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.
Effective modeling of electronic health records (EHR) is rapidly becoming an important topic in both academia and industry. A recent study showed that using the graphical structure underlying EHR data (e.g. relationship between diagnoses and treatments) improves the performance of prediction tasks such as heart failure…
Proposes a method to derive knowledge graphs from EHR data.
Electronic health records (EHR) are increasingly being used for constructing disease risk prediction models. Feature engineering in EHR data however is challenging due to their highly dimensional and heterogeneous nature. Low-dimensional representations of EHR data can potentially mitigate these challenges. In this pap…
Study proposes a model to improve patient subtyping from EHR data.
Novel imputation method for EHRs with structured and sporadic missingness.
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…
This paper reviews methods for discovering patient subgroups from EHR data.
Paper proposes multimodal contrastive learning for EHR data.
ME2Vec learns medical entity vectors from EHR data.
WEST uses EHRs and expert cases to improve rare disease phenotyping.
Study proposes a multimodal model for cardiovascular risk prediction using EHRs.
This work generates synthetic EHRs with privacy guarantees for machine learning tasks.
PEHRT harmonizes EHR data for translational research.
New method generates diverse EHR data types while maintaining privacy.
Framework harmonizes EHR data across institutions for better analysis.
Deep learning models exhibit state-of-the-art performance for many predictive healthcare tasks using electronic health records (EHR) data, but these models typically require training data volume that exceeds the capacity of most healthcare systems. External resources such as medical ontologies are used to bridge the da…
Study finds multi-task learning and pre-training can improve healthcare models on EHR data.
Extracting information from electronic health records (EHR) is a challenging task since it requires prior knowledge of the reports and some natural language processing algorithm (NLP). With the growing number of EHR implementations, such knowledge is increasingly challenging to obtain in an efficient manner. We address…
Predicts EHR features that improve prediction, learning coherent topics.
Neural TPPs improve EHR modelling efficiency.
Electronic health records (EHRs) have contributed to the computerization of patient records and can thus be used not only for efficient and systematic medical services, but also for research on biomedical data science. However, there are many missing values in EHRs when provided in matrix form, which is an important is…
CEDAR efficiently analyzes distributed EHR data without sharing patient-level info.
Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. However, learning in a clinical setting presents unique challenges that complicate the use of common machine learning methodologies. For example…
Databases of electronic health records (EHRs) are increasingly used to inform clinical decisions. Machine learning methods can find patterns in EHRs that are predictive of future adverse outcomes. However, statistical models may be built upon patterns of health-seeking behavior that vary across patient subpopulations, …
Approach for modeling EHR data with rare features, improving prediction and interpretation.
Electronic health records (EHR) data provide a cost and time-effective opportunity to conduct cohort studies of the effects of multiple time-point interventions in the diverse patient population found in real-world clinical settings. Because the computational cost of analyzing EHR data at daily (or more granular) scale…
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…
Paper proposes a graph network for EHR data that learns robust representations.
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…
Deep models predict suicidal ideation from EHR and EMA data.
Proposes statistical inference for dependency knowledge graphs from EHR data.
Information in electronic health records (EHR), such as clinical narratives, examination reports, lab measurements, demographics, and other patient encounter entries, can be transformed into appropriate data representations that can be used for downstream clinical machine learning tasks using representation learning. L…
VBphenoR uses variational Bayes for EHR-based patient phenotyping.
Causal thinking improves healthcare decisions from EHRs.
ODVICE augments EHR cohorts using ontology to improve analysis robustness.
A variety of methods existing for generating synthetic electronic health records (EHRs), but they are not capable of generating unstructured text, like emergency department (ED) chief complaints, history of present illness or progress notes. Here, we use the encoder-decoder model, a deep learning algorithm that feature…
Doctor2Vec learns doctor representations from EHRs for better clinical trial recruitment.
FUALA improves Federated Learning for EHR data, enhancing model uncertainty.
The increased adoption of Electronic Health Records(EHRs) has brought changes to the way the patient care is carried out. The rich heterogeneous and temporal data space stored in EHRs can be leveraged by machine learning models to capture the underlying information and make clinically relevant predictions. This can be …
New model captures patient-level EHR data efficiently.
The widespread availability of electronic health records (EHRs) promises to usher in the era of personalized medicine. However, the problem of extracting useful clinical representations from longitudinal EHR data remains challenging. In this paper, we explore deep neural network models with learned medical feature embe…
OMTL uses ontology to learn from imbalanced EHR data.
Kernel TCK_IM tackles missing data in EHR time series, improving analysis.
BERT-XML automates ICD coding from EHR notes using BERT pretraining.
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…
Develops algorithm to find subgroups with different treatment effects in HIV patients.
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…
Deep learning model creates patient representations for scalable EHR-based stratification.