Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies.

OBJECTIVE: Predictive disease modeling using electronic health record data is a growing field. Although clinical data in their raw form can be used directly for predictive modeling, it is a common practice to map data to standard terminologies to facilitate data aggregation and reuse. There is, however, a lack of systematic investigation of how different representations could affect the performance of predictive models, especially in the context of machine learning and deep learning. MATERIALS AND METHODS: We projected the input diagnoses data in the Cerner HealthFacts database to Unified Medical Language System (UMLS) and 5 other terminologies, including CCS, CCSR, ICD-9, ICD-10, and PheWAS, and evaluated the prediction performances of these terminologies on 2 different tasks: the risk prediction of heart failure in diabetes patients and the risk prediction of pancreatic cancer. Two popular models were evaluated: logistic regression and a recurrent neural network. RESULTS: For logistic regression, using UMLS delivered the optimal area under the receiver operating characteristics (AUROC) results in both dengue hemorrhagic fever (81.15%) and pancreatic cancer (80.53%) tasks. For recurrent neural network, UMLS worked best for pancreatic cancer prediction (AUROC 82.24%), second only (AUROC 85.55%) to PheWAS (AUROC 85.87%) for dengue hemorrhagic fever prediction. DISCUSSION/CONCLUSION: In our experiments, terminologies with larger vocabularies and finer-grained representations were associated with better prediction performances. In particular, UMLS is consistently 1 of the best-performing ones. We believe that our work may help to inform better designs of predictive models, although further investigation is warranted

Underwater Communication Acoustic Transducers: A Technology Review

This paper provides a comprehensive review on transducer technologies for underwater communications. The popularly used communication transducers, such as piezoelectric acoustic transducers, electromagnetic acoustic transducers, and acousto-optic devices are reviewed in detail. The reasons that common air communication technologies are invalid die to the differences between the media of air and water are addresses. Because of the abilities to overcome challenges the complexity of marine environments, piezoelectric acoustic transducers are playing the major underwater communication roles for science, surveillance, and Naval missions. The configuration and material properties of piezoelectric transducers effects on signal output power, beamwidth, amplitude, and other properties are discussed. The methods of code and decode communication information signals into acoustic waves are also presented. Finally, several newly developed piezoelectric transducers are recommended for future studies

Prediction of Brain Metastases Development in Patients With Lung Cancer by Explainable Artificial Intelligence From Electronic Health Records

PURPOSE: Early detection of brain metastases (BMs) is critical for prompt treatment and optimal control of the disease. In this study, we seek to predict the risk of developing BM among patients diagnosed with lung cancer on the basis of electronic health record (EHR) data and to understand what factors are important for the model to predict BM development through explainable artificial intelligence approaches accurately. MATERIALS AND METHODS: We trained a recurrent neural network model, REverse Time AttentIoN (RETAIN), to predict the risk of developing BM using structured EHR data. To interpret the model\u27s decision process, we analyzed the attention weights in the RETAIN model and the SHAP values from a feature attribution method, Kernel SHAP, to identify the factors contributing to BM prediction. RESULTS: We developed a high-quality cohort with 4,466 patients with BM from the Cerner Health Fact database, which contains over 70 million patients from more than 600 hospitals. RETAIN uses this data set to achieve the best area under the receiver operating characteristic curve at 0.825, a significant improvement over the baseline model. We also extended a feature attribution method, Kernel SHAP, to structured EHR data for model interpretation. Both RETAIN and Kernel SHAP can identify important features related to BM prediction. CONCLUSION: To the best of our knowledge, this is the first study to predict BM using structured EHR data. We achieved decent prediction performance for BM prediction and identified factors highly relevant to BM development. The sensitivity analysis demonstrated that both RETAIN and Kernel SHAP could discriminate unrelated features and put more weight on the features important to BM. Our study explored the potential of applying explainable artificial intelligence for future clinical applications

Prediction of Brain Metastases Development in Patients With Lung Cancer by Explainable Artificial Intelligence From Electronic Health Records

