Abstract
Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from performing COVID-19 diagnoses. We ...constructed a machine learning model for COVID-19 diagnosis that was based and cross-validated on the routine blood tests of 5333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected the operational ROC point at a sensitivity of 81.9% and a specificity of 97.9%. The cross-validated AUC was 0.97. The five most useful routine blood parameters for COVID-19 diagnosis according to the feature importance scoring of the XGBoost algorithm were: MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. t-SNE visualization showed that the blood parameters of the patients with a severe COVID-19 course are more like the parameters of a bacterial than a viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results represent a significant contribution to improvements in COVID-19 diagnosis.
Quick and accurate medical diagnoses are crucial for the successful treatment of diseases. Using machine learning algorithms and based on laboratory blood test results, we have built two models to ...predict a haematologic disease. One predictive model used all the available blood test parameters and the other used only a reduced set that is usually measured upon patient admittance. Both models produced good results, obtaining prediction accuracies of 0.88 and 0.86 when considering the list of five most likely diseases and 0.59 and 0.57 when considering only the most likely disease. The models did not differ significantly, which indicates that a reduced set of parameters can represent a relevant "fingerprint" of a disease. This knowledge expands the model's utility for use by general practitioners and indicates that blood test results contain more information than physicians generally recognize. A clinical test showed that the accuracy of our predictive models was on par with that of haematology specialists. Our study is the first to show that a machine learning predictive model based on blood tests alone can be successfully applied to predict haematologic diseases. This result and could open up unprecedented possibilities for medical diagnosis.
The growing threat of antibiotic resistance necessitates accurate differentiation between bacterial and viral infections for proper antibiotic administration. In this study, a Virus vs. Bacteria ...machine learning model was developed to distinguish between these infection types using 16 routine blood test results, C-reactive protein concentration (CRP), biological sex, and age. With a dataset of 44,120 cases from a single medical center, the model achieved an accuracy of 82.2 %, a sensitivity of 79.7 %, a specificity of 84.5 %, a Brier score of 0.129, and an area under the ROC curve (AUC) of 0.905, outperforming a CRP-based decision rule. Notably, the machine learning model enhanced accuracy within the CRP range of 10–40 mg/L, a range where CRP alone is less informative. These results highlight the advantage of integrating multiple blood parameters in diagnostics. The "Virus vs. Bacteria" model paves the way for advanced diagnostic tools, leveraging machine learning to optimize infection management.
The composition, distribution and the sources of polycyclic aromatic hydrocarbons (PAHs) in the surficial sediments of the Gulf of Trieste were investigated. To document the spatial PAH input, ...surficial sediment samples from 17 locations throughout the Gulf were analysed. The total PAH load determined in the surficial sediment samples are between 30 and 600 ng g
−1, and were the highest in the immediate vicinity of the Port of Trieste. The PAH contents decline rapidly with increasing distance from the shore. The ratios of methylphenanthrenes/phenanthrene and methylpyrene/pyrene are sensitive indicators of the origin of PAH pollution in the Gulf which is mostly pyrolitic. The phenanthrene/anthracene ratio was used to determine the approximate location and distance from the source of PAH pollution, while 1-methy l-7-isopropylphenanthrene (retene) was used as indicator for forest fires. A sediment depth profile indicates a major increase in the PAH concentrations after the First World War.
The growing threat of antibiotic resistance necessitates accurate differentiation between bacterial and viral infections for proper antibiotic administration. In this study, a Virus vs. Bacteria ...machine learning model was developed to distinguish between these infection types using 16 routine blood test results, C-reactive protein concentration (CRP), biological sex, and age. With a dataset of 44,120 cases from a single medical center, the model achieved an accuracy of 82.2 %, a sensitivity of 79.7 %, a specificity of 84.5 %, a Brier score of 0.129, and an area under the ROC curve (AUC) of 0.905, outperforming a CRP-based decision rule. Notably, the machine learning model enhanced accuracy within the CRP range of 10-40 mg/L, a range where CRP alone is less informative. These results highlight the advantage of integrating multiple blood parameters in diagnostics. The "Virus vs. Bacteria" model paves the way for advanced diagnostic tools, leveraging machine learning to optimize infection management.
