Role of physical medicine for cancer rehabilitation and return to work under the premise of the “Wiedereingliederungsteilzeitgesetz”

Cenik, Fadime; Mähr, Bruno; Palma, Stefano; Kéilani, Mohammad; Nowotny, Thomas; Crevenna, Richard

doi:10.1007/s00508-019-1504-7

Cited by 12 publications

(6 citation statements)

References 30 publications

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“…Thermotherapy includes the application of heat or cold, while phototherapy includes ultraviolet light, so-called cooled red light, and infrared light. Balneology and climate therapy include baths, mud, and different climatic conditions, which have been shown to be effective in muscular relaxation, pain reduction, and improvement in the flexibility of structures [67-70].…”

Section: Physical Medicine and Rehabilitationmentioning

confidence: 99%

Radiotherapy-Induced Fatigue in Breast Cancer Patients

et al. 2020

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Background: A large proportionof breast cancer patients who undergo adjuvant radiotherapy suffer from radiotherapy-induced fatigue. The possible causative factors of this specific side effect are diverse. Summary: Prevalence, duration, and severity of radiotherapy-induced fatigue are dependent on the type of radiotherapy, as well as on the irradiated volume, dose scheme, on the number of radiation fields, the combination with other treatments, diurnal rhythm, smoking, and time-to-hospitalization. Recommended treatments include non-pharmacologic interventions, such as physical and psychosocial interventions. Pharmacologic therapies include treatment with methylphenidate and modafinil. In addition to its early detection with standardized instruments, adequate education to breast cancer patients about risks and predisposing factors of radiotherapy-induced fatigue is essential. Multidimensional strategies help to maintain the patients’ quality of life and therefore guarantee treatment adherence and efficacy. Key Messages: Radiotherapy-induced fatigue is an underreported, underdiagnosed, and undertreated side effect. This review provides an overview of radiotherapy-induced fatigue in breast cancer patients receiving adjuvant radiotherapy.

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Section: Physical Medicine and Rehabilitationmentioning

confidence: 99%

Radiotherapy-Induced Fatigue in Breast Cancer Patients

et al. 2020

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“…In 1971, the United States first proposed the concept of “tumor rehabilitation” ( 4 ), the main purpose of which is to help cancer patient’s recover their mental, physical and physical functions under cancer conditions and limited treatment. The way of cancer rehabilitation mainly depends on the nature of the tumor and the stage of development of the tumor ( 5 ). Early detection and rehabilitation of cancer have greatly improved the survival rate and quality of life of patients.…”

Section: Introductionmentioning

confidence: 99%

An Assisted Diagnosis Model for Cancer Patients Based on Federated Learning

Zhang

et al. 2022

Front. Oncol.

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Since the 20th century, cancer has been a growing threat to human health. Cancer is a malignant tumor with high clinical morbidity and mortality, and there is a high risk of recurrence after surgery. At the same time, the diagnosis of whether the cancer is in situ recurrence is crucial for further treatment of cancer patients. According to statistics, about 90% of cancer-related deaths are due to metastasis of primary tumor cells. Therefore, the study of the location of cancer recurrence and its influencing factors is of great significance for the clinical diagnosis and treatment of cancer. In this paper, we propose an assisted diagnosis model for cancer patients based on federated learning. In terms of data, the influencing factors of cancer recurrence and the special needs of data samples required by federated learning were comprehensively considered. Six first-level impact indicators were determined, and the historical case data of cancer patients were further collected. Based on the federated learning framework combined with convolutional neural network, various physical examination indicators of patients were taken as input. The recurrence time and recurrence location of patients were used as output to construct an auxiliary diagnostic model, and linear regression, support vector regression, Bayesling regression, gradient ascending tree and multilayer perceptrons neural network algorithm were used as comparison algorithms. CNN’s federated prediction model based on improved under the condition of the joint modeling and simulation on the five types of cancer data accuracy reached more than 90%, the accuracy is better than single modeling machine learning tree model and linear model and neural network, the results show that auxiliary diagnosis model based on the study of cancer patients in assisted the doctor in the diagnosis of patients, As well as effectively provide nutritional programs for patients and have application value in prolonging the life of patients, it has certain guiding significance in the field of medical cancer rehabilitation.

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“…The topic of cancer recovery can be traced back to 1971 when the United States first proposed the concept of cancer recovery in the National Cancer Plan [5]. Cenik F defines cancer rehabilitation as "helping cancer patients maximize their physical, social, psychological and professional functions under the limitations of cancer itself and cancer treatment" [6]. The main purpose of cancer rehabilitation is to restore the patient's psychological, physical, and physiological functions well.…”

Section: Introductionmentioning

confidence: 99%

Design on Early Warning System for Renal Cancer Recurrence Based on CNN-Based Internet of Things

et al. 2022

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Kidney cancer is a tumor of the urinary system. Its incidence is second only to bladder tumors. In recent years, the incidence of kidney cancer has shown an overall upward trend, but the early judgment of kidney cancer is still in the stage of imaging discovery. Early detection and early treatment cannot be achieved. Based on the natural advantages of the Internet of Things in the medical field, this paper focuses on the intelligent early-warning model of renal cancer recurrence and builds a renal cancer early-warning system integrated with the Internet of Things. We integrated the influencing factors of renal cell carcinoma and constructed the final sample set. We conducted data analysis and optimized the data set. Aiming at the instability of renal cancer recurrence, five supervised learning prediction algorithms, including multiple linear regression, Bayesian ridge regression, gradient boosting tree, support vector regression, and convolutional neural network, are used to develop a renal cancer recurrence prediction model, compare and discuss the predictive performance of five algorithms. Finally, we established the best renal cancer recurrence prediction model based on the convolutional neural network, and combined with the Internet of Things medical framework to design an intelligent early warning system for renal cancer recurrence, so as to predict the recurrence time of renal cancer patients. And give warning prompts according to its status. The recurrence early warning system can assist doctors in making preliminary judgments on the patient's condition, and has a certain auxiliary effect on the clinical diagnosis and treatment of cancer and kidney cancer. INDEX TERMSKidney cancer; Recurrence time; Recurrence warning system; Convolutional neural network.

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Role of physical medicine for cancer rehabilitation and return to work under the premise of the “Wiedereingliederungsteilzeitgesetz”

Cited by 12 publications

References 30 publications

Radiotherapy-Induced Fatigue in Breast Cancer Patients

Radiotherapy-Induced Fatigue in Breast Cancer Patients

An Assisted Diagnosis Model for Cancer Patients Based on Federated Learning

Design on Early Warning System for Renal Cancer Recurrence Based on CNN-Based Internet of Things

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