PURPOSE: Early detection of brain metastases (BMs) is critical for prompt treatment and optimal control of the disease. In this study, we seek to predict the risk of developing BM among patients diagnosed with lung cancer on the basis of electronic health record (EHR) data and to understand what factors are important for the model to predict BM development through explainable artificial intelligence approaches accurately. MATERIALS AND METHODS: We trained a recurrent neural network model, REverse Time AttentIoN (RETAIN), to predict the risk of developing BM using structured EHR data. To interpret the model\u27s decision process, we analyzed the attention weights in the RETAIN model and the SHAP values from a feature attribution method, Kernel SHAP, to identify the factors contributing to BM prediction. RESULTS: We developed a high-quality cohort with 4,466 patients with BM from the Cerner Health Fact database, which contains over 70 million patients from more than 600 hospitals. RETAIN uses this data set to achieve the best area under the receiver operating characteristic curve at 0.825, a significant improvement over the baseline model. We also extended a feature attribution method, Kernel SHAP, to structured EHR data for model interpretation. Both RETAIN and Kernel SHAP can identify important features related to BM prediction. CONCLUSION: To the best of our knowledge, this is the first study to predict BM using structured EHR data. We achieved decent prediction performance for BM prediction and identified factors highly relevant to BM development. The sensitivity analysis demonstrated that both RETAIN and Kernel SHAP could discriminate unrelated features and put more weight on the features important to BM. Our study explored the potential of applying explainable artificial intelligence for future clinical applications

Observations and Modeling of the Mars Low‐Altitude Ionospheric Response to the 10 September 2017 X‐Class Solar Flare

Solar extreme ultraviolet and X‐ray photons are the main sources of ionization in the Martian ionosphere and can be enhanced significantly during a solar flare. On 10 September 2017, the Mars Atmosphere and Volatile EvolutioN orbiter observed an X8.2 solar flare, the largest it has encountered to date. Here we investigate the ionospheric response before, during, and after this event with the SuperThermal Electron Transport model. We find good agreement between modeled and measured photoelectron spectra. In addition, the high photoelectron fluxes during the flare provide adequate statistics to allow us to clearly and repeatedly identify the carbon Auger peak in the ionospheric photoelectron energy spectra at Mars for the first time. By applying photochemical equilibrium, O2+ and CO2+ densities are obtained and compared with Mars Atmosphere and Volatile EvolutioN observations. The variations in ion densities during this event due to the solar irradiance enhancement and the neutral atmosphere expansion are discussed.Plain Language SummarySolar extreme ultraviolet and X‐ray photons are the main source of ionization in the Martian ionosphere, photoionizing the neutral particles and producing photoelectrons and ions. These short‐wavelength photon fluxes can be enhanced by a factor of a few to orders of magnitudes during a solar flare (the result of the rapid conversion of magnetic energy to kinetic energy in the solar corona). On 10 September 2017, the Mars Atmosphere and Volatile EvolutioN mission encountered the largest solar flare (X8.2) to date. The comprehensive measurements from Mars Atmosphere and Volatile EvolutioN provide us with an opportunity to evaluate the ionospheric response to this flare event in detail with models. In particular, we investigate the photoelectron flux and ion density response to the flare with an electron transport model. The modeled and measured photoelectron fluxes are in a good agreement. Ion density enhancement at a fixed altitude is from tens of percent to 1500% due to a combination of intensified solar photon fluxes and the heated and then expanded neutral atmosphere during this flare event.Key PointsThe modeled and measured photoelectron spectra are in good agreement during an X8.2 solar flare eventThe carbon Auger peak is clearly and repeatedly identified in electron energy spectra of the Martian ionosphere for the first timeThe ion density enhancement due to the flare at a fixed altitude is from tens to 1,500%Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145576/1/grl57692.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145576/2/grl57692_am.pd

