arXiv research

Study predicts high-cost patients using insurance claims data.

Monitoring patients in ICU is a challenging and high-cost task. Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources. There has been continuous progress in machine learning research for ICU management, and most of this work has focused on…

Simulation framework assesses ROI of chronic disease adherence and policy timing.

Improves disease progression prediction using auxiliary surrogate labels and health markers.

The multimodal web elements such as text and images are associated with inherent memory costs to store and transfer over the Internet. With the limited network connectivity in developing countries, webpage rendering gets delayed in the presence of high-memory demanding elements such as images (relative to text). To ove…

The paper develops a method to learn robust decision policies from observational data, reducing high-cost outcomes.

Late diagnosis and high costs are key factors that negatively impact the care of cancer patients worldwide. Although the availability of biological markers for the diagnosis of cancer type is increasing, costs and reliability of tests currently present a barrier to the adoption of their routine use. There is a pressing…

In this paper we introduce the ice-start problem, i.e., the challenge of deploying machine learning models when only little or no training data is initially available, and acquiring each feature element of data is associated with costs. This setting is representative for the real-world machine learning applications. Fo…

Improved forecasting of suicide attempts using LSGPs for patients with little data.

Efficiently fine-tunes patient-independent seizure detection models with tensor kernel machine.

Personalized models explain TB treatment outcomes considering patient context.

Simulates patient pathways to detect delayed rare disease diagnoses.

Model predicts wound and episode-level readmission risk and time to re-admit.

Currently, approximately 30% of epileptic patients treated with antiepileptic drugs (AEDs) remain resistant to treatment (known as refractory patients). This project seeks to understand the underlying similarities in refractory patients vs. other epileptic patients, identify features contributing to drug resistance acr…

Cluster analysis aims at separating patients into phenotypically heterogenous groups and defining therapeutically homogeneous patient subclasses. It is an important approach in data-driven disease classification and subtyping. Acute coronary syndrome (ACS) is a syndrome due to sudden decrease of coronary artery blood f…

The health outcomes of high-need patients can be substantially influenced by the degree of patient engagement in their own care. The role of care managers includes that of enrolling patients into care programs and keeping them sufficiently engaged in the program, so that patients can attain various goals. The attainmen…

System detects multiple patients' behaviors in real-time using mmWave radar and CNN.

Deep learning model creates patient representations for scalable EHR-based stratification.

Characterizing a patient's progression through stages of sepsis is critical for enabling risk stratification and adaptive, personalized treatment. However, commonly used sepsis diagnostic criteria fail to account for significant underlying heterogeneity, both between patients as well as over time in a single patient. W…

We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs. Heterogeneity of the patients population is captured via a hierarchical latent class model. The proposed algorithm aims t…

Longitudinal patient data has the potential to improve clinical risk stratification models for disease. However, chronic diseases that progress slowly over time are often heterogeneous in their clinical presentation. Patients may progress through disease stages at varying rates. This leads to pathophysiological misalig…

Study automates detection of visitation disruptions in ICU patients.

Bayesian methods improve group testing for identifying infected patients.

Paper predicts IVF pregnancy rates from basic patient info.

Model identifies key problems in HIV patients' records.

Evaluating the clinical similarities between pairwise patients is a fundamental problem in healthcare informatics. A proper patient similarity measure enables various downstream applications, such as cohort study and treatment comparative effectiveness research. One major carrier for conducting patient similarity resea…

This paper presents efficient sampling methods for Gaussian processes.

Study develops electronic phenotypes of ICU patient acuity.

Deep learning clusters patient time-series data for better prognosis.

Multi-output Gaussian processes (GPs) are a flexible Bayesian nonparametric framework that has proven useful in jointly modeling the physiological states of patients in medical time series data. However, capturing the short-term effects of drugs and therapeutic interventions on patient physiological state remains chall…

Over the past decades, both critical care and cancer care have improved substantially. Due to increased cancer-specific survival, we hypothesized that both the number of cancer patients admitted to the ICU and overall survival have increased since the millennium change. MIMIC-III, a freely accessible critical care data…

Machine learning detects NASH patients from medical claims data.

Many computational models were proposed to extract temporal patterns from clinical time series for each patient and among patient group for predictive healthcare. However, the common relations among patients (e.g., share the same doctor) were rarely considered. In this paper, we represent patients and clinicians relati…

The paper tracks patient recovery using graphs of joint movement data.

Model predicts HU response for sickle cell patients.

Enhances understanding of patient healthcare journeys using self-attention.

Paper proposes a new method for more accurate group testing of infected patients.

New model identifies patient-specific disease root causes.

Rare diseases affecting 350 million individuals are commonly associated with delay in diagnosis or misdiagnosis. To improve those patients' outcome, rare disease detection is an important task for identifying patients with rare conditions based on longitudinal medical claims. In this paper, we present a deep learning m…

Deep learning model predicts severe COVID-19 outcomes.

Language models improve clinical prediction models using EHR data.

Adaptive prediction timing improves healthcare outcomes by predicting patient events at the right frequency.

Study predicts 10-year survival rates for breast cancer patients.

In the scenario of real-time monitoring of hospital patients, high-quality inference of patients' health status using all information available from clinical covariates and lab tests is essential to enable successful medical interventions and improve patient outcomes. Developing a computational framework that can learn…

Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life. We describe a …

Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients' privacy, Protected Health Information (PHI) must be removed from the patient notes before they can be legally released, a process known as patient note de-identification. The main objectiv…

Machine learning predicts trauma patient mortality risk.

Study predicts risk of true-lumen narrowing after ATAAD surgery using CT data.