Cloud-Based Machine Learning Platform to Predict Clinical Outcomes at Home for Patients With Cardiovascular Conditions Discharged From Hospital: Clinical Trial.

BACKGROUND: Hospitalizations account for almost one-third of the US $4.1 trillion health care cost in the United States. A substantial portion of these hospitalizations are attributed to readmissions, which led to the establishment of the Hospital Readmissions Reduction Program (HRRP) in 2012. The HRRP reduces payments to hospitals with excess readmissions. In 2018, >US $700 million was withheld; this is expected to exceed US $1 billion by 2022. More importantly, there is nothing more physically and emotionally taxing for readmitted patients and demoralizing for hospital physicians, nurses, and administrators. Given this high uncertainty of proper home recovery, intelligent monitoring is needed to predict the outcome of discharged patients to reduce readmissions. Physical activity (PA) is one of the major determinants for overall clinical outcomes in diabetes, hypertension, hyperlipidemia, heart failure, cancer, and mental health issues. These are the exact comorbidities that increase readmission rates, underlining the importance of PA in assessing the recovery of patients by quantitative measurement beyond the questionnaire and survey methods. OBJECTIVE: This study aims to develop a remote, low-cost, and cloud-based machine learning (ML) platform to enable the precision health monitoring of PA, which may fundamentally alter the delivery of home health care. To validate this technology, we conducted a clinical trial to test the ability of our platform to predict clinical outcomes in discharged patients. METHODS: Our platform consists of a wearable device, which includes an accelerometer and a Bluetooth sensor, and an iPhone connected to our cloud-based ML interface to analyze PA remotely and predict clinical outcomes. This system was deployed at a skilled nursing facility where we collected >17,000 person-day data points over 2 years, generating a solid training database. We used these data to train our extreme gradient boosting (XGBoost)-based ML environment to conduct a clinical trial, Activity Assessment of Patients Discharged from Hospital-I, to test the hypothesis that a comprehensive profile of PA would predict clinical outcome. We developed an advanced data-driven analytic platform that predicts the clinical outcome based on accurate measurements of PA. Artificial intelligence or an ML algorithm was used to analyze the data to predict short-term health outcome. RESULTS: We enrolled 52 patients discharged from Stanford Hospital. Our data demonstrated a robust predictive system to forecast health outcome in the enrolled patients based on their PA data. We achieved precise prediction of the patients' clinical outcomes with a sensitivity of 87%, a specificity of 79%, and an accuracy of 85%. CONCLUSIONS: To date, there are no reliable clinical data, using a wearable device, regarding monitoring discharged patients to predict their recovery. We conducted a clinical trial to assess outcome data rigorously to be used reliably for remote home care by patients, health care professionals, and caretakers.

Hierarchical Superhydrophobic Poly(vinylidene fluoride-co-hexafluoropropylene) Membrane with a Bead (SiO2 Nanoparticles)-on-String (Nanofibers) Structure for All-Day Passive Radiative Cooling.

Passive all-day radiative cooling has been proposed as a promising pathway to cool objects by reflecting sunlight and dissipating heat to the cold outer space through atmospheric windows without any energy consumption. However, most of the existing radiative coolers are susceptible to contamination, which may decrease the optical property and gradually degrade the outdoor radiative cooling performance. Herein, we prepared a hierarchical superhydrophobic fluorinated-SiO2/PVDF-HFP nanofiber membrane by a facile and scalable technology of electrospinning and electrostatic spraying. Due to the synergistic effects of the efficient scattering of nanofibers/micropores and the phonon polarization resonance of SiO2 nanoparticles, the membrane achieves up to 97.8% average solar reflectance and 96.6% average atmospheric window emittance. The membrane displays sub-ambient temperature drop values of 11.5 and 4.1 °C in daytime and nighttime outdoor conditions, respectively, exhibiting remarkable radiative cooling performance. Importantly, the unique bead (SiO2 nanoparticles)-on-string (nanofibers) structure forms hierarchical roughness that endows the surface with a superior self-cleaning property. In addition, the obtained membrane exhibits remarkable flexibility and mechanical stability, which are of significant importance in cooling vehicles, buildings, and large-scale equipment.

From deterministic to stochastic: an interpretable stochastic model-free reinforcement learning framework for portfolio optimization.

As a fundamental problem in algorithmic trading, portfolio optimization aims to maximize the cumulative return by continuously investing in various financial derivatives within a given time period. Recent years have witnessed the transformation from traditional machine learning trading algorithms to reinforcement learning algorithms due to their superior nature of sequential decision making. However, the exponential growth of the imperfect and noisy financial data that is supposedly leveraged by the deterministic strategy in reinforcement learning, makes it increasingly challenging for one to continuously obtain a profitable portfolio. Thus, in this work, we first reconstruct several deterministic and stochastic reinforcement algorithms as benchmarks. On this basis, we introduce a risk-aware reward function to balance the risk and return. Importantly, we propose a novel interpretable stochastic reinforcement learning framework which tailors a stochastic policy parameterized by Gaussian Mixtures and a distributional critic realized by quantiles for the problem of portfolio optimization. In our experiment, the proposed algorithm demonstrates its superior performance on U.S. market stocks with a 63.1% annual rate of return while at the same time reducing the market value max drawdown by 10% when back-testing during the stock market crash around March 2020.

Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications.

Recent studies suggest that epi-transcriptome regulation via post-transcriptional RNA modifications is vital for all RNA types. Precise identification of RNA modification sites is essential for understanding the functions and regulatory mechanisms of RNAs. Here, we present MultiRM, a method for the integrated prediction and interpretation of post-transcriptional RNA modifications from RNA sequences. Built upon an attention-based multi-label deep learning framework, MultiRM not only simultaneously predicts the putative sites of twelve widely occurring transcriptome modifications (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um), but also returns the key sequence contents that contribute most to the positive predictions. Importantly, our model revealed a strong association among different types of RNA modifications from the perspective of their associated sequence contexts. Our work provides a solution for detecting multiple RNA modifications, enabling an integrated analysis of these RNA modifications, and gaining a better understanding of sequence-based RNA modification mechanisms.

Investigation of Dropwise Condensation Heat Transfer on Laser-Ablated Superhydrophobic/Hydrophilic Hybrid Copper Surfaces.

Heterogeneous surfaces with wetting contrast have gained extensive attention in recent years because of their potential application in condensation heat transfer enhancement. In this work, we engineered superhydrophobic/hydrophilic hybrid (SHH) surfaces on copper substrates via a laser-ablation process. We demonstrated that the as-fabricated SHH surfaces present dropwise condensation behavior; the condensate droplet growth, departure, and heat transfer performance depend strongly on the spacing of the hydrophilic spot. The surface with the hydrophilic spot spacing of 100 µm (SHH100) exhibits the most efficient dropwise condensation in terms of fast droplet growth rate, efficient coalescence-induced droplet departure, as well as enhanced heat transfer coefficient (HTC) compared to the homogeneous superhydrophobic (SHPo) surface. The mechanism underlying the enhanced condensation heat transfer performance is analyzed. A 12% enhancement on condensation HTC was found was found on SHH100 surface compared with the SHPo surface. Our results provide important insights for the design of hybrid surfaces with wetting contrast for enhancing condensation heat transfer performance in many industrial applications.