Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications

Día Virtual Uso de la herramienta\ua0Webex de CUDI

Además de conocer el modo de utilidad de la herramienta Webex cada uno de los participantes en esta actividad tuvo la oportunidad de interactuar con los instructores mediante una capacitación utilizando la misma herramienta

Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications

Fission dynamics at high excitation energies investigated in complete kinematics measurements

Light-charged particles emitted in proton-induced fission reactions on 208 Pb have been measured at different kinetic energies: 370A, 500A, and 650A MeV. The experiment was performed by the SOFIA collaboration at the GSI facilities in Darmstadt (Germany). The inverse kinematics technique was combined with a setup especially designed to measure light-charged particles in coincidence with fission fragments. The data were compared with different model calculations to assess the ground-to-saddle dynamics. The results confirm that transient and dissipative effects are required for an accurate description of the fission observables

Pre-and post-puberty physiological plasma oxytocin concentrations in male domestic cats (Felis silvestris catus)

The hormone oxytocin is released by the neuropituitary gland through stimulation of the neurons of the supraoptic and paraventricular nuclei of the hypothalamus. In order to determine the physiological concentrations of this hormone in domestic cats, blood samples were collected from 15 male animals (Felis silvestris catus) during the pre- and post-puberty periods (at four and eight months of age, respectively). Oxytocin determination was accomplished by radioimmunoassay. The average oxytocin concentrations measured in the pre- and post-puberty periods were 2.54±0.24 (μg/dL) and 2.53±0.28 (μg/dL), respectively, and there were no statistical differences between these measurements. Because there are few literature on the analysis of this hormone, especially in the case of male Felis silvestris catus, more studies on the influence of oxytocin on the physiology and reproduction of this species should be conducted under maintenance and situations of stress (such as transportation), and other routine events