagnostics10060421] [Cited by in Crossref: 8] [Cited by in F6Publishing: 14] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Marquez Chavez J, Tang W. A Vision-Based System for Stage Classification of Parkinsonian Gait Using Machine Learning and Synthetic Data. Sensors (Basel) 2022;22:4463. [PMID: 35746246 DOI: 10.3390/s22124463] [Reference Citation Analysis]
2 Trabassi D, Serrao M, Varrecchia T, Ranavolo A, Coppola G, De Icco R, Tassorelli C, Castiglia SF. Machine Learning Approach to Support the Detection of Parkinson's Disease in IMU-Based Gait Analysis. Sensors (Basel) 2022;22:3700. [PMID: 35632109 DOI: 10.3390/s22103700] [Reference Citation Analysis]
3 Suri JS, Paul S, Maindarkar MA, Puvvula A, Saxena S, Saba L, Turk M, Laird JR, Khanna NN, Viskovic K, Singh IM, Kalra M, Krishnan PR, Johri A, Paraskevas KI. Cardiovascular/Stroke Risk Stratification in Parkinson’s Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review. Metabolites 2022;12:312. [DOI: 10.3390/metabo12040312] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
4 Ileșan RR, Cordoș CG, Mihăilă LI, Fleșar R, Popescu AS, Perju-Dumbravă L, Faragó P. Proof of Concept in Artificial-Intelligence-Based Wearable Gait Monitoring for Parkinson's Disease Management Optimization. Biosensors (Basel) 2022;12:189. [PMID: 35448249 DOI: 10.3390/bios12040189] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
5 Salari N, Kazeminia M, Sagha H, Daneshkhah A, Ahmadi A, Mohammadi M. The performance of various machine learning methods for Parkinson’s disease recognition: a systematic review. Curr Psychol. [DOI: 10.1007/s12144-022-02949-8] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
6 Giannakopoulou KM, Roussaki I, Demestichas K. Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review. Sensors (Basel) 2022;22:1799. [PMID: 35270944 DOI: 10.3390/s22051799] [Reference Citation Analysis]
7 di Biase L, Tinkhauser G, Martin Moraud E, Caminiti ML, Pecoraro PM, Di Lazzaro V. Adaptive, personalized closed-loop therapy for Parkinson's disease: biochemical, neurophysiological, and wearable sensing systems. Expert Rev Neurother 2021;:1-18. [PMID: 34736368 DOI: 10.1080/14737175.2021.2000392] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
8 Phokaewvarangkul O, Vateekul P, Wichakam I, Anan C, Bhidayasiri R. Using Machine Learning for Predicting the Best Outcomes With Electrical Muscle Stimulation for Tremors in Parkinson's Disease. Front Aging Neurosci 2021;13:727654. [PMID: 34566628 DOI: 10.3389/fnagi.2021.727654] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
9 Mantri S, Lepore M, Edison B, Daeschler M, Kopil CM, Marras C, Chahine LM. The Experience of OFF Periods in Parkinson's Disease: Descriptions, Triggers, and Alleviating Factors. J Patient Cent Res Rev 2021;8:232-8. [PMID: 34322575 DOI: 10.17294/2330-0698.1836] [Reference Citation Analysis]
10 Barrachina-Fernández M, Maitín AM, Sánchez-Ávila C, Romero JP. Wearable Technology to Detect Motor Fluctuations in Parkinson's Disease Patients: Current State and Challenges. Sensors (Basel) 2021;21:4188. [PMID: 34207198 DOI: 10.3390/s21124188] [Cited by in F6Publishing: 7] [Reference Citation Analysis]
11 Corrà MF, Atrsaei A, Sardoreira A, Hansen C, Aminian K, Correia M, Vila-Chã N, Maetzler W, Maia L. Comparison of Laboratory and Daily-Life Gait Speed Assessment during ON and OFF States in Parkinson's Disease. Sensors (Basel) 2021;21:3974. [PMID: 34207565 DOI: 10.3390/s21123974] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
12 Hussain A, Choi HE, Kim HJ, Aich S, Saqlain M, Kim HC. Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques. Diagnostics (Basel) 2021;11:829. [PMID: 34064395 DOI: 10.3390/diagnostics11050829] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
13 Yadav S, Singh MK. Hybrid Machine Learning Classifier and Ensemble Techniques to Detect Parkinson’s Disease Patients. SN COMPUT SCI 2021;2. [DOI: 10.1007/s42979-021-00587-8] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Site A, Nurmi J, Lohan ES. Systematic Review on Machine-Learning Algorithms Used in Wearable-Based eHealth Data Analysis. IEEE Access 2021;9:112221-35. [DOI: 10.1109/access.2021.3103268] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]