Understanding Arteriosclerotic Heart Disease Patients Using Electronic Health Records: A Machine Learning and Shapley Additive exPlanations Approach.

OBJECTIVES: The number of deaths from cardiovascular disease is projected to reach 23.3 million by 2030. As a contribution to preventing this phenomenon, this paper proposed a machine learning (ML) model to predict patients with arteriosclerotic heart disease (AHD). We also interpreted the prediction model results based on the ML approach and deployed modelagnostic ML methods to identify informative features and their interpretations. METHODS: We used a hematology Electronic Health Record (EHR) with information on erythrocytes, hematocrit, hemoglobin, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, leukocytes, thrombocytes, age, and sex. To detect and predict AHD, we explored random forest (RF), XGBoost, and AdaBoost models. We examined the prediction model results based on the confusion matrix and accuracy measures. We used the Shapley Additive exPlanations (SHAP) framework to interpret the ML model and quantify the contribution of features to predictions. RESULTS: Our study included data from 6,837 patients, with 4,702 records from patients diagnosed with AHD and 2,135 records from patients without an AHD diagnosis. AdaBoost outperformed RF and XGBoost, achieving an accuracy of 0.78, precision of 0.82, F1-score of 0.85, and recall of 0.88. According to the SHAP summary bar plot method, hemoglobin was the most important attribute for detecting and predicting AHD patients. The SHAP local interpretability bar plot revealed that hemoglobin and mean corpuscular hemoglobin concentration had positive impacts on AHD prediction based on a single observation. CONCLUSIONS: ML models based on real clinical data can be used to predict AHD.

Anisotropic Surface Broadening and Core Depletion during the Evolution of a Strong-Field Induced Nanoplasma.

Strong-field ionization of nanoscale clusters provides excellent opportunities to study the complex correlated electronic and nuclear dynamics of near-solid density plasmas. Yet, monitoring ultrafast, nanoscopic dynamics in real-time is challenging, which often complicates a direct comparison between theory and experiment. Here, near-infrared laser-induced plasma dynamics in ∼600 nm diameter helium droplets are studied by femtosecond time-resolved x-ray coherent diffractive imaging. An anisotropic, ∼20 nm wide surface region, defined as the range where the density lies between 10% and 90% of the core value, is established within ∼100 fs, in qualitative agreement with theoretical predictions. At longer timescales, however, the width of this region remains largely constant while the radius of the dense plasma core shrinks at average rates of ≈71 nm/ps along and ≈33 nm/ps perpendicular to the laser polarization. These dynamics are not captured by previous plasma expansion models. The observations are phenomenologically described within a numerical simulation; details of the underlying physics, however, remain to be explored.

Sizes of pure and doped helium droplets from single shot x-ray imaging.

Advancements in x-ray free-electron lasers on producing ultrashort, ultrabright, and coherent x-ray pulses enable single-shot imaging of fragile nanostructures, such as superfluid helium droplets. This imaging technique gives unique access to the sizes and shapes of individual droplets. In the past, such droplet characteristics have only been indirectly inferred by ensemble averaging techniques. Here, we report on the size distributions of both pure and doped droplets collected from single-shot x-ray imaging and produced from the free-jet expansion of helium through a 5 µm diameter nozzle at 20 bars and nozzle temperatures ranging from 4.2 to 9 K. This work extends the measurement of large helium nanodroplets containing 109-1011 atoms, which are shown to follow an exponential size distribution. Additionally, we demonstrate that the size distributions of the doped droplets follow those of the pure droplets at the same stagnation condition but with smaller average sizes.

Angular Momentum in Rotating Superfluid Droplets.

The angular momentum of rotating superfluid droplets originates from quantized vortices and capillary waves, the interplay between which remains to be uncovered. Here, the rotation of isolated submicrometer superfluid ^{4}He droplets is studied by ultrafast x-ray diffraction using a free electron laser. The diffraction patterns provide simultaneous access to the morphology of the droplets and the vortex arrays they host. In capsule-shaped droplets, vortices form a distorted triangular lattice, whereas they arrange along elliptical contours in ellipsoidal droplets. The combined action of vortices and capillary waves results in droplet shapes close to those of classical droplets rotating with the same angular velocity. The findings are corroborated by density functional theory calculations describing the velocity fields and shape deformations of a rotating superfluid cylinder.

Evaporation Dynamics from Ag-Doped He Droplets upon Laser Excitation.

Silver clusters were assembled in helium droplets of different sizes ranging from 104 to 1011 atoms. The clusters were heated upon laser irradiation at 355 nm, and evaporation dynamics of He atoms were studied by quadrupole mass spectroscopy using signals from He+, He2+, and He4+ splitter ions. We found that for droplets containing less than 107 atoms the laser irradiation leads to evaporation of He atoms. However, the laser irradiation leads to the breakup of the large droplets into smaller ones.

Communication: X-ray coherent diffractive imaging by immersion in nanodroplets.

Lensless x-ray microscopy requires the recovery of the phase of the radiation scattered from a specimen. Here, we demonstrate a de novo phase retrieval technique by encapsulating an object in a superfluid helium nanodroplet, which provides both a physical support and an approximate scattering phase for the iterative image reconstruction. The technique is robust, fast-converging, and yields the complex density of the immersed object. Images of xenon clusters embedded in superfluid helium droplets reveal transient configurations of quantum vortices in this fragile system.

Helium superfluidity. Shapes and vorticities of superfluid helium nanodroplets.

Helium nanodroplets are considered ideal model systems to explore quantum hydrodynamics in self-contained, isolated superfluids. However, exploring the dynamic properties of individual droplets is experimentally challenging. In this work, we used single-shot femtosecond x-ray coherent diffractive imaging to investigate the rotation of single, isolated superfluid helium-4 droplets containing ~10(8) to 10(11) atoms. The formation of quantum vortex lattices inside the droplets is confirmed by observing characteristic Bragg patterns from xenon clusters trapped in the vortex cores. The vortex densities are up to five orders of magnitude larger than those observed in bulk liquid helium. The droplets exhibit large centrifugal deformations but retain axially symmetric shapes at angular velocities well beyond the stability range of viscous classical droplets.