Text Extraction from Electronic Health Records for Predicting the Patient Diabetics Level by Machine Learning
| Autor: | R. Vineeth, R. Rithish, B. V. Ajay Prakash, D. V. S. N. Sai Varma, N. Monish Gowda |
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| Rok vydání: | 2021 |
| Předmět: |
Focus (computing)
Cost efficiency business.industry Computer science Medical record Machine learning computer.software_genre Field (computer science) Task (project management) Random forest health services administration Redundancy (engineering) Feature (machine learning) Artificial intelligence business computer health care economics and organizations |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9789811601705 |
| DOI: | 10.1007/978-981-16-0171-2_27 |
| Popis: | In the modern world with the increase in the technical terms used in the medical field, the problem in understanding these medical terms arises and the need for the electronic health records (EHR) for easy understanding by common people is needed. EHR gives the complete and updated details of a particular patient. EHR is cost efficient as it decreases the required paperwork and reduces the redundancy of testing. The electronic health records (EHRs) mainly focus on making the task easier for the people in interpreting the medical reports which seem complex in understanding. The EHR contains valuable data for identifying health outcomes. EHRs are real-time entities where patients provide medical records that are interpreted and stored digitally so that they are readily available whenever required. The aforementioned feature would be helpful for patients to get an idea about the type of diabetes that includes Type-1, Type-2, pre-diabetes and no_diabetes which will be predicted using various machine learning algorithms such as random forest and LightGBM which showed a prominent result and best-obtained accuracy. |
| Databáze: | OpenAIRE |
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