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Computational tool for predicting in-hospital mortality in severe spontaneous intracerebral hemorrhage

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This app serves as a computational tool for predicting in-hospital mortality in severe spontaneous intracerebral hemorrhage. Users can input relevant clinical data, including information on medication usage, vital signs, laboratory values, and patient history. Upon submission, the app calculates the probability of hospitalization and mortality from the disease, with a threshold for disease mortality set at 29.3%. Based on this prediction, the app provides guidance on whether early intervention and monitoring are warranted or if current treatment can be continued with close monitoring of the patient's condition. Additionally, the app generates a SHAP force plot to visualize the impact of each feature on the prediction, aiding in the interpretation of the model's decision. This tool can be valuable for healthcare professionals in assessing the prognosis of patients with severe spontaneous intracerebral hemorrhage and guiding clinical management strategies.

See owner's GitHub repository for more information:

This app is based on the paper:

Mao, B., Zhang, R., Pan, Y., Zheng, R., Shen, Y., Lu, W., Lu, Y., Xu, S., Wu, J., Wang, M., & Wan, S. (2023). Machine learning for the prediction of in-hospital mortality in patients with spontaneous intracerebral hemorrhage. medRxiv, 2023.08.15.23294147.

This application was not uploaded by the author, but through their publicly available Github repository,

MIT License

Copyright (c) 2023 Rrreert


Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.

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