Background: In the intensive care unit (ICU), clinical staff must stay vigilant to promptly detect and treat hypotensive episodes (HEs). Given the stressful context of busy ICUs, an automated hypotensive risk stratifi...
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Background: In the intensive care unit (ICU), clinical staff must stay vigilant to promptly detect and treat hypotensive episodes (HEs). Given the stressful context of busy ICUs, an automated hypotensive risk stratifier can help ICU clinicians focus care and resources by prospectively identifying patients at increased risk of impending HEs. The objective of this study was to investigate the possible existence of discriminatory patterns in hemodynamic data that can be indicative of future hypotensive risk. Methods: Given the complexity and heterogeneity of ICU data, a machine learning approach was used in this study. Time series of minute-by-minute measures of mean arterial blood pressure, heart rate, pulse pressure, and relative cardiac output from 1,311 records from the MIMIC II Database were used. An HE was defined as a 30-minute period during which the mean arterial pressure was below 60 mmHg for at least 90% of the time. Features extracted from the hemodynamic data during an observation period of either 30 or 60 minutes were analyzed to predict the occurrence of HEs 1 or 2 hours into the future. Artificial neural networks (ANNs) were trained for binary classification (normotensive vs. hypotensive) and regression (estimation of future mean blood pressure). Results: The ANNs were successfully trained to discriminate patterns in the multidimensional hemodynamic data that were predictive of future HEs. The best overall binary classification performance resulted in a mean area under ROC curve of 0.918, a sensitivity of 0.826, and a specificity of 0.859. Predicting further into the future resulted in poorer performance, whereas observation duration minimally affected performance. The low prevalence of HEs led to poor positive predictive values. In regression, the best mean absolute error was 9.67%. Conclusions: The promising patternrecognition performance demonstrates the existence of discriminatory patterns in hemodynamic data that can indicate impending hypotension
Background: Conflicting data exist regarding the prognostic and predictive impact of survivin (BIRC5) in breast cancer. We previously reported survivin cytoplasmic-to-nuclear ratio (CNR) as an independent prognostic i...
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Background: Conflicting data exist regarding the prognostic and predictive impact of survivin (BIRC5) in breast cancer. We previously reported survivin cytoplasmic-to-nuclear ratio (CNR) as an independent prognostic indicator in breast cancer. Here, we validate survivin CNR in a separate and extended cohort. Furthermore, we present new data suggesting that a low CNR may predict outcome in tamoxifen-treated patients. Methods: Survin expression was assessed using immunhistochemistry on a breast cancer tissue microarray (TMA) containing 512 tumours. Whole slide digital images were captured using an Aperio XT scanner. Automated image analysis was used to identify tumour from stroma and then to quantify tumour-specific nuclear and cytoplasmic survivin. A decision tree model selected using a 10-fold cross-validation approach was used to identify prognostic subgroups based on nuclear and cytoplasmic survivin expression. Results: Following optimisation of the staining procedure, it was possible to evaluate survivin protein expression in 70.1% (n = 359) of the 512 tumours represented on the TMA. Decision tree analysis predicted that nuclear, as opposed to cytoplasmic, survivin was the most important determinant of overall survival (OS) and breast cancer-specific survival (BCSS). The decision tree model confirmed CNR of 5 as the optimum threshold for survival analysis. Univariate analysis demonstrated an association between a high CNR (>5) and a prolonged BCSS (HR 0.49, 95% CI 0.29-0.81, p = 0.006). Multivariate analysis revealed a high CNR (>5) was an independent predictor of BCSS (HR 0.47, 95% CI 0.27-0.82, p = 0.008). An increased CNR was associated with ER positive (p = 0.045), low grade (p = 0.007), Ki-67 (p = 0.001) and Her2 (p = 0.026) negative tumours. Finally, a high CNR was an independent predictor of OS in tamoxifen-treated ER-positive patients (HR 0.44, 95% CI 0.23-0.87, p = 0.018). Conclusion: Using the same threshold as our previous study, we have validated surv
Novel gas sensor devices have been developed using polythiophene (pTh) film and poly(3-n-dodecylthiophene) (pDpTh.) film coated over pTh film. These polymer films were electrochemically deposited and doped by cyclic v...
