Robust multi-modal fusion architecture for medical data with knowledge distillation.

Background: The fusion of multi-modal data has been shown to significantly enhance the performance of deep learning models, particularly on medical data. However, missing modalities are common in medical data due to patient specificity, which poses a substantial challenge to the application of these models.

Objective: This study aimed to develop a novel and efficient multi-modal fusion framework for medical datasets that maintains consistent performance, even in the absence of one or more modalities.

High-Fidelity and High-Efficiency Talking Portrait Synthesis With Detail-Aware Neural Radiance Fields.

In this paper, we propose a novel rendering framework based on neural radiance fields (NeRF) named HH-NeRF that can generate high-resolution audio-driven talking portrait videos with high fidelity and fast rendering. Specifically, our framework includes a detail-aware NeRF module and an efficient conditional super-resolution module. Firstly, a detail-aware NeRF is proposed to efficiently generate a high-fidelity low-resolution talking head, by using the encoded volume density estimation and audio-eye-aware color calculation.

Large negative differential conductance and its transformation in a single radical molecule.

The discovery of negative differential conductance (NDC) in a single molecule and mechanism controlling this phenomenon are important for molecular electronics. We investigated the electronic properties of a typical radical molecule 3-carbamoyl-2,2,5,5-tetramethyl-3-pyrrolin-1-yloxy (CTPO) on an Au(111) surface using low-temperature scanning tunneling microscopy (STM) and inelastic electron tunneling spectroscopy. Large NDC was observed in single CTPO molecules at the boundary of the crystal monolayer.

Computer aided diagnosis of diabetic retinopathy based on multi-view joint learning.

Diabetic retinopathy (DR) is a kind of ocular complication of diabetes, and its degree grade is an essential basis for early diagnosis of patients. Manual diagnosis is a long and expensive process with a specific risk of misdiagnosis. Computer-aided diagnosis can provide more accurate and practical treatment recommendations.

Research on Multimodal Fusion of Temporal Electronic Medical Records.

The surge in deep learning-driven EMR research has centered on harnessing diverse data forms. Yet, the amalgamation of diverse modalities within time series data remains an underexplored realm. This study probes a multimodal fusion approach, merging temporal and non-temporal clinical notes along with tabular data.

Representation of time-varying and time-invariant EMR data and its application in modeling outcome prediction for heart failure patients.

Objective: To represent a patient record with both time-invariant and time-varying features as a single vector using an end-to-end deep learning model, and further to predict the kidney failure (KF) status and mortality of heart failure (HF) patients.

Materials And Methods: The time-invariant EMR data included demographic information and comorbidities, and the time-varying EMR data were lab tests. We used a Transformer encoder module to represent the time-invariant data, and refined a long short-term memory (LSTM) with a Transformer encoder attached to the top to represent the time-varying data, taking the original measured values and their corresponding embedding vectors, masking vectors, and two types of time intervals as inputs.

Spatially Resolved Stimulation for the Controlled Debromination in Single Molecules on a Surface.

A controlled chemical reaction on a specific bond in a single molecule is an inevitable step toward atomic engineering and fabrication. Here, we explored the debromination of a single 9,10-dibromoanthracene (DBA) molecule on a surface as stimulated by the voltage pulse through the tip of a scanning tunneling microscope (STM). A voltage threshold of about 2.

Sequential Data-Based Patient Similarity Framework for Patient Outcome Prediction: Algorithm Development.

Background: Sequential information in electronic medical records is valuable and helpful for patient outcome prediction but is rarely used for patient similarity measurement because of its unevenness, irregularity, and heterogeneity.

Objective: We aimed to develop a patient similarity framework for patient outcome prediction that makes use of sequential and cross-sectional information in electronic medical record systems.

Methods: Sequence similarity was calculated from timestamped event sequences using edit distance, and trend similarity was calculated from time series using dynamic time warping and Haar decomposition.

Exploring the Relationship Between Anxiety, Depression, and Sleep Disturbance Among HIV Patients in China From a Network Perspective.

Mental disorder of people living with HIV (PLWH) has become a common and increasing worldwide public health concern. We aimed to explore the relationship between anxiety, depression, and sleep disturbance for PLWH from a network perspective. The network model featured 28 symptoms on the Hospital Anxiety and Depression scale questionnaire and Pittsburgh Sleep Quality Index questionnaire in a sample of 4,091 HIV-infected persons.