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.
ConCare personalizes healthcare predictions by capturing EMR features.
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 examines biases in clinical word embeddings, revealing performance gaps across groups.
Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research. For this reason, the 2010 i2b2/VA Natural Language Processing Challenges for Clinical Records introduced a concept extraction task aimed at identifying and classifying concepts into predefined categories (i.…
Proposes clustering as a new evaluation method for clinical knowledge embedding.
Framework predicts patient risk progression over time.
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…
Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however is obtaining meaningful insights from high dimensional, sp…
Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however is obtaining meaningful insights from high dimensional, sp…
Mapping and translating professional but arcane clinical jargons to consumer language is essential to improve the patient-clinician communication. Researchers have used the existing biomedical ontologies and consumer health vocabulary dictionary to translate between the languages. However, such approaches are limited b…
ECS evaluates synthetic CXR images' distributional fidelity.
Paper proposes multimodal contrastive learning for EHR data.
Proposes a method to derive knowledge graphs from EHR data.
Deep learning predicts heart failure readmission from clinical notes.
Predicting the risk of mortality for patients with acute myocardial infarction (AMI) using electronic health records (EHRs) data can help identify risky patients who might need more tailored care. In our previous work, we built computational models to predict one-year mortality of patients admitted to an intensive care…
CoI framework models clinical feature interactions, revealing temporal dependencies and enhancing transparency.
SARD improves deep learning clinical prediction performance.
Interventional cancer clinical trials are generally too restrictive, and some patients are often excluded on the basis of comorbidity, past or concomitant treatments, or the fact that they are over a certain age. The efficacy and safety of new treatments for patients with these characteristics are, therefore, not defin…
Key features of mental illnesses are reflected in speech. Our research focuses on designing a multimodal deep learning structure that automatically extracts salient features from recorded speech samples for predicting various mental disorders including depression, bipolar, and schizophrenia. We adopt a variety of pre-t…
A framework visualizes embedding spaces of neural survival analysis models using anchor directions.
Improved 3D ECG feature attributions for clinical interpretation.
Model predicts patient outcomes from EHR data by limiting feature interactions.
Neural model predicts survival outcomes and reveals feature relationships.
Enhances medical code predictions for multi-morbidity patients using text classification.
The volume of stroke lesion is the gold standard for predicting the clinical outcome of stroke patients. However, the presence of stroke lesion may cause neural disruptions to other brain regions, and these potentially damaged regions may affect the clinical outcome of stroke patients. In this paper, we introduce the t…
The paper predicts diseases using both clinical and genomics data.
Capturing sentence semantics plays a vital role in a range of text mining applications. Despite continuous efforts on the development of related datasets and models in the general domain, both datasets and models are limited in biomedical and clinical domains. The BioCreative/OHNLP organizers have made the first attemp…
Study improves mortality prediction in hospital patients using comprehensive feature engineering.
The recent adoption of Electronic Health Records (EHRs) by health care providers has introduced an important source of data that provides detailed and highly specific insights into patient phenotypes over large cohorts. These datasets, in combination with machine learning and statistical approaches, generate new opport…
With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic pr…
TrialGraph uses graph machine learning to improve clinical trial design and predict side effects.
Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease. This task is challenging due to factors such as low signal-to-noise ratios, signal artefacts, high variance in seizure semiology among epileptic patients, a…
In the integrative analyses of omics data, it is often of interest to extract data representation from one data type that best reflect its relations with another data type. This task is traditionally fulfilled by linear methods such as canonical correlation analysis (CCA) and partial least squares (PLS). However, infor…
The study compares machine learning models for depression detection and highlights the importance of feature selection.
Clinical notes are a rich source of information about patient state. However, using them to predict clinical events with machine learning models is challenging. They are very high dimensional, sparse and have complex structure. Furthermore, training data is often scarce because it is expensive to obtain reliable labels…
Proposes statistical inference for dependency knowledge graphs from EHR data.
Method augments CTNs for ICD coding with neural network imputation.
A new method uses Gaussian Processes for feature-based nonrigid image registration.
Study uses interviews to automatically detect BD and BPD with good accuracy.
AdaCare learns health status from biomarkers across multiple time scales.
Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events usi…
A new hybrid federated learning algorithm for combining clinical and omics data.
Improved language model for French clinical reports achieves state-of-the-art performance in medical NLP tasks.
A new method uses asymmetric Shapley values to assess gene importance in clinical prediction models.
Self-supervised learning improves EEG signal analysis without labeled data.
Machine learning improves EEG pathology classification.
Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing. In this article, we present a new set of embeddings for medical concepts learned using an extremely large collection of multimodal medical data. Leaning on recent theoretical insigh…
Framework learns structural and functional brain network embeddings while preserving their properties.