Stroke Prognosis Dataset Taizhou and Fuyong

Citation Author(s):
Haochang
Jin
Shenzhen University
Submitted by:
Haochang Jin
Last updated:
Sat, 02/22/2025 - 03:02
DOI:
10.21227/m0qj-h307
License:
Creative Commons Attribution
206 Views
Categories:
Artificial Intelligence
Machine Learning
Brain
Health
Keywords:
Artificial intelligence (AI), Stroke prognosis, prediction
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Abstract 

The Fuyong dataset records 134 stroke patients who received treatment in the Shenzhen Fuyong People's Hospital between March 1, 2022, and September 31, 2024. Besides, medical records of 435 stroke patients treated in the Affiliated Taizhou People's Hospital of Nanjing Medical University between January 1, 2020, and December 31, 2023, are included in the Taizhou dataset. These two datasets use the pre- and post-thrombolysis of the NIHSS scores as a metric for evaluating the immediate efficacy of the thrombolytic intervention. A decrease of 4 or more points in the NIHSS score after thrombolysis is considered a positive outcome, indicating an effective prognosis, while a decrease of fewer than 4 points is regarded as a negative outcome, indicating a poor prognosis.

Instructions: 

There two parts of the dataset. "Fuyong" are records of stroke patients provided by Shenzhen Fuyong People's Hospital and "Taizhou" are records provided by the Affiliated Taizhou People's Hospital of Nanjing Medical University. All records are in the format of excel sheets. Python scripts used to preprocess the dataset are provided.

Comments

My name is Taguhi Yenokyan and I am a student researcher at California State University Northridge, currently conducting research in machine learning for medical diagnosis. Access to the "Stroke Prognosis Dataset Taizhou and Fuyong" dataset would significantly contribute to advancing my research. I assure you that the dataset will be used solely for academic purposes and handled in compliance with all ethical and privacy guidelines set by my institution and any applicable data regulations.

 

If there are any formal agreements, ethical review processes, or additional steps required to obtain access, I am happy to comply. I would greatly appreciate any guidance on the process and any associated terms of use.

 

Please let me know if you need any further details regarding my research or institutional affiliation. I appreciate your time and consideration and look forward to your response.

 

Best regards,

Taguhi Yenokyan

Submitted by Taguhi Yenokyan on Tue, 03/11/2025 - 17:11

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