191 research outputs found
Knowledge discovery from posts in online health communities using unified medical language system
Patient-reported posts in Online Health Communities (OHCs) contain various valuable information that can help establish knowledge-based online support for online patients. However, utilizing these reports to improve online patient services in the absence of appropriate medical and healthcare expert knowledge is difficult. Thus, we propose a comprehensive knowledge discovery method that is based on the Unified Medical Language System for the analysis of narrative posts in OHCs. First, we propose a domain-knowledge support framework for OHCs to provide a basis for post analysis. Second, we develop a Knowledge-Involved Topic Modeling (KI-TM) method to extract and expand explicit knowledge within the text. We propose four metrics, namely, explicit knowledge rate, latent knowledge rate, knowledge correlation rate, and perplexity, for the evaluation of the KI-TM method. Our experimental results indicate that our proposed method outperforms existing methods in terms of providing knowledge support. Our method enhances knowledge support for online patients and can help develop intelligent OHCs in the future
A Knowledge-Constrained Role-Based Access Control model for protecting patient privacy in hospital information systems
Current access control mechanisms of the hospital
information system can hardly identify the real access intention of system users. A relaxed access control increases the risk of compromise of patient privacy. To reduce unnecessary access of patient information by hospital staff, this paper proposes a Knowledge-Constrained Role-Based Access Control (KCRBAC)model in which a variety of medical domain knowledge is considered in access control. Based on the proposed Purpose Tree and knowledge-involved algorithms, the model can dynamically define the boundary of access to the patient information according to the context, which helps protect patient privacy by controlling access. Compared with the Role-Based Access Control model, KC-RBAC can effectively protectpatient information according to the results of the experiments
AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation
In the domain of streaming recommender systems, conventional methods for
addressing new user IDs or item IDs typically involve assigning initial ID
embeddings randomly. However, this practice results in two practical
challenges: (i) Items or users with limited interactive data may yield
suboptimal prediction performance. (ii) Embedding new IDs or low-frequency IDs
necessitates consistently expanding the embedding table, leading to unnecessary
memory consumption. In light of these concerns, we introduce a reinforcement
learning-driven framework, namely AutoAssign+, that facilitates Automatic
Shared Embedding Assignment Plus. To be specific, AutoAssign+ utilizes an
Identity Agent as an actor network, which plays a dual role: (i) Representing
low-frequency IDs field-wise with a small set of shared embeddings to enhance
the embedding initialization, and (ii) Dynamically determining which ID
features should be retained or eliminated in the embedding table. The policy of
the agent is optimized with the guidance of a critic network. To evaluate the
effectiveness of our approach, we perform extensive experiments on three
commonly used benchmark datasets. Our experiment results demonstrate that
AutoAssign+ is capable of significantly enhancing recommendation performance by
mitigating the cold-start problem. Furthermore, our framework yields a
reduction in memory usage of approximately 20-30%, verifying its practical
effectiveness and efficiency for streaming recommender systems
Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning
As an efficient distributed machine learning approach, Federated learning
(FL) can obtain a shared model by iterative local model training at the user
side and global model aggregating at the central server side, thereby
protecting privacy of users. Mobile users in FL systems typically communicate
with base stations (BSs) via wireless channels, where training performance
could be degraded due to unreliable access caused by user mobility. However,
existing work only investigates a static scenario or random initialization of
user locations, which fail to capture mobility in real-world networks. To
tackle this issue, we propose a practical model for user mobility in FL across
multiple BSs, and develop a user scheduling and resource allocation method to
minimize the training delay with constrained communication resources.
