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.
Develops Lagrange-Hamilton geometry for COVID-19 disease dynamics.
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. Learning such temporal patterns from event data is crucial f…
Disease progression models are instrumental in predicting individual-level health trajectories and understanding disease dynamics. Existing models are capable of providing either accurate predictions of patients prognoses or clinically interpretable representations of disease pathophysiology, but not both. In this pape…
Study forecasts cholera outbreaks in Malawi using dynamic models.
Unified model forecasts epidemics with spatial and temporal dynamics.
In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which med…
Model shows screening for infectious disease is hard but Thompson sampling works well.
For many complex diseases, there is a wide variety of ways in which an individual can manifest the disease. The challenge of personalized medicine is to develop tools that can accurately predict the trajectory of an individual's disease, which can in turn enable clinicians to optimize treatments. We represent an indivi…
Method estimates parameters for disease spread models robustly.
Bayesian meta-learning predicts Alzheimer's disease progression.
BoXHED boosts hazard estimation for dynamic health risk scores.
Proposes Ada-Sit method for mortality prediction of rare diseases.
Modeling disease progression using irregular time intervals in EHRs.
Enhances disease progression modeling using LLMs for complex brain connectivity.
New model predicts banana disease risk from climate data.
Prediction of the future trajectory of a disease is an important challenge for personalized medicine and population health management. However, many complex chronic diseases exhibit large degrees of heterogeneity, and furthermore there is not always a single readily available biomarker to quantify disease severity. Eve…
Improves disease progression prediction using auxiliary surrogate labels and health markers.
Alzheimer's Disease (AD) is characterized by a cascade of biomarkers becoming abnormal, the pathophysiology of which is very complex and largely unknown. Event-based modeling (EBM) is a data-driven technique to estimate the sequence in which biomarkers for a disease become abnormal based on cross-sectional data. It can…
Simulation-based inference aids in predicting disease dynamics for health policy.
New PG samplers improve inference in coupled state-space models.
Novel approach detects early warning indicators in complex systems.
We propose a method to predict the subject-specific longitudinal progression of brain structures extracted from baseline MRI, and evaluate its performance on Alzheimer's disease data. The disease progression is modeled as a trajectory on a group of diffeomorphisms in the context of large deformation diffeomorphic metri…
Background and objectives: Parkinson's disease is a neurological disorder that affects the motor system producing lack of coordination, resting tremor, and rigidity. Impairments in handwriting are among the main symptoms of the disease. Handwriting analysis can help in supporting the diagnosis and in monitoring the pro…
LHM integrates expert ODEs with neural ODEs for disease progression prediction.
RL algorithms with medical integration improve personalized treatment recommendations.
PHIBP predicts infectious disease outbreaks in sparse data regions.
Proposes dynamic borrowing method for historical data in clinical trials.
AdaptiveNet tackles disease progression prediction in rheumatoid arthritis using deep neural networks.
This paper presents the first deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexit…
One primary task of population health analysis is the identification of risk factors that, for some subpopulation, have a significant association with some health condition. Examples include finding lifestyle factors associated with chronic diseases and finding genetic mutations associated with diseases in precision he…
In this work, we consider the problem of predicting the course of a progressive disease, such as cancer or Alzheimer's. Progressive diseases often start with mild symptoms that might precede a diagnosis, and each patient follows their own trajectory. Patient trajectories exhibit wild variability, which can be associate…
Social dynamics is concerned primarily with interactions among individuals and the resulting group behaviors, modeling the temporal evolution of social systems via the interactions of individuals within these systems. In particular, the availability of large-scale data from social networks and sensor networks offers an…
Market trade-routes can support infectious-disease transmission, impacting biological populations and even disrupting causal trade. Epidemiological models increasingly account for reductions in infectious contact, such as risk-aversion behaviour in response to pathogen outbreaks. However, market dynamics clearly differ…
Here we present DIVE: Data-driven Inference of Vertexwise Evolution. DIVE is an image-based disease progression model with single-vertex resolution, designed to reconstruct long-term patterns of brain pathology from short-term longitudinal data sets. DIVE clusters vertex-wise biomarker measurements on the cortical surf…
Motion Code models time series dynamics with sparse approximations.
Model for valuing options on epidemic spread.
Patient subtyping based on temporal observations can lead to significantly nuanced subtyping that acknowledges the dynamic characteristics of diseases. Existing methods for subtyping trajectories treat the evolution of clinical observations as a homogeneous process or employ data available at regular intervals. In real…
Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk. Conventional methods in survival analysis are often constrained by strong parametric assumptions and limited in their ability to learn from high-dimensional data, while existing neural network mode…
Paper presents a spatio-temporal Bayesian model for early detection of COVID-19 hotspots.
New test identifies specific biological parameters for personalized CVD detection.
Study develops a dynamic risk model for COVID-19 mortality using UK Biobank data.
CASCADE improves uncertainty communication in Parkinson's disease medication management.
Proposes a neural network for dynamic risk prediction of AMD using longitudinal fundus images.
Bayesian hypergraph inference models disease pathways from EHR data.
Bayesian model identifies health disparities in disease progression.
GraphKKE learns fixed-length feature vectors from time-evolving graphs of human microbiome data.
Cardiac motion modeling using LDDMM and shape splines.
Disease phenotyping algorithms process observational clinical data to identify patients with specific diseases. Supervised phenotyping methods require significant quantities of expert-labeled data, while unsupervised methods may learn non-disease phenotypes. To address these limitations, we propose the Semi-Supervised …