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.
StageNet improves health risk prediction by integrating disease stage information.
In recent years, increasingly augmentation of health data, such as patient Electronic Health Records (EHR), are becoming readily available. This provides an unprecedented opportunity for knowledge discovery and data mining algorithms to dig insights from them, which can, later on, be helpful to the improvement of the q…
Model predicts default risk based on company's financial forecasts and credit conditions.
Deep learning predicts opioid use disorder risk in patients.
Machine learning models outperform traditional actuarial methods in predicting health insurance costs.
AdaCare learns health status from biomarkers across multiple time scales.
The widespread digitization of patient data via electronic health records (EHRs) has created an unprecedented opportunity to use machine learning algorithms to better predict disease risk at the patient level. Although predictive models have previously been constructed for a few important diseases, such as breast cance…
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…
Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network t…
The distribution of health care payments to insurance plans has substantial consequences for social policy. Risk adjustment formulas predict spending in health insurance markets in order to provide fair benefits and health care coverage for all enrollees, regardless of their health status. Unfortunately, current risk a…
Deep learning improves CVD risk prediction from health records.
Accurate prediction of suicide risk in mental health patients remains an open problem. Existing methods including clinician judgments have acceptable sensitivity, but yield many false positives. Exploiting administrative data has a great potential, but the data has high dimensionality and redundancies in the recording …
Early diagnosis is important for type 2 diabetes (T2D) to improve patient prognosis, prevent complications and reduce long-term treatment costs. We present a novel risk profiling approach based exclusively on health expenditure data that is available to Belgian mutual health insurers. We used expenditure data related t…
Risk prediction is central to both clinical medicine and public health. While many machine learning models have been developed to predict mortality, they are rarely applied in the clinical literature, where classification tasks typically rely on logistic regression. One reason for this is that existing machine learning…
Study uses machine learning to predict future health from various health data types.
Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records (EHRs) provide an alternative to the longitudinal cohort studies traditio…
Study identifies key health behaviors linked to adolescent suicide attempts.
Neural network predicts cardiovascular events from EHRs with high accuracy.
High quality risk adjustment in health insurance markets weakens insurer incentives to engage in inefficient behavior to attract lower-cost enrollees. We propose a novel methodology based on Markov Chain Monte Carlo methods to improve risk adjustment by clustering diagnostic codes into risk groups optimal for health ex…
Study uses machine learning to predict stroke risk in China with improved accuracy.
Model predicts wound and episode-level readmission risk and time to re-admit.
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…
BoXHED boosts hazard estimation for dynamic health risk scores.
Approach for modeling EHR data with rare features, improving prediction and interpretation.
Holdout set improves risk score accuracy without biasing predictions.
Study assesses health plan risk measures for Solvency Capital Requirement.
Risk adjustment has become an increasingly important tool in healthcare. It has been extensively applied to payment adjustment for health plans to reflect the expected cost of providing coverage for members. Risk adjustment models are typically estimated using linear regression, which does not fully exploit the informa…
Bayesian hypergraph inference models disease pathways from EHR data.
Model predicts cognitive health risks based on smartphone usage patterns.
Model predicts cannabis use disorder risk for adolescents and young adults.
The occurrence of drug-drug-interactions (DDI) from multiple drug dispensations is a serious problem, both for individuals and health-care systems, since patients with complications due to DDI are likely to reenter the system at a costlier level. We present a large-scale longitudinal study (18 months) of the DDI phenom…
Study examines impact of fairness penalties on clinical risk prediction models.
New method improves prediction accuracy for low-risk patients in healthcare.
The paper addresses selection bias in conformal prediction for focal units.
We propose multivariate nonstationary Gaussian processes for jointly modeling multiple clinical variables, where the key parameters, length-scales, standard deviations and the correlations between the observed output, are all time dependent. We perform posterior inference via Hamiltonian Monte Carlo (HMC). We also prov…
The rapid growth of Electronic Health Records (EHRs), as well as the accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests and attentions. Recent progress in the design and applications of deep learning methods has shown promising results and is forcing massive changes in h…
More than two thirds of mental health problems have their onset during childhood or adolescence. Identifying children at risk for mental illness later in life and predicting the type of illness is not easy. We set out to develop a platform to define subtypes of childhood social-emotional development using longitudinal,…
AdaptHetero uses MLI to tailor EHR models for subgroup-specific predictions.
Adherence can be defined as "the extent to which patients take their medications as prescribed by their healthcare providers"[Osterberg and Blaschke, 2005]. World Health Organization's reports point out that, in developed countries, only about 50% of patients with chronic diseases correctly follow their treatments. Thi…
System predicts HIV patients at risk of dropping out of care.
Risk-based active learning improves SHM decision-making.
Managing patients with chronic diseases is a major and growing healthcare challenge in several countries. A chronic condition, such as diabetes, is an illness that lasts a long time and does not go away, and often leads to the patient's health gradually getting worse. While recent works involve raw electronic health re…
Proposes a copula-based filter for diabetes risk prediction.
Paper proposes a method to model health outcomes using varying-coefficients and KNN-based LASSO.
Deep learning models predict postoperative complications more accurately than random forests.
Two new estimators reduce costs and improve accuracy for EHR outcome prediction.
Deep neural network predicts health costs better than traditional models.
MLHO predicts COVID-19 adverse outcomes using past medical records.