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Novel gas sensor devices have been developed using polythiophene (pTh) film and poly(3-n-dodecylthiophene) (pDpTh.) film coated over pTh film. These polymer films were electrochemically deposited and doped by cyclic voltammetry on thin-film electrodes where the isolation gap was formed by a micromachining process. We examined the response characteristics of the conducting polymer films against various sample gases over a range of temperatures of the sensitive layer. The resistance changes of both sensitive layers of pTh and pDpTh were highly dependent on the kind of layer. In particular, pTh film responded to ammonia gas and pDpTh films clearly responded to hydrophobic gases, such as chloroform, methane and ethanol. The response of these films to several gases was analyzed with a patternrecognition (PARC) algorithm. It was found that our simple gas sensor device could discriminate between the gases that were used here. (C) 2002 Elsevier Science B.V. All rights reserved.
Novel gas sensor devices have been developed using polythiophene (pTh) film and poly(3-n-dodecylthiophene) (pDpTh.) film coated over pTh film. These polymer films were electrochemically deposited and doped by cyclic v...
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Novel gas sensor devices have been developed using polythiophene (pTh) film and poly(3-n-dodecylthiophene) (pDpTh.) film coated over pTh film. These polymer films were electrochemically deposited and doped by cyclic voltammetry on thin-film electrodes where the isolation gap was formed by a micromachining process. We examined the response characteristics of the conducting polymer films against various sample gases over a range of temperatures of the sensitive layer. The resistance changes of both sensitive layers of pTh and pDpTh were highly dependent on the kind of layer. In particular, pTh film responded to ammonia gas and pDpTh films clearly responded to hydrophobic gases, such as chloroform, methane and ethanol. The response of these films to several gases was analyzed with a patternrecognition (PARC) algorithm. It was found that our simple gas sensor device could discriminate between the gases that were used here. (C) 2002 Elsevier Science B.V. All rights reserved.
Background: Laser Induced Breakdown Spectroscopy (LIBS) can be used to measure trace element concentrations in solids, liquids and gases, with spatial resolution and absolute quantifaction being feasible, down to part...
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Background: Laser Induced Breakdown Spectroscopy (LIBS) can be used to measure trace element concentrations in solids, liquids and gases, with spatial resolution and absolute quantifaction being feasible, down to parts-per-million concentration levels. Some applications of LIBS do not necessarily require exact, quantitative measurements. These include applications in dentistry, which are of a more "identify-and-sort" nature - e.g. identification of teeth affected by caries. Methods: A one-fibre light delivery / collection assembly for LIBS analysis was used, which in principle lends itself for routine in vitro / in vivo applications in a dental practice. A number of evaluation algorithms for LIBS data can be used to assess the similarity of a spectrum, measured at specific sample locations, with a training set of reference spectra. Here, the description has been restricted to one pattern recognition algorithm, namely the so-called Mahalanobis Distance method. Results: The plasma created when the laser pulse ablates the sample (in vitro / in vivo), was spectrally analysed. We demonstrated that, using the Mahalanobis Distance pattern recognition algorithm, we could unambiguously determine the identity of an "unknown" tooth sample in real time. Based on single spectra obtained from the sample, the transition from caries-affected to healthy tooth material could be distinguished, with high spatial resolution. Conclusions: The combination of LIBS and pattern recognition algorithms provides a potentially useful tool for dentists for fast material identification problems, such as for example the precise control of the laser drilling / cleaning process.
Seismicity of the Himalayan arc lying within the limits shown in figure 1 and covering the period 1964 to 1987 was scanned using M8 algorithm with a view to identifying the times of increased probabilities (TIPs) of t...
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Seismicity of the Himalayan arc lying within the limits shown in figure 1 and covering the period 1964 to 1987 was scanned using M8 algorithm with a view to identifying the times of increased probabilities (TIPs) of the occurrence of earthquakes of magnitude greater than or equal to 7·0, during the period 1970 to 1987. In this period, TIPs occupy 18% of the space time considered. One of these precedes the only earthquake in this magnitude range which occurred during the period. Two numerical parameters used in the algorithm, namely the magnitude thresholds, had to be altered for the present study owing to incomplete data. Further monitoring of TIPs is however warranted, both for testing the predictive capability of this algorithm in the Himalayan region and for creating a base for the search of short-term precursors.
In this paper a new concept of Floating Approximation is introduced. It is based on Pawlaks theory of Rough Sets and the existence of ‘hidden attributes’ in knowledge representation systems. After explaining the mot...
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In this paper a new concept of Floating Approximation is introduced. It is based on Pawlaks theory of Rough Sets and the existence of ‘hidden attributes’ in knowledge representation systems. After explaining the motivation, a simplified algorithm developed and implemented by the author is described. First experience is reported and some ideas for further research are suggested.
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