A PREDICTIVE INDEX OF INTRA-DIALYSIS IDH A STATISTICAL CLINICAL DATA MINING APPROACH
Published In: 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN INFORMATION PROCESSING AND COMMUNICATION TECHNOLOGY
Author(s): CAMILLA BIANCHI , CARLO SCHOENHOLZER , CLAUDIO MINORETTI , GIULIA CAPPOLI , GIUSEPPE PONTORIERO , GIUSEPPE ROMBOLA , GIUSTINA CASAGRANDE , MARIA LAURA COSTANTINO
Abstract: Intra-Dialysis Hypotension (IDH) is one of the main hemodialysis related complications, occurring in 25-30% of the sessions. The factors involved in the onset of hypotension in patients undergoing dialysis are due both to clinical conditions (e.g. presence of vascular or cardiac diseases, neuropathology, anemia) and treatment settings such as temperature of the dialysate, sodium concentration, buffer composition, ultrafiltration rate, etc. The patient’s peculiar reaction to the treatment implies difficulties in preventing IDH episodes. This work explores the possibility to use a multivariate analysis of clinical data to quantify the risk to develop IDH at the beginning of each session. The study is framed in the DialysIS project (Dialysis therapy between Italy and Switzerland) funded by INTERREG – Italy – Switzerland and Co-funded by European Union. Data referring to a total of 516 sessions performed on 70 adult patients undergoing dialysis treatment (50 patients enrolled at A. Manzoni
- Publication Date: 19-Apr-2015
- DOI: 10.15224/978-1-63248-044-6-76
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IMAGE DE-NOISING METHOD BASED ON WAVELET FUNCTION LEARNING FOR MEDICAL IMAGE
Published In: 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN INFORMATION PROCESSING AND COMMUNICATION TECHNOLOGY
Author(s): SANGHUN YUN , WON-SEOK KANG
Abstract: Medical imaging is playing the key role in diagnosing and treatment of diseases. For making accurate decisions, the images acquired by various medical imaging modalities must be free from noise. So image de-noising became an important pre-processing step in Medical image analysis. In this paper, we propose a new de-noising method for medical images. Our method divides up the medical image into multiwindows and assigns the optimal mother wavelet function to each windows. And we are using an n-gram based wavelet learning technique in order to investigate optimal wavelet sequences for an image de-noising. The wavelet learning approach uses Mean Square Error (MSE) as a feature to generate an n-gram table. The performance of the proposed method is compared with the existing methods using Peak Signal to Noise Ratio (PSNR). The results showed that the proposed method has a better PSNR than the previous methods
- Publication Date: 19-Apr-2015
- DOI: 10.15224/978-1-63248-044-6-77
- Views: 0
- Downloads: 0