Correlation Engine 2.0
Clear Search sequence regions

  • asian (2)
  • blood (2)
  • data analysis (1)
  • dialysis (3)
  • dry weight (9)
  • forest (4)
  • hemodialysis (4)
  • humans (1)
  • hypoalbuminemia (1)
  • japan (2)
  • levels protein (1)
  • patients (5)
  • random (4)
  • renal dialysis (1)
  • serum (1)
  • weight (2)
  • Sizes of these terms reflect their relevance to your search.

    Machine Learning has been increasingly used in the medical field, including managing patients undergoing hemodialysis. The random forest classifier is a Machine Learning method that can generate high accuracy and interpretability in the data analysis of various diseases. We attempted to apply Machine Learning to adjust dry weight, the appropriate volume status of patients undergoing hemodialysis, which requires a complex decision-making process considering multiple indicators and the patient's physical conditions. All medical data and 69,375 dialysis records of 314 Asian patients undergoing hemodialysis at a single dialysis center in Japan between July 2018 and April 2020 were collected from the electronic medical record system. Using the random forest classifier, we developed models to predict the probabilities of adjusting the dry weight at each dialysis session. The areas under the receiver-operating-characteristic curves of the models for adjusting the dry weight upward and downward were 0.70 and 0.74, respectively. The average probability of upward adjustment of the dry weight had sharp a peak around the actual change over time, while the average probability of downward adjustment of the dry weight formed a gradual peak. Feature importance analysis revealed that median blood pressure decline was a strong predictor for adjusting the dry weight upward. In contrast, elevated serum levels of C-reactive protein and hypoalbuminemia were important indicators for adjusting the dry weight downward. The random forest classifier should provide a helpful guide to predict the optimal changes to the dry weight with relative accuracy and may be useful in clinical practice. © 2023. The Author(s).


    Hiroko Inoue, Megumi Oya, Masashi Aizawa, Kyogo Wagatsuma, Masatomo Kamimae, Yusuke Kashiwagi, Masayoshi Ishii, Hanae Wakabayashi, Takayuki Fujii, Satoshi Suzuki, Noriyuki Hattori, Narihito Tatsumoto, Eiryo Kawakami, Katsuhiko Asanuma. Predicting dry weight change in Hemodialysis patients using machine learning. BMC nephrology. 2023 Jun 29;24(1):196

    Expand section icon Mesh Tags

    PMID: 37386392

    View Full Text