| Autor: |
Singh, Sandeep Kumar, Bhatia, Madhulika, Madaan, Rosy, Wadhwa, Bhawna |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3031 Issue 1, p1-14, 14p |
| Abstrakt: |
Globally, the behavior of people seeking information is changing due to the need for health information. Finding health information online on illnesses, diagnoses, and treatments can be difficult for many people. It will save a lot of time if a recommendation system for physicians and medications can be created utilizing review mining. They have difficulty understanding the diverse medical jargon. Actually, the dataset contains forty unique diseases and one hundred thirty-two symptoms. The system used a 92% pre-trained model and a testing portion is about 8% to enhance the accuracy of the K-Nearest Neighbor algorithm. The goal of the recommender system is to adapt to the unique user-related needs of the health domain. To the best of our knowledge, no recent work in the area of remedial big data analytics has focused on all data kinds simultaneously. When compared to various common calculating algorithms, our suggested algorithm's planning accuracy is 93% with a regular pace that is faster than the uni-modal illness risk prediction algorithm and generates a report. In this study, there were 25,074 discharge summaries from NYPH included in the case report. Healthcare information technology is transforming the way healthcare providers interact with patients and driving productivity, quality, and efficiency. To support this transformation, the Methodology for this flow of diagram is "Fig 1". All healthcare professionals have secure access to health information that is relevant to their practice setting. Studies for predicting general disease are taken from general symptoms are shown in Table 2. HIE also needs a system that can recognize sensitive content and provide recommendations so healthcare providers can avoid potentially compromising conversations with their patients. Based on our research, we propose an alternative for healthcare professionals who want tailored recommendation engines for their specific needs. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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