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- 01J55SQ05PKB8H6TC3NRVVDZ7N classification A1.
- 01J55SQ05PKB8H6TC3NRVVDZ7N date "2024".
- 01J55SQ05PKB8H6TC3NRVVDZ7N language "eng".
- 01J55SQ05PKB8H6TC3NRVVDZ7N type journalArticle.
- 01J55SQ05PKB8H6TC3NRVVDZ7N hasPart 01J55SXXCE82K2ZNM18R6Z0VYC.pdf.
- 01J55SQ05PKB8H6TC3NRVVDZ7N subject "Medicine and Health Sciences".
- 01J55SQ05PKB8H6TC3NRVVDZ7N subject "Technology and Engineering".
- 01J55SQ05PKB8H6TC3NRVVDZ7N doi "10.3390/app14135556".
- 01J55SQ05PKB8H6TC3NRVVDZ7N issn "2076-3417".
- 01J55SQ05PKB8H6TC3NRVVDZ7N issue "13".
- 01J55SQ05PKB8H6TC3NRVVDZ7N volume "14".
- 01J55SQ05PKB8H6TC3NRVVDZ7N abstract "In the realm of anesthetic management during surgical procedures, the reliable estimation of mean arterial pressure (MAP) is critical for ensuring patient safety and optimizing drug administration. This paper investigates the determination of the optimal Long Short-Term Memory (LSTM) architectures aimed at enhancing the estimation of MAP. Using data from a trial involving 70 patients undergoing Total Intravenous Anesthesia (TIVA) provides the effect-site concentrations of Propofol and Remifentanil as key input variables for LSTM models. Our solution categorizes the selection strategies into three distinct methodologies: (i) a population-based method applying a single model across all patients, (ii) a patient-specific method tailoring models to individual physiological responses, and (iii) a novel category-specific method that groups patients based on the correlation between input variables, the effect-site concentrations of Propofol and Remifentanil, and MAP output. The novelty of this paper lies in the proposed method to identify the optimal architecture, evaluating 288 models to fine-tune the best model for each patient and category. Our findings suggest that the patient-specific model outperforms others, highlighting the benefits of personalized model architectures in medical artificial intelligence (AI) applications. The category-specific models provide a pragmatic solution, with reasonable accuracy and enhanced computational efficiency. By contrast, the population-based models, while efficient, have a lower estimation accuracy. This study confirms the significance of sophisticated LSTM architectures in medical AI, providing insights into their potential for advancing patient-specific anesthetic care by accurately online estimating MAP.".
- 01J55SQ05PKB8H6TC3NRVVDZ7N author 30a972b2-9784-11ed-bac3-dd73abd88931.
- 01J55SQ05PKB8H6TC3NRVVDZ7N author C10E6192-1B01-11E2-A477-54B510BDE39D.
- 01J55SQ05PKB8H6TC3NRVVDZ7N author F6F803D2-F0ED-11E1-A9DE-61C894A0A6B4.
- 01J55SQ05PKB8H6TC3NRVVDZ7N author FEE28A86-F0ED-11E1-A9DE-61C894A0A6B4.
- 01J55SQ05PKB8H6TC3NRVVDZ7N author b13acc98-cb2c-11ec-887a-cc1b0132e2ed.
- 01J55SQ05PKB8H6TC3NRVVDZ7N dateCreated "2024-08-13T11:59:27Z".
- 01J55SQ05PKB8H6TC3NRVVDZ7N dateModified "2024-12-12T20:59:53Z".
- 01J55SQ05PKB8H6TC3NRVVDZ7N name "Selecting optimal Long Short-Term Memory (LSTM) architectures for online estimation of mean arterial pressure (MAP) in patients undergoing general anesthesia".
- 01J55SQ05PKB8H6TC3NRVVDZ7N pagination urn:uuid:3109b4aa-07a6-42ad-bed3-40e81e81d7a3.
- 01J55SQ05PKB8H6TC3NRVVDZ7N sameAs LU-01J55SQ05PKB8H6TC3NRVVDZ7N.
- 01J55SQ05PKB8H6TC3NRVVDZ7N sourceOrganization urn:uuid:43c8a699-0ec6-432f-b7be-746ae43e7d61.
- 01J55SQ05PKB8H6TC3NRVVDZ7N sourceOrganization urn:uuid:8862efd8-4870-42e6-af9f-9926e82f5bda.
- 01J55SQ05PKB8H6TC3NRVVDZ7N type A1.