Papers Patient Phenotyping
“Patient Phenotyping” 태그가 달린 논문 17편 · 필터 해제
A variational Bayes latent class approach for EHR-based patient phenotyping in R
The VBphenoR package for R provides a closed-form variational Bayes approach to patient phenotyping using Electronic Health Records (EHR) data. We implement a variational Bayes Gaussian Mixture Model (GMM) algorithm usin…
Patient PhenotypingDANIEL: A Distributed and Scalable Approach for Global Representation Learning with EHR Applications
Classical probabilistic graphical models face fundamental challenges in modern data environments, which are characterized by high dimensionality, source heterogeneity, and stringent data-sharing constraints. In this work…
Representation LearningPatient PhenotypingDiscovering multiple antibiotic resistance phenotypes using diverse top-k subgroup list discovery
Antibiotic resistance is one of the major global threats to human health and occurs when antibiotics lose their ability to combat bacterial infections. In this problem, a clinical decision support system could use phenot…
Data MiningDecision MakingDiverse Top-k Subgroup List DiscoveryPatient Phenotyping+1Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements
Catheter ablation of Atrial Fibrillation (AF) consists of a one-size-fits-all treatment with limited success in persistent AF. This may be due to our inability to map the dynamics of AF with the limited resolution and co…
Patient PhenotypingM3H: Multimodal Multitask Machine Learning for Healthcare
Developing an integrated many-to-many framework leveraging multimodal data for multiple tasks is crucial to unifying healthcare applications ranging from diagnoses to operations. In resource-constrained hospital environm…
Binary ClassificationPatient PhenotypingTime SeriesCollaborative learning of common latent representations in routinely collected multivariate ICU physiological signals
In Intensive Care Units (ICU), the abundance of multivariate time series presents an opportunity for machine learning (ML) to enhance patient phenotyping. In contrast to previous research focused on electronic health rec…
Collaborative FilteringPatient PhenotypingTime SeriesPain Forecasting using Self-supervised Learning and Patient Phenotyping: An attempt to prevent Opioid Addiction
Sickle Cell Disease (SCD) is a chronic genetic disorder characterized by recurrent acute painful episodes. Opioids are often used to manage these painful episodes; the extent of their use in managing pain in this disorde…
ClusteringDecision MakingPatient PhenotypingSelf-Supervised Learning+1POPDx: An Automated Framework for Patient Phenotyping across 392,246 Individuals in the UK Biobank Study
Objective For the UK Biobank standardized phenotype codes are associated with patients who have been hospitalized but are missing for many patients who have been treated exclusively in an outpatient setting. We describe …
Patient PhenotypingA methodology based on Trace-based clustering for patient phenotyping
Background: The current situation of critical progression as regards the resistance of bacteria to antibiotics has led to the use of machine learning techniques in order to provide clinicians with new knowledge for deci…
ClusteringData MiningDecision MakingPatient PhenotypingHiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Data
The recent success of machine learning methods applied to time series collected from Intensive Care Units (ICU) exposes the lack of standardized machine learning benchmarks for developing and comparing such methods. Whil…
BIG-bench Machine LearningCirculatory FailureICU MortalityKidney Function+5Towards dynamic multi-modal phenotyping using chest radiographs and physiological data
The healthcare domain is characterized by heterogeneous data modalities, such as imaging and physiological data. In practice, the variety of medical data assists clinicians in decision-making. However, most of the curren…
Decision MakingPatient PhenotypingTemporal Phenotyping using Deep Predictive Clustering of Disease Progression
Due to the wider availability of modern electronic health records, patient care data is often being stored in the form of time-series. Clustering such time-series data is crucial for patient phenotyping, anticipating pat…
ClusteringDecision MakingPatient PhenotypingTime Series+2A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients
A crucial step within secondary analysis of electronic health records (EHRs) is to identify the patient cohort under investigation. While EHRs contain medical billing codes that aim to represent the conditions and treatm…
Patient PhenotypingBenchmarking machine learning models on multi-centre eICU critical care dataset
Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established …
BenchmarkingBIG-bench Machine LearningDecompensationMortality Prediction+1Actor-Critic Approach for Temporal Predictive Clustering
Due to the wider availability of modern electronic health records (EHR), patient care data is often being stored in the form of time-series. Clustering such time-series data is crucial for patient phenotyping, anticipati…
ClusteringDecision MakingPatient PhenotypingTime Series+2Visualization of Emergency Department Clinical Data for Interpretable Patient Phenotyping
Visual summarization of clinical data collected on patients contained within the electronic health record (EHR) may enable precise and rapid triage at the time of patient presentation to an emergency department (ED). The…
Decision MakingDimensionality ReductionPatient PhenotypingComparing Rule-Based and Deep Learning Models for Patient Phenotyping
Objective: We investigate whether deep learning techniques for natural language processing (NLP) can be used efficiently for patient phenotyping. Patient phenotyping is a classification task for determining whether a pat…
Deep LearningPatient Phenotyping