Specifically, we first formulate an optimization problem with user mobility
that jointly considers user selection, BS assignment to users, and bandwidth
allocation to minimize the latency in each communication round. This
optimization problem turned out to be NP-hard and we proposed a delay-aware
greedy search algorithm (DAGSA) to solve it. Simulation results show that the
proposed algorithm achieves better performance than the state-of-the-art
baselines and a certain level of user mobility could improve training
performance
Fulfilling information needs of online patients using domain knowledge in online health communities
Background: Online health communities (OHCs) experience difficulties in utilizing patient-reported posts to fulfill the information needs of online patients concerning healthrelated issues. Objectives: We aim to propose a comprehensive method that leverages medical domain knowledge to extract useful information from posts to fulfill information needs of online patients. Methods: A knowledge representation framework based on authoritative knowledge sources in the medical field for the OHC is proposed. On the basis of the framework, a health-related information extraction process for analyzing the posts in the OHC is proposed. Then, knowledge support rate (KSR) and effective information rate (EIR) are introduced as metrics to evaluate changes in knowledge extracted from the knowledge sources in terms of fulfilling the information needs of patients in the OHC. Results: On the basis of a dataset with 372,343 posts in an OHC, experimental results indicate that our method effectively extracts relevant knowledge for online patients. Moreover, KSR and EIR are feasible metrics of changes in knowledge in terms of fulfilling the information needs. Conclusions: The OHCs effectively fulfill the information needs of patients by utilizing authoritative domain knowledge in the medical field. Knowledge-based services for online patients facilitate an intelligent OHC in the future
An HPC-Based Hydrothermal Finite Element Simulator for Modeling Underground Response to Community-Scale Geothermal Energy Production
Geothermal heat, as renewable energy, shows great advantage with respect to
its environmental impact due to its significantly lower CO2 emissions than
conventional fossil fuel. Open and closed-loop geothermal heat pumps, which
utilize shallow geothermal systems, are an efficient technology for cooling and
heating buildings, especially in urban areas. Integrated use of geothermal
energy technologies for district heating, cooling, and thermal energy storage
can be applied to optimize the subsurface for communities to provide them with
multiple sustainable energy and community resilience benefits. The utilization
of the subsurface resources may lead to a variation in the underground
environment, which might further impact local environmental conditions.
However, very few simulators can handle such a highly complex set of coupled
computations on a regional or city scale. We have developed high-performance
computing (HPC) based hydrothermal finite element (FE) simulator that can
simulate the subsurface and its hydrothermal conditions at a scale of tens of
km. The HPC simulator enables us to investigate the subsurface thermal and
hydrologic response to the built underground environment (such as basements and
subways) at the community scale. In this study, a coupled hydrothermal
simulator is developed based on the open-source finite element library deal.II.
The HPC simulator was validated by comparing the results of a benchmark case
study against COMSOL Multiphysics, in which Aquifer Thermal Energy Storage
(ATES) is modeled and a process of heat injection into ATES is simulated. The
use of an energy pile system at the Treasure Island redevelopment site (San
Francisco, CA, USA) was selected as a case study to demonstrate the HPC
capability of the developed simulator. The simulator is capable of modeling
multiple city-scale geothermal scenarios in a reasonable amount of time.Comment: 46th Workshop on Geothermal Reservoir Engineering Stanford
University, Stanford, California, February 15-17, 202
CoDeF: Content Deformation Fields for Temporally Consistent Video Processing
We present the content deformation field CoDeF as a new type of video
representation, which consists of a canonical content field aggregating the
static contents in the entire video and a temporal deformation field recording
the transformations from the canonical image (i.e., rendered from the canonical
content field) to each individual frame along the time axis.Given a target
video, these two fields are jointly optimized to reconstruct it through a
carefully tailored rendering pipeline.We advisedly introduce some
regularizations into the optimization process, urging the canonical content
field to inherit semantics (e.g., the object shape) from the video.With such a
design, CoDeF naturally supports lifting image algorithms for video processing,
in the sense that one can apply an image algorithm to the canonical image and
effortlessly propagate the outcomes to the entire video with the aid of the
temporal deformation field.We experimentally show that CoDeF is able to lift
image-to-image translation to video-to-video translation and lift keypoint
detection to keypoint tracking without any training.More importantly, thanks to
our lifting strategy that deploys the algorithms on only one image, we achieve
superior cross-frame consistency in processed videos compared to existing
video-to-video translation approaches, and even manage to track non-rigid
objects like water and smog.Project page can be found at
https://qiuyu96.github.io/CoDeF/.Comment: Project Webpage: https://qiuyu96.github.io/CoDeF/, Code:
https://github.com/qiuyu96/CoDe
Artificial Channels in an Infectious Biofilm Created by Magnetic Nanoparticles Enhanced Bacterial Killing by Antibiotics
The poor penetrability of many biofilms contributes to the recalcitrance of infectious biofilms to antimicrobial treatment. Here, a new application for the use of magnetic nanoparticles in nanomedicine to create artificial channels in infectious biofilms to enhance antimicrobial penetration and bacterial killing is proposed. Staphylococcus aureus biofilms are exposed to magnetic-iron-oxide nanoparticles (MIONPs), while magnetically forcing MIONP movement through the biofilm. Confocal laser scanning microscopy demonstrates artificial channel digging perpendicular to the substratum surface. Artificial channel digging significantly (4-6-fold) enhances biofilm penetration and bacterial killing efficacy by gentamicin in two S. aureus strains with and without the ability to produce extracellular polymeric substances. Herewith, this work provides a simple, new, and easy way to enhance the eradication of infectious biofilms using MIONPs combined with clinically applied antibiotic therapies
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