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.
Machine learning constructs problem-based medical records from electronic health records.
Accurate real-time monitoring systems of influenza outbreaks help public health officials make informed decisions that may help save lives. We show that information extracted from cloud-based electronic health records databases, in combination with machine learning techniques and historical epidemiological information,…
XLabel tool reduces medical experts' workload by 40% and explains its decisions.
The paper presents a systematic review of state-of-the-art approaches to identify patient cohorts using electronic health records. It gives a comprehensive overview of the most commonly de-tected phenotypes and its underlying data sets. Special attention is given to preprocessing of in-put data and the different modeli…
Analyzing electronic health records (EHR) poses significant challenges because often few samples are available describing a patient's health and, when available, their information content is highly diverse. The problem we consider is how to integrate sparsely sampled longitudinal data, missing measurements informative …
This paper presents an example of how demographical characteristics of patients influence their susceptibility to certain medical conditions. In this paper, we investigate the association of health conditions to age of patients in a heterogeneous population. We show that besides the symptoms a patients is having, the a…
Deep learning predicts drug prescriptions across global health records.
AI predicts medical specialty diagnostic choices from EHR records.
AdaptHetero uses MLI to tailor EHR models for subgroup-specific predictions.
Electronic health records (EHR) are rich heterogeneous collection of patient health information, whose broad adoption provides great opportunities for systematic health data mining. However, heterogeneous EHR data types and biased ascertainment impose computational challenges. Here, we present mixEHR, an unsupervised g…
Develops algorithm to find subgroups with different treatment effects in HIV patients.
Neural network predicts cardiovascular events from EHRs with high accuracy.
Deep learning predicts opioid use disorder risk in patients.
The past decade has seen an explosion in the amount of digital information stored in electronic health records (EHR). While primarily designed for archiving patient clinical information and administrative healthcare tasks, many researchers have found secondary use of these records for various clinical informatics tasks…
New method generates diverse EHR data types while maintaining privacy.
Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes. This process is often constrained by having a relatively small number of patient records for training the model. We demonstrate that using patient repres…
Cardea automates machine learning for EHRs, improving model building efficiency.
Federated learning opens a number of research opportunities due to its high communication efficiency in distributed training problems within a star network. In this paper, we focus on improving the communication efficiency for fully decentralized federated learning over a graph, where the algorithm performs local updat…
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…
Neural TPPs improve EHR modelling efficiency.
Enhancing spectral embedding for low-dimensional embeddings in rare disease cohorts
We show how to learn low-dimensional representations (embeddings) of patient visits from the corresponding electronic health record (EHR) where International Classification of Diseases (ICD) diagnosis codes are removed. We expect that these embeddings will be useful for the construction of predictive statistical models…
Identifying patients who will be discharged within 24 hours can improve hospital resource management and quality of care. We studied this problem using eight years of Electronic Health Records (EHR) data from Stanford Hospital. We fit models to predict 24 hour discharge across the entire inpatient population. The best …
Study assesses weakly-supervised methods for rare outcomes in medical records.
This study improves AKI prediction precision using CNN on EHR data.
The wide implementation of electronic health record (EHR) systems facilitates the collection of large-scale health data from real clinical settings. Despite the significant increase in adoption of EHR systems, this data remains largely unexplored, but presents a rich data source for knowledge discovery from patient hea…
Electronic phenotyping is the task of ascertaining whether an individual has a medical condition of interest by analyzing their medical record and is foundational in clinical informatics. Increasingly, electronic phenotyping is performed via supervised learning. We investigate the effectiveness of multitask learning fo…
Electronic Health Record (EHR) data can be represented as discrete counts over a high dimensional set of possible procedures, diagnoses, and medications. Supervised topic models present an attractive option for incorporating EHR data as features into a prediction problem: given a patient's record, we estimate a set of …
Effective representation learning of electronic health records is a challenging task and is becoming more important as the availability of such data is becoming pervasive. The data contained in these records are irregular and contain multiple modalities such as notes, and medical codes. They are preempted by medical co…
Electronic health records are an increasingly important resource for understanding the interactions between patient health, environment, and clinical decisions. In this paper we report an empirical study of predictive modeling of several patient outcomes using three state-of-the-art machine learning methods. Our primar…
New method uses probabilistic independence to discover disease signatures from medical records.
Develops methods to learn optimal treatment regimes using causal tree methods.
ODVICE augments EHR cohorts using ontology to improve analysis robustness.
Framework harmonizes EHR data across institutions for better analysis.
Novel framework detects CKD in diabetic patients using sparse EHR representations.
New research finds uncertainty estimation techniques fail to reliably detect abnormal medical cases.
VBphenoR uses variational Bayes for EHR-based patient phenotyping.
PEHRT harmonizes EHR data for translational research.
AACE learns treatment policies from EHRs using annotations to improve accuracy.
Proposes statistical inference for dependency knowledge graphs from EHR data.
Generative Adversarial Networks (GANs) represent a promising class of generative networks that combine neural networks with game theory. From generating realistic images and videos to assisting musical creation, GANs are transforming many fields of arts and sciences. However, their application to healthcare has not bee…
Paper proposes multimodal contrastive learning for EHR data.
Binary PheNorm extends phenotype labeling for EHRs using binary silver labels.
Federated learning for Bayesian clustering of large datasets.
Deep learning models have exhibited superior performance in predictive tasks with the explosively increasing Electronic Health Records (EHR). However, due to the lack of transparency, behaviors of deep learning models are difficult to interpret. Without trustworthiness, deep learning models will not be able to assist i…
Privacy-preserving synthetic data from EHRs for learning and inference.
New method uses surrogate outcomes and single-record data to improve suicide risk modeling.
Novel unsupervised scheme for highly imbalanced and overlapping datasets.