The Experts below are selected from a list of 48 Experts worldwide ranked by ideXlab platform

Nwamaka D Eneanya - One of the best experts on this subject based on the ideXlab platform.

  • an interdisciplinary approach to dialysis decision making in the ckd Patient with Depression
    Advances in Chronic Kidney Disease, 2014
    Co-Authors: Jane O Schell, Renee Bovacollis, Nwamaka D Eneanya
    Abstract:

    Depression and depressive symptoms are common in advanced kidney disease and are associated with poor outcomes. For those with CKD not on dialysis, Depression may influence how Patients cope and prepare for their disease and its management, including decisions about dialysis treatment. Patient self-reported scales exist to better identify Depression; how to incorporate these scales into clinical practice and assist with treatment decision-making is less clear. We present a case-based discussion of depressive symptoms in Patients with advanced kidney disease not on dialysis. We highlight the contribution of underlying somatic and psychosocial factors in the assessment and management of Depression. We further define the role of the interdisciplinary care team, including palliative care and hospice medicine, to assist with symptom management and end-of-life care for CKD Patients with Depression.

Jane O Schell - One of the best experts on this subject based on the ideXlab platform.

  • an interdisciplinary approach to dialysis decision making in the ckd Patient with Depression
    Advances in Chronic Kidney Disease, 2014
    Co-Authors: Jane O Schell, Renee Bovacollis, Nwamaka D Eneanya
    Abstract:

    Depression and depressive symptoms are common in advanced kidney disease and are associated with poor outcomes. For those with CKD not on dialysis, Depression may influence how Patients cope and prepare for their disease and its management, including decisions about dialysis treatment. Patient self-reported scales exist to better identify Depression; how to incorporate these scales into clinical practice and assist with treatment decision-making is less clear. We present a case-based discussion of depressive symptoms in Patients with advanced kidney disease not on dialysis. We highlight the contribution of underlying somatic and psychosocial factors in the assessment and management of Depression. We further define the role of the interdisciplinary care team, including palliative care and hospice medicine, to assist with symptom management and end-of-life care for CKD Patients with Depression.

Renee Bovacollis - One of the best experts on this subject based on the ideXlab platform.

  • an interdisciplinary approach to dialysis decision making in the ckd Patient with Depression
    Advances in Chronic Kidney Disease, 2014
    Co-Authors: Jane O Schell, Renee Bovacollis, Nwamaka D Eneanya
    Abstract:

    Depression and depressive symptoms are common in advanced kidney disease and are associated with poor outcomes. For those with CKD not on dialysis, Depression may influence how Patients cope and prepare for their disease and its management, including decisions about dialysis treatment. Patient self-reported scales exist to better identify Depression; how to incorporate these scales into clinical practice and assist with treatment decision-making is less clear. We present a case-based discussion of depressive symptoms in Patients with advanced kidney disease not on dialysis. We highlight the contribution of underlying somatic and psychosocial factors in the assessment and management of Depression. We further define the role of the interdisciplinary care team, including palliative care and hospice medicine, to assist with symptom management and end-of-life care for CKD Patients with Depression.

Junmei Wu - One of the best experts on this subject based on the ideXlab platform.

  • Task-State Heart Rate Variability Parameter- Based Depression Detection Model and Effect of Therapy on the Parameters
    IEEE Access, 2019
    Co-Authors: Yaowen Xing, Mengmeng Miao, Quanchi Li, Qian Li, Xiaoyan Chen, Quan Zhang, Junmei Wu
    Abstract:

    Depression is a common psychiatric disease. At present, psychometric scales are the main methods for detecting Depression in Patients and evaluating the clinical treatment effect of Depression. However, the accuracy of the scales is influenced by the subjective factors of Patients and doctors. This paper explored the construction of a Depression detection model based on task-state heart rate variability (HRV) parameters and the effect of therapy on the related HRV parameters. The candidate HRV parameters were first extracted from the task-state electrocardiogram (ECG) collected before treatment and at three observation points during treatment. Then, a statistical t-test was used to screen those characteristic HRV parameters with a significant difference between the depressed and normal groups before treatment. The characteristic HRV parameters and a support-vector machine (SVM) were combined to construct the detection model. Finally, a score model was designed to reveal dynamic changes in the HRV parameters during the treatment process. This paper constructed an automatic, simple, and efficient Depression detection model: peakHF+SVM. Detection accuracy reached 89.66%, and this model had comprehensive advantages compared with other related methods. During the entire treatment process, the change in the scores and the time to achieve the maximal scores were different among Patients. The type and number of HRV parameters related to the maximal score of each Patient also were different. The Depression detection model has good application prospects in the objective, quantitative, and automatic detection of Depression. The same curative method produced different effects on each Patient with Depression. The proposed score model may be helpful for the quantitative assessment of the therapeutic effect.

Yaowen Xing - One of the best experts on this subject based on the ideXlab platform.

  • Task-State Heart Rate Variability Parameter- Based Depression Detection Model and Effect of Therapy on the Parameters
    IEEE Access, 2019
    Co-Authors: Yaowen Xing, Mengmeng Miao, Quanchi Li, Qian Li, Xiaoyan Chen, Quan Zhang, Junmei Wu
    Abstract:

    Depression is a common psychiatric disease. At present, psychometric scales are the main methods for detecting Depression in Patients and evaluating the clinical treatment effect of Depression. However, the accuracy of the scales is influenced by the subjective factors of Patients and doctors. This paper explored the construction of a Depression detection model based on task-state heart rate variability (HRV) parameters and the effect of therapy on the related HRV parameters. The candidate HRV parameters were first extracted from the task-state electrocardiogram (ECG) collected before treatment and at three observation points during treatment. Then, a statistical t-test was used to screen those characteristic HRV parameters with a significant difference between the depressed and normal groups before treatment. The characteristic HRV parameters and a support-vector machine (SVM) were combined to construct the detection model. Finally, a score model was designed to reveal dynamic changes in the HRV parameters during the treatment process. This paper constructed an automatic, simple, and efficient Depression detection model: peakHF+SVM. Detection accuracy reached 89.66%, and this model had comprehensive advantages compared with other related methods. During the entire treatment process, the change in the scores and the time to achieve the maximal scores were different among Patients. The type and number of HRV parameters related to the maximal score of each Patient also were different. The Depression detection model has good application prospects in the objective, quantitative, and automatic detection of Depression. The same curative method produced different effects on each Patient with Depression. The proposed score model may be helpful for the quantitative assessment of the therapeutic effect.