Symptom-based prediction by machine learning to reduce hospital workload during COVID-19 surge: A validation study
Amelia Nur Vidyanti1,2*, Sekar Satiti1,2, Atitya Fithri Khairani1,2, Aditya Rifqi Fauzi1, Muhammad Hardhantyo3,4, Herdiantri Sufriyana5,6, Emily Chia-Yu Su5,7,8
1 Department of Neurology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
2 Department of Neurology, Dr. Sardjito General Hospital, Yogyakarta 55281, Indonesia
3 Center for Health Policy and Management, Faculty of Medicine, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
4 Faculty of Health Science, Respati University Yogyakarta, 55281, Indonesia
5 Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
6 Department of Medical Physiology, Faculty of Medicine, Universitas Nahdlatul Ulama Surabaya, 60237, Indonesia
7 Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei 11031, Taiwan
8 Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 11031, Taiwan
* Address all correspondence to: Amelia Nur Vidyanti, MD, PhD Department of Neurology Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada/Dr. Sardjito Hospital Jl. Kesehatan No. 1 Yogyakarta 55281, Indonesia Tel/Fax: +62-274-587333; E-mail: amelia.nur.v@ugm.ac.id
The journal article will be published soon.
To reproduce our work, a set of hardware requirements may be needed. We used a single machine. It was equipped by 8 logical processors for the 3.40 GHz central processing unit (CPU) (Core(TM) i7-4770, Intel®, Santa Clara, CA, USA), and 16 GB RAM. But, one can use a machine with only 4 logical processors and 4 GB RAM.
Please follow through the R Markdown (colab_scov2sar.Rmd). Installation approximately requires ~5 minutes.
All codes require ~5 minutes to complete. However, datasets should be requested to the corresponding author. Put data into a 'data' folder.
Briefly, all system requirements, installation guide, demo, and instructions for use are available in R Markdown (colab_scov2sar.Rmd) and other files in this repository.