Quantum Holographic Encoding in a Two-dimensional Electron Gas

The advent of bottom-up atomic manipulation heralded a new horizon for attainable information density, as it allowed a bit of information to be represented by a single atom. The discrete spacing between atoms in condensed matter has thus set a rigid limit on the maximum possible information density. While modern technologies are still far from this scale, all theoretical downscaling of devices terminates at this spatial limit. Here, however, we break this barrier with electronic quantum encoding scaled to subatomic densities. We use atomic manipulation to first construct open nanostructures--"molecular holograms"--which in turn concentrate information into a medium free of lattice constraints: the quantum states of a two-dimensional degenerate Fermi gas of electrons. The information embedded in the holograms is transcoded at even smaller length scales into an atomically uniform area of a copper surface, where it is densely projected into both two spatial degrees of freedom and a third holographic dimension mapped to energy. In analogy to optical volume holography, this requires precise amplitude and phase engineering of electron wavefunctions to assemble pages of information volumetrically. This data is read out by mapping the energy-resolved electron density of states with a scanning tunnelling microscope. As the projection and readout are both extremely near-field, and because we use native quantum states rather than an external beam, we are not limited by lensing or collimation and can create electronically projected objects with features as small as ~0.3 nm. These techniques reach unprecedented densities exceeding 20 bits/nm2 and place tens of bits into a single fermionic state.Comment: Published online 25 January 2009 in Nature Nanotechnology; 12 page manuscript (including 4 figures) + 2 page supplement (including 1 figure); supplementary movie available at http://mota.stanford.ed

A global method for coupling transport with chemistry in heterogeneous porous media

Modeling reactive transport in porous media, using a local chemical equilibrium assumption, leads to a system of advection-diffusion PDE's coupled with algebraic equations. When solving this coupled system, the algebraic equations have to be solved at each grid point for each chemical species and at each time step. This leads to a coupled non-linear system. In this paper a global solution approach that enables to keep the software codes for transport and chemistry distinct is proposed. The method applies the Newton-Krylov framework to the formulation for reactive transport used in operator splitting. The method is formulated in terms of total mobile and total fixed concentrations and uses the chemical solver as a black box, as it only requires that on be able to solve chemical equilibrium problems (and compute derivatives), without having to know the solution method. An additional advantage of the Newton-Krylov method is that the Jacobian is only needed as an operator in a Jacobian matrix times vector product. The proposed method is tested on the MoMaS reactive transport benchmark.Comment: Computational Geosciences (2009) http://www.springerlink.com/content/933p55085742m203/?p=db14bb8c399b49979ba8389a3cae1b0f&pi=1

Data Resource Profile: the Children and Young People with Long COVID (CLoCk) Study

Key Features: The Children and Young People with Long COVID (CLoCk) study is a national matched prospective study which was set up with the aims of (i) describing the clinical phenotype of post-COVID symptomology in children and young people (CYP), (ii) producing a research definition for long COVID in CYP and (iii) establishing the prevalence of long COVID in CYP. In total, 219 175 CYP aged 11–17 years, who were tested for SARS-CoV-2 via polymerase chain reaction (PCR) testing between September 2020 and March 2021 in England, were invited to participate. Test-positive and test-negative CYP were matched at study invitation on month of test, age, sex and geographical region. CYP were enrolled into the study at 3, 6 or 12 months after their index PCR test (n = 31 012). Depending on when they enrolled, they were also invited to fill in follow-up questionnaires at 6, 12 and 24 months after their index test. The overall response rate was 14.1%, with retention across sweeps varying from 36.6% to 54.1%. The dataset includes information on physical and mental health using validated scales over time, allowing for examination of within-individual change. CYP report symptoms themselves rather than relying on parental report or administrative records. Requests for access to the participant-level data from this study can be submitted via email to: [[email protected]]

Long COVID in Children and Youth After Infection or Reinfection with the Omicron Variant: A Prospective Observational Study

To describe the prevalence of long COVID in children infected for the first time (n=332) or reinfected (n=243) with Omicron variant SARS-CoV-2, compared with test-negative children (n=311). 12-16% infected with Omicron met the research definition of long COVID at 3 and 6 months after infection, with no evidence of difference between cases of first-positive and reinfection (pchi-square=0.17)