Toxicity analysis of adjuvant radiotherapy in breast cancer

Romero, Aníbal; Calvo, M. Espinosa; Ariza, Maria Virginia Trujillo; Serrano, C. Bueno; Ugalde, Pilar Font; Beltrán, L. Tudela; Cabezas, S. García; Díez, Marc; Eito, A. Palacios

doi:10.1016/j.rpor.2013.03.118

Cited by 1 publication

(2 citation statements)

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“…Due to the multiplicity and high dimensionality of the data, machine learning or deep learning methods need to be used to extract information from complex electronic medical record data. Paper [ 16 ] considered the concept extraction problem as a sequence labeling task and explored various structure learning methods based on RNN feature extraction with the goal of assigning relevant labels to each key entity word in clinical medical records. Word2vec models are used in [ 17 ] to transform some clinical concepts in electronic medical records into high-dimensional vectors and then used these vectors to represent patients and used them as the downstream learning task input.…”

Section: Related Workmentioning

confidence: 99%

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Value Analysis of Neoadjuvant Radiotherapy for Breast Cancer after Modified Radical Mastectomy Based on Data Mining

Liu

Huang

Pan

et al. 2022

Computational Intelligence and Neuroscience

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Over the last two generations, there has been a surge of interest in nonmutilating treatment for women with early breast cancer. Neoadjuvant radiation therapy, which is progressively being provided to breast cancer patients, could be used to decrease tumor burden while also providing an ability to examine treatment response. This paper aims to explore the effects of the initiation time of radiotherapy after modified adjuvant radical mastectomy on the prognosis of breast cancer. The EMR data can be used to mine hidden rules, which are of great significance for treatment and prognosis analysis. In collaboration with breast cancer, the appropriate prediction model and visualization method are selected and a visual analysis system for breast cancer group and treatment plan based on electronic medical record is constructed. Patients with multiple dimensions are reduced and clustered to form patient groups. The differences of characteristics among patient groups are intuitively displayed by using Nightingale diagram, word cloud, and time axis visualization methods. The support vector machine (SVM) model is used to predict the treatment scheme. The radiotherapy time after modified radical surgery in the two groups was within 15 weeks (observation group) and 15 weeks (routine group), respectively. The incidence of complications, local recurrence rate, progression-free survival, and quality of life scores of patients in the routine group and observation group were compared. The total incidence of complications differed significantly between the observation and routine groups. The physical function, material function, psychological function, and social function of the observation group were significantly higher than the routine group P < 0.05 . Radiotherapy within 15 weeks after modified radical mastectomy for breast cancer can not only reduce the local recurrence rate but also prolong the progression-free survival of patients, and the incidence of complications will not increase, which will greatly help improve the quality of life of patients.

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Section: Related Workmentioning

confidence: 99%

“…In this paper, we choose the multidimensional scaling (MDS) [ 14 – 16 ] algorithm to reduce the high-dimensional attributes of patients to two dimensions. The MDS algorithm calculates the similarity between patients using geometric space (Euclidean space or high-dimensional space) distances.…”

Section: Visual Analysis Of Similar Patient Cohortsmentioning

confidence: 99%