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10.1186/s12911-016-0360-x http://scihub22266oqcxt.onion/10.1186/s12911-016-0360-x C5034569!5034569!27658851     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Med+Inform+Decis+Mak 2016 ; 16 (ä): ä Nephropedia Template TP {{tp|p=27658851|t=2016. Identification of disease states associated with coagulopathy in trauma |pdf=|usr=}}{{27658851}} gab.com Text Identification of disease states associated with coagulopathy in trauma JO: BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=27658851 #FOAvip Background: Trauma is the leading cause of death between the ages of 1 to 44 in the United States. Blood loss is the primary cause of these deaths. The discrimination of states through which patients transition would be helpful in understanding the disease process, and in identification of critical states and appropriate interventions. Even though these states are strongly associated with patients? blood composition data, there has not been a way to directly identify them. Statistical tools such as hidden Markov models can be used to infer the discrete latent states from the blood composition data. Methods: We applied a hidden Markov model to time-series multivariate patient measurements from the UCSF/ San Francisco General Hospital and Trauma Center. Ten blood factor related measurements were used to identify the model: factors II, V, VII, VIII, IX, X, antithrombin III, protein C, prothrombin time and partial thromboplastin time. Missing data in the time-series dataset was considered in the hidden Markov model. The number of states was determined by minimizing the Bayesian information criterion across different numbers of states. Results: After preprocessing, 1090 patients with a total number of 2176 time point measurements were included in the analysis. The hidden Markov model identified 6 disease states and 3 stages. We analyzed their relationships to the blood composition data and the coagulation cascade. The states are very indicative of the disease progression status of patients. Conclusions: Six disease states and 3 stages associated with Coagulopathy in trauma were identified in our study. The hidden Markov model can be useful in identifying latent states by using patients? time-series multivariate data. The information obtained from the states and stages can be useful in the clinical setting. Electronic supplementary material: The online version of this article (doi:10.1186/s12911-016-0360-x) contains supplementary material, which is available to authorized users. Twit Text FOAVip Identification of disease states associated with coagulopathy in trauma ????BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=27658851 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27658851 #FOAvip English Wikipedia <ref>{{Cite pmid|27658851}}</ref> Identification of disease states associated with coagulopathy in trauma #MMPMID27658851 Zhang Y; Wu TB; Daigle BJ; Cohen M; Petzold L BMC Med Inform Decis Mak 2016[]; 16 (ä): ä PMID27658851show ga Background: Trauma is the leading cause of death between the ages of 1 to 44 in the United States. Blood loss is the primary cause of these deaths. The discrimination of states through which patients transition would be helpful in understanding the disease process, and in identification of critical states and appropriate interventions. Even though these states are strongly associated with patients? blood composition data, there has not been a way to directly identify them. Statistical tools such as hidden Markov models can be used to infer the discrete latent states from the blood composition data. Methods: We applied a hidden Markov model to time-series multivariate patient measurements from the UCSF/ San Francisco General Hospital and Trauma Center. Ten blood factor related measurements were used to identify the model: factors II, V, VII, VIII, IX, X, antithrombin III, protein C, prothrombin time and partial thromboplastin time. Missing data in the time-series dataset was considered in the hidden Markov model. The number of states was determined by minimizing the Bayesian information criterion across different numbers of states. Results: After preprocessing, 1090 patients with a total number of 2176 time point measurements were included in the analysis. The hidden Markov model identified 6 disease states and 3 stages. We analyzed their relationships to the blood composition data and the coagulation cascade. The states are very indicative of the disease progression status of patients. Conclusions: Six disease states and 3 stages associated with Coagulopathy in trauma were identified in our study. The hidden Markov model can be useful in identifying latent states by using patients? time-series multivariate data. The information obtained from the states and stages can be useful in the clinical setting. Electronic supplementary material: The online version of this article (doi:10.1186/s12911-016-0360-x) contains supplementary material, which is available to authorized users. äIdentification of disease states associated with coagulopathy in traum(epmc).pdf DeepDyve Pubget Overpricing