21 skills found
groue / GRDB.swiftA toolkit for SQLite databases, with a focus on application development
groue / GRDBQueryThe SwiftUI companion for GRDB
OHDSI / PatientLevelPredictionAn R package for performing patient level prediction in an observational database in the OMOP Common Data Model.
OHDSI / CohortMethodAn R package for performing new-user cohort studies in an observational database in the OMOP Common Data Model.
ODM2 / ODMToolsPythonODMTools is a python application for managing observational data using the Observations Data Model. ODMTools allows you to query, visualize, and edit data stored in an Observations Data Model (ODM) database.ODMTools was originally developed as part of the CUAHSI Hydrologic Information System.
apecs-org / Polar EO DatabasePolar Earth Observation Database of satellite sensors
dataplumber / NexusNEXUS is an emerging data-intensive analysis framework developed with a new approach for handling science data that enables large-scale data analysis. It takes on a different approach in handling array-based observational temporal, geospatial artifacts by fully leveraging the elasticity of Cloud Computing environment. Rather than performing on-the-fly file I/Os, NEXUS stores tiled data in Cloud-scaled databases with high-performance spatial lookup service. NEXUS is also packaged with a suite of science data analytic web services that are developed using Apache Spark.
OHDSI / DeepPatientLevelPredictionAn R package for performing patient level prediction using deep learning in an observational database in the OMOP Common Data Model.
OHDSI / SelfControlledCaseSeriesAn R package for performing Self-Controlled Case Series (SCCS) analyses in an observational database in the OMOP Common Data Model.
Clinical-Genomics / LoqusdbA simple observation count database
dogsheep / Inaturalist To SqliteCreate a SQLite database containing your observation history from iNaturalist
Benyaminhosseiny / NSDL4EOA database of over 500 published papers on Earth Observation with Remote Sensing data using Non-Supervised Deep Learning techniques, classified by their learning methods (Un-, Semi-, Self-, Transfer-, and Weakly-Supervised), Applications, Data, etc.
ankitsingh1240 / CUSTOMER CHURN PREDICTIONINSAIDINSTRUCTIONS:You are required to come up with the solution of the given business case.Business Context:This case requires trainees to develop a model for predicting customer churn at a fictitious wireless telecom company and use insights from the model to develop an incentive plan for enticing would-be churners to remain with company.Data for the case are available in csv format. The data are a scaled down version of the full database generously donated by an anonymous wireless telephone company. There are still 7043 customers in the database, and 20 potential predictors. Candidates can use whatever method they wish to develop their machine learning model. The data are available inone data file with 7043 rows that combines the calibration and validation customers. “calibration” database consisting of 4000customers and a “validation” database consisting of 3043customers. Each database contained (1) a “churn” variable signifying whether the customer had left the company two months after observation, and (2) a set of 20 potential predictor variables that could be used in a predictive churn model. Following usual model development procedures, the model would be estimated on the calibration data and tested on the validation data.
DHEEPAK29 / Project ML IoTObjective : To help Doctors (or) Supervisors remotely monitor the Condition of the ambiance, Health condition, and the proximity from the set location of a Patient (or) Person under observation. This project imbibes the concept of the Internet of Things (IoT) and so the data is accessible seamlessly even if the supervisor is remote. Using range detection techniques and health parameters, Manipulations are done in the backend such that the alerts are notified based on set conditions to the respective person in case of emergency, we can conclusively predict the condition of a Patient (or) Person under observation remotely and accurately. Further, the data received from a patient is integrated into the database for analytics in Machine Learning (ML) to predict the reaction of another patient who suffers from the same disease or condition in the future. In addition, the product is feasible to be designed as a Handy and User-Friendly prototype, Cost-Efficient model, Less power-consuming mechanism, and Alterable Design.
ucuapps / Robust DL Pipeline For PVC LocalizationPremature ventricular contraction(PVC) is among the most frequently occurring types of arrhythmias. Along with other cardiovascular diseases, it may easily cause hazardous health conditions, making PVC detection task extremely important in cardiac care. However, the long-term nature of monitoring, sophisticated morphological features, and patient variability makes the manual observation of PVC an impractical task. Existing approaches for automated PVC identification suffer from a range of disadvantages. These include domain-specific handcrafted features, usage of manually delineated R peaks locations, tested on a tiny sample of PVC beats(usually a small subset of MIT-BIH database). We address some of these drawbacks in proposed framework, which takes a raw ECG signal as an input and localizes R peaks of the PVC beats. It consists of two neural networks. The first one is an encoder-decoder architecture that localizes the R peak of both Normal and anomalous heartbeats. Provided R peaks positions, our CardioIncNet model, adopted for ECG signal data, does the delineation of healthy versus PVC bits. We have performed the extensive evaluation of our pipeline with both single- and cross-dataset paradigms on three public datasets. Our approach results in over 0.99 and 0.979 F1-measure on both single- and cross-dataset paradigms for R peaks localization task and above 0.96 and 0.85 F1 score for the PVC beats classification task.
OHDSI / CaseControlAn R package for performing (nested) matched case-control analyses in an observational database in the OMOP Common Data Model.
AlejandroRuete / IgnoranceMapsSimple algorithms to display ignorance maps of raw distributional data accessed from species observation databases
OHDSI / CaseCrossoverAn R package for performing case-crossover and case-time-control analyses in an observational database in the OMOP Common Data Model.
ohdsi-studies / CervelloCovid-19 pandEmic impacts on mental health Related conditions Via multi-database nEtwork: a LongitutinaL Observational study (CERVELLO)
nerc-comet / Tien Shan Active Fault DatabaseTien Shan Active Fault Database is a multi-functional digital collection of active faults which integrates decades of mapping and field studies in Central Asia by researchers from the UK Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) alongside global collaborators.
