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Poor adherence to medical regimen causes approximately 33% to 69% of medication-related hospitalizations and accounts for $100 billion in annual health care costs. In this paper we address the problem of unintentional non adherence, when patient fails to take a medication due to forgetfulness or carelessness. We present the safe approach to software implementation of a portable reminder device with...
This paper proposes a parameterized Support Vector Machine (ParaSVM) approach for modeling the Drug Concentration to Time (DCT) curves. It combines the merits of Support Vector Machine (SVM) algorithm that considers various patient features and an analytical model that approximates the predicted DCT points and enables curve calibrations using occasional real Therapeutic Drug Monitoring (TDM) measurements...
The decision-making process regarding drug dose, regularly used in everyday medical practice, is critical to patients’ health and recovery. It is a challenging process, especially for a drug with narrow therapeutic ranges, in which a medical doctor decides the quantity (dose amount) and frequency (dose interval) on the basis of a set of available patient features and doctor’s clinical experience (a...
Drug delivery is one of the most common clinical routines in hospitals, and is critical to patients' health and recovery. It includes a decision making process in which a medical doctor decides the amount (dose) and frequency (dose interval) on the basis of a set of available patients' feature data and the doctor's clinical experience (a priori adaptation). This process can be computerized in order...
Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction...
This paper proposes a novel computational structure for Sum of Absolute Difference (SAD) in Fast Full Search (FFS) algorithm to ellipsis part of the SAD value dependency. This structure adopts an SAD Accumulating Termination Algorithm (SAD-ATA) and an Early Loop Termination Decision (ELTD). SAD-ATA is used to analyze and determine whether to skip the following operation of successive SAD accumulation...
In MPEG-2 to H.264 transcoding, MPEG-2 motion vector (MV) reuse is an effective technique to simplify motion estimation (ME) processing. The irregularity of MPEG-2 MV also brings difficulty in applying data reuse method for hardware design, which plays a critical role for bandwidth reduction. In this paper, two search window reuse methods are introduced for HDTV application. The Level C method utilizes...
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