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Related Concept Videos

Chronic Kidney Disease III: Interprofessional Care01:28

Chronic Kidney Disease III: Interprofessional Care

50
Chronic kidney disease (CKD) requires collaborative and comprehensive management. CKD progresses through stages and can lead to end-stage kidney disease (ESKD) if untreated. Interprofessional collaboration and patient education are crucial, enabling patients to manage their health and improve their quality of life.Diagnostic approach for chronic kidney diseaseThe diagnosis of CKD primarily focuses on the glomerular filtration rate (GFR), which assesses kidney function by measuring how well...
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Chronic Kidney Disease I: Introduction01:25

Chronic Kidney Disease I: Introduction

43
Chronic Kidney Disease (CKD) arises when the kidneys progressively lose their ability to function, ultimately leading to end-stage kidney disease (ESKD). At this advanced stage, the kidneys can no longer filter waste or maintain essential body functions, requiring renal replacement therapy (RRT) through dialysis or a kidney transplant for survival.Early-stage chronic kidney disease and detection challengesIn CKD's early stages, symptoms often remain absent because healthy nephrons compensate...
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Acute Kidney Injury V: Interprofessional Care01:20

Acute Kidney Injury V: Interprofessional Care

30
Acute Kidney Injury (AKI) requires a collaborative healthcare approach to restore renal function and prevent complications. Essential management strategies involve monitoring fluid and electrolyte balance, adjusting medications, initiating dialysis when necessary, and providing nutritional support.Fluid and Electrolyte ManagementFluid Monitoring: Regularly monitoring body weight, central venous pressure, and urine output helps detect fluid imbalances early. Patient intake and output are...
30
Dialysis01:27

Dialysis

390
Renal failure occurs when the kidneys lose their ability to filter waste products from the blood effectively. It can be classified into two types: acute renal failure (ARF) and chronic renal failure (CRF).
Acute kidney injury develops suddenly and can be caused by pre-renal causes (e.g., hypovolemia, shock), intrinsic renal causes (e.g., acute tubular necrosis), or post-renal causes (e.g., urinary obstruction). In contrast, chronic renal failure progresses gradually over time and is often...
390
Chronic Kidney Disease IV: Nursing Management01:18

Chronic Kidney Disease IV: Nursing Management

29
Nursing management is essential for preventing complications, maintaining stability, and improving patients' quality of life in chronic kidney disease (CKD). By using a structured approach, nurses help slow CKD progression and support effective patient care​.1. Comprehensive patient assessmentEffective management begins with nurses reviewing the patient’s medical history, and identifying key risk factors like diabetes, hypertension, and nephrotoxic drug use. Nurses assess signs of...
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Kidney Transplant I: Introduction01:28

Kidney Transplant I: Introduction

29
A kidney transplant is a surgical approach that involves replacing a non-functioning kidney with a healthy one from a donor. This procedure is often a treatment option for end-stage renal disease (ESRD) patients. The method requires careful recipient selection, including evaluating various medical and psychosocial factors. These criteria vary between transplant centers but generally include assessments of the patient's overall health, adherence to medical recommendations, and lifestyle...
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Standard-based personalized healthcare delivery for kidney illness using deep learning.

Shelly Sachdeva1

  • 1Department of Computer Science & Engineering, NIT Delhi, New Delhi, India.

Physiological Measurement
|June 21, 2023
PubMed
Summary

Deep learning models accurately diagnose kidney diseases using Electronic Health Records. This predictive analytics approach enhances patient care and aids medical professionals in treatment decisions.

Keywords:
deep learningelectronic health recordshealth informaticsstandardized EHR system

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Area of Science:

  • Medical Informatics
  • Machine Learning
  • Artificial Intelligence

Background:

  • Predictive analytics in healthcare uses patient data for improved services and treatment planning.
  • Deep learning, a subset of machine learning, utilizes deep artificial neural networks for pattern recognition, significantly improving predictive accuracy over traditional models.

Purpose of the Study:

  • To analyze the impact of deep learning on a standardized Electronic Health Records dataset for diagnosing kidney-related diseases.
  • To evaluate a novel deep learning architecture for its effectiveness in a real-world healthcare scenario.

Main Methods:

  • Employed a modularized deep learning architecture, Encoder-Combiner-Decoder (ECD), known for its robust framework and adaptability.
  • Trained the ECD model using the openEHR Benchmark Dataset (ORBDA), a real-world dataset from the Brazilian Public Health System (DATASUS).

Main Results:

  • The deep learning model demonstrated high precision, recall, and F1 scores in identifying kidney-related diseases from the ORBDA dataset.
  • The model's performance indicates a strong capability for accurate and consistent diagnosis of kidney conditions.

Conclusions:

  • The developed deep learning model offers a novel approach to analyzing standardized healthcare data for disease diagnosis.
  • This model shows potential for deployment in medical institutions, enabling medical professionals to assess its clinical utility and performance.