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.
Study shows model uncertainty is crucial for medical predictions, especially for individual patients.
Optimizes personalized medicine prediction by individual tuning.
This study addresses the issue of predicting the glaucomatous visual field loss from patient disease datasets. Our goal is to accurately predict the progress of the disease in individual patients. As very few measurements are available for each patient, it is difficult to produce good predictors for individuals. A rece…
Paper develops methods for estimating patient-specific survival curve prediction intervals.
Enhances patient failure prediction using dynamic survival models.
We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention. Our proposed framework targets at making personalized and reliable predictions from time-series data, such as Electronic Health Records (…
Machine learning predicts biologic therapy outcomes in psoriasis patients.
Improved forecasting of suicide attempts using LSGPs for patients with little data.
An accurate model of a patient's individual survival distribution can help determine the appropriate treatment for terminal patients. Unfortunately, risk scores (e.g., from Cox Proportional Hazard models) do not provide survival probabilities, single-time probability models (e.g., the Gail model, predicting 5 year prob…
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…
Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and gen…
Objective: Predict patient-specific vitals deemed medically acceptable for discharge from a pediatric intensive care unit (ICU). Design: The means of each patient's hr, sbp and dbp measurements between their medical and physical discharge from the ICU were computed as a proxy for their physiologically acceptable state …
New method improves prediction accuracy for low-risk patients in healthcare.
This research improves transparency in RNN predictions of ICU mortality risk.
New diagnostics detect variability in individual risk estimates from machine learning models in healthcare.
Identifies patient-specific root causes of disease using structural equation models.
Study predicts when ALS patients will lose speech, swallowing, etc. based on covariates.
Machine learning predicts trauma patient mortality risk.
Predicts clinical events using a landmark approach with machine learning for large biomarker histories.
Study on how imprecise medical data affects predictions in hyperthyroidism.
Personalized models explain TB treatment outcomes considering patient context.
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…
Models for predicting the risk of cardiovascular events based on individual patient characteristics are important tools for managing patient care. Most current and commonly used risk prediction models have been built from carefully selected epidemiological cohorts. However, the homogeneity and limited size of such coho…
WRSE predicts dynamic survival distributions in ICU patients.
Multiple Sclerosis is a degenerative condition of the central nervous system that affects nearly 2.5 million of individuals in terms of their physical, cognitive, psychological and social capabilities. Researchers are currently investigating on the use of patient reported outcome measures for the assessment of impact a…
Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or pri…
Deep learning boosts rare disease detection from medical claims.
Though suicide is a major public health problem in the US, machine learning methods are not commonly used to predict an individual's risk of attempting/committing suicide. In the present work, starting with an anonymized collection of electronic health records for 522,056 unique, California-resident adolescents, we dev…
Alzheimer's disease (AD) is a degenerative brain disease impairing a person's ability to perform day to day activities. The clinical manifestations of Alzheimer's disease are characterized by heterogeneity in age, disease span, progression rate, impairment of memory and cognitive abilities. Due to these variabilities, …
Kernel method optimizes personalized dose rules for patients.
Patient journeys are compared to find clusters of similar disease trajectories.
Determining the optimal initial dose for warfarin is a critically important task. Several factors have an impact on the therapeutic dose for individual patients, such as patients' physical attributes (Age, Height, etc.), medication profile, co-morbidities, and metabolic genotypes (CYP2C9 and VKORC1). These wide range f…
There is tremendous interest in precision medicine as a means to improve patient outcomes by tailoring treatment to individual characteristics. An individualized treatment rule formalizes precision medicine as a map from patient information to a recommended treatment. A treatment rule is defined to be optimal if it max…
A treatment regime is a function that maps individual patient information to a recommended treatment, hence explicitly incorporating the heterogeneity in need for treatment across individuals. Patient responses are dichotomous and can be predicted through an unknown relationship that depends on the patient information …
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…
BEHRT improves disease prediction in EHRs by 8-10%.
LI-ITR combines flexible ML with interpretable approximations for personalized treatment rules.
Machine learning builds personalized pediatric abdominal phantoms.
The paper compares methods for estimating individual treatment effects.
Febrile neutropenia (FN) has been associated with high mortality, especially among adults with cancer. Understanding the patient and provider level heterogeneity in FN hospital admissions has potential to inform personalized interventions focused on increasing survival of individuals with FN. We leverage machine learni…
Proposes a tool to contrast global vs personalized models in clinical prediction.
Framework integrates mental disorder measurements for personalized treatment.
Study predicts Type 2 diabetes with 85% accuracy using machine learning.
New method optimizes individualized decision rules for precision medicine.
MLHO predicts COVID-19 adverse outcomes using past medical records.
AdaptiveNet tackles disease progression prediction in rheumatoid arthritis using deep neural networks.
Study uses machine learning to analyze patient survey data for Lyme disease.
Sepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the individual patient is essential for the best outcomes. In this paper, we take steps toward this goal by applying a mixture-of-experts framework …