Physicians taking care of patients with coronavirus disease (COVID-19) have described different changes in routine blood parameters. However, these changes, hinder them from performing COVID-19 ...diagnosis. We constructed a machine learning predictive model for COVID-19 diagnosis. The model was based and cross-validated on the routine blood tests of 5,333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected operational ROC point at a sensitivity of 81.9% and specificity of 97.9%. The cross-validated area under the curve (AUC) was 0.97. The five most useful routine blood parameters for COVID19 diagnosis according to the feature importance scoring of the XGBoost algorithm were MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. tSNE visualization showed that the blood parameters of the patients with severe COVID-19 course are more like the parameters of bacterial than viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results present a significant contribution to improvements in COVID-19 diagnosis.
Quick and accurate medical diagnosis is crucial for the successful treatment of a disease. Using machine learning algorithms, we have built two models to predict a hematologic disease, based on ...laboratory blood test results. In one predictive model, we used all available blood test parameters and in the other a reduced set, which is usually measured upon patient admittance. Both models produced good results, with a prediction accuracy of 0.88 and 0.86, when considering the list of five most probable diseases, and 0.59 and 0.57, when considering only the most probable disease. Models did not differ significantly from each other, which indicates that a reduced set of parameters contains a relevant fingerprint of a disease, expanding the utility of the model for general practitioner's use and indicating that there is more information in the blood test results than physicians recognize. In the clinical test we showed that the accuracy of our predictive models was on a par with the ability of hematology specialists. Our study is the first to show that a machine learning predictive model based on blood tests alone, can be successfully applied to predict hematologic diseases and could open up unprecedented possibilities in medical diagnosis.
In order to determine PAHs in marine sediment samples by GC/MS(SIM) a new extraction approach of ASE-SFE was evaluated using combined accelerated solvent extraction (ASE, dynamic and static mode) and ...supercritical fluid extraction (SFE, dynamic mode) without further purification of the sample. The solvents used for ASE-SFE were methylene chloride and carbon dioxide. The recovery data, precision and accuracy of the whole method were evaluated statistically. The average recoveries of PAHs, based on deuterated internal standards were 77% for 2-3-ring PAHs, 85% for 4-ring PAHs, 88% for 5-ring PAHs and 97% for 6-ring PAHs. The extraction time required for the ASE-SFE technique was 30 min, which is longer than in the case of independent use of ASE and shorter compared to SFE. ASE-SFE recoveries of PAHs from SRM marine sediment are comparable for (2-3-ring, 4-ring PAHs) or higher (5-ring, 6-ring PAHs) than reported for the conventional extraction methods of ASE and SFE. Method detection limits of (MDL) were statistically estimated. MDL values obtained for 15 PAHs compounds vary between 0.06 ngg(-1) and 3.54 ngg(-1).
Vehicles Lipset, David; Handler, Richard
2014., 20140815, 2014
eBook
Metaphor, as an act of human fancy, combines ideas in improbable ways to sharpen meanings of life and experience. Theoretically, this arises from an association between a sign-for example, a cattle ...car-and its referent, the Holocaust. These "sign-vehicles" serve as modes of semiotic transportation through conceptual space. Likewise, on-the-ground vehicles can be rich metaphors for the moral imagination. Following on this insight,Vehiclespresents a collection of ethnographic essays on the metaphoric significance of vehicles in different cultures. Analyses include canoes in Papua New Guinea, pedestrians and airplanes in North America, lowriders among Mexican-Americans, and cars in contemporary China, Japan, and Eastern Europe, as well as among African-Americans in the South. Vehicles not only "carry people around," but also "carry" how they are understood in relation to the dynamics of culture, politics and history.
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