A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Bayesian methods improve group testing for identifying infected patients.
Paper proposes a new method for more accurate group testing of infected patients.
This study proposes a method to predict ICU infections from imbalanced data using clustering-based undersampling and ensemble classifiers.
Although timely sepsis diagnosis and prompt interventions in Intensive Care Unit (ICU) patients are associated with reduced mortality, early clinical recognition is frequently impeded by non-specific signs of infection and failure to detect signs of sepsis-induced organ dysfunction in a constellation of dynamically cha…
Develops a method to analyze SARS-CoV-2 viral load vs. age, finding a significant increase.
Paper derives policy rules from observational data for hepatitis C treatment.
Bayesian optimization improves classifier selection for acute infection and mortality.
A large fraction of the electronic health records consists of clinical measurements collected over time, such as blood tests, which provide important information about the health status of a patient. These sequences of clinical measurements are naturally represented as time series, characterized by multiple variables a…
Novel method combines neural network features with survival models for ICU infections.
Machine learning model diagnoses COVID-19 from routine blood tests.
Deep learning model predicts severe COVID-19 outcomes.
Research proposes a risk-free machine learning model for COVID screening from routine blood tests.
Study uses machine learning to optimize antibiotic therapy for MRSA skin infections.
Deep learning identifies transcriptomic patterns and cell types associated with SARS-CoV-2 infection and COVID-19 severity.
This study interprets machine learning models to identify biomarkers for severe COVID-19 infection.
Blood lactate concentration is a strong indicator of mortality risk in critically ill patients. While frequent lactate measurements are necessary to assess patient's health state, the measurement is an invasive procedure that can increase risk of hospital-acquired infections. For this reason we formally define the prob…
MLHO predicts COVID-19 adverse outcomes using past medical records.
This paper tackles efficient testing strategies for COVID-19 by using a partially observable MDP approach.
Optimizes group testing for COVID-19 to reduce test numbers.
Chronic Pulmonary Aspergillosis (CPA) is a complex lung disease caused by infection with Aspergillus. Computed tomography (CT) images are frequently requested in patients with suspected and established disease, but the radiological signs on CT are difficult to quantify making accurate follow-up challenging. We propose …
Study reveals hidden infections and infection dynamics from early data.
Italy's daily COVID-19 cases stratified by age groups.
Three approaches learn personalized treatment policies for UTI patients.
After admission to emergency department (ED), patients with critical illnesses are transferred to intensive care unit (ICU) due to unexpected clinical deterioration occurrence. Identifying such unplanned ICU transfers is urgently needed for medical physicians to achieve two-fold goals: improving critical care quality a…
Sepsis is a poorly understood and potentially life-threatening complication that can occur as a result of infection. Early detection and treatment improves patient outcomes, and as such it poses an important challenge in medicine. In this work, we develop a flexible classifier that leverages streaming lab results, vita…
A new model characterizes undocumented and asymptomatic infections to quantify COVID-19 uncertainties.
Sepsis is a life-threatening condition caused by the body's response to an infection. In order to treat patients with sepsis, physicians must control varying dosages of various antibiotics, fluids, and vasopressors based on a large number of variables in an emergency setting. In this project we employ a "world model" m…
Study predicts future hospitalizations to manage COVID-19 patient surge.
Satellite constructions on a knot can be thought of as taking some strands of a knot and then tying in another knot. Using satellite constructions one can construct many distinct isotopy classes of knots. Pushing this further one can construct distinct concordance classes of knots which preserve some algebraic invarian…
CRISP predicts individual-level COVID-19 risk based on contact data.
Modeling infection hotspots to quantify effects of contact tracing and testing.
Clinical measurements collected over time are naturally represented as multivariate time series (MTS), which often contain missing data. An autoencoder can learn low dimensional vectorial representations of MTS that preserve important data characteristics, but cannot deal explicitly with missing data. In this work, we …
Study develops a dynamic risk model for COVID-19 mortality using UK Biobank data.
We present a scalable end-to-end classifier that uses streaming physiological and medication data to accurately predict the onset of sepsis, a life-threatening complication from infections that has high mortality and morbidity. Our proposed framework models the multivariate trajectories of continuous-valued physiologic…
Population attributes are essential in health for understanding who the data represents and precision medicine efforts. Even within disease infection labels, patients can exhibit significant variability; "fever" may mean something different when reported in a doctor's office versus from an online app, precluding direct…
Model predicts COVID-19 growth in Senegal, highlighting health care capacity importance.
Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traff…
Machine learning improves diagnostic test accuracy for bovine tuberculosis.
A time-dependent SIR model predicts COVID-19 spread and herd immunity.
Precision medicine is becoming a focus in medical research recently, as its implementation brings values to all stakeholders in the healthcare system. Various statistical methodologies have been developed tackling problems in different aspects of this field, e.g., assessing treatment heterogeneity, identifying patient …
HIV RNA viral load (VL) is an important outcome variable in studies of HIV infected persons. There exists only a handful of methods which classify patients by viral load patterns. Most methods place limits on the use of viral load measurements, are often specific to a particular study design, and do not account for com…
New Milnor's invariant condition for topologically slice links.
Antimicrobial resistance is an important public health concern that has implications in the practice of medicine worldwide. Accurately predicting resistance phenotypes from genome sequences shows great promise in promoting better use of antimicrobial agents, by determining which antibiotics are likely to be effective i…
LSTM models predict low likelihood of another COVID-19 wave in India.
Many complex ecosystems, such as those formed by multiple microbial taxa, involve intricate interactions amongst various sub-communities. The most basic relationships are frequently modeled as co-occurrence networks in which the nodes represent the various players in the community and the weighted edges encode levels o…
Budney recently constructed an operad that encodes splicing of knots. He further showed that the space of (long) knots is generated over this operad by the space of torus knots and hyperbolic knots, thus generalizing the satellite decomposition of knots from isotopy classes to the level of the space of knots. Infection…
We consider the problem of learning the weighted edges of a graph by observing the noisy times of infection for multiple epidemic cascades on this graph. Past work has considered this problem when the cascade information, i.e., infection times, are known exactly. Though the noisy setting is well motivated by many epide…
Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.