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Epidemiology
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Epidemiology is a scientific discipline that provides reliable knowledge for clinical medicine focusing on prevention, diagnosis and treatment of diseases. Research in Epidemiology aims at characterizing risk factors for the outbreak of diseases and at evaluating the efficiency of certain treatment strategies, e.g., to compare a new treatment with an established gold standard. This research is strongly hypothesis-driven and statistical analysis is the major tool for epidemiologists so far. Correlations between genetic factors, environmental factors, life style-related parameters, age and diseases are analyzed.
Source: Visual Analytics of Image-Centric Cohort Studies in Epidemiology
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/feizhe/HDCQR_Paper
공식 코드https://github.com/flowersteam/EpidemiOptim
공식 코드EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models
https://github.com/francescopinotti92/Competition-Cooperation
공식 코드Interplay between competitive and cooperative interactions in a three-player pathogen system
https://github.com/frb-yousefi/aggregated-multitask-gp
공식 코드Multi-task Learning for Aggregated Data using Gaussian Processes
https://github.com/fuzihaofzh/band
공식 코드https://github.com/gabriel-turinici/epidemiology_heterogeneous_gamma
공식 코드The impact of recovery rate heterogeneity in achieving herd immunity
https://github.com/github-usr-ano/temporal_graph_data_pdes
공식 코드Synthetic Datasets for Machine Learning on Spatio-Temporal Graphs using PDEs
https://github.com/huangdaolang/cost-aware-sbi
공식 코드https://github.com/isds-neu/percnn
공식 코드https://github.com/jesperhauch/polynomial_deep_learning
공식 코드Learning and Generalizing Polynomials in Simulation Metamodeling
https://github.com/jgehrcke/covid-19-germany-gae
공식 코드https://github.com/johnnardini/forecasting_predicting_abms
공식 코드https://github.com/jonpappalord/crowd_flow_prediction
공식 코드Enhancing crowd flow prediction in various spatial and temporal granularities
https://github.com/juaml/confoundleakage
공식 코드Confound-leakage: Confound Removal in Machine Learning Leads to Leakage
https://github.com/kslote1/hui-walter
공식 코드Online Performance Estimation with Unlabeled Data: A Bayesian Application of the Hui-Walter Paradigm
https://github.com/ktxlh/TrajGPT
공식 코드https://github.com/lanl-ansi/cholera-meta-population-model
공식 코드Congruity of genomic and epidemiological data in modeling of local cholera outbreaks
https://github.com/mackelab/markovsbi
공식 코드https://github.com/mackelab/simformer
공식 코드https://github.com/mad-stat/parnn
공식 코드Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting
https://github.com/maksimchup/Markovian-Projection-in-filtering-for-SRNs
공식 코드https://github.com/michaelplanknz/interacting-random-walk-models
공식 코드Random walk models in the life sciences: including births, deaths and local interactions
https://github.com/ml4ai/scenario-context
공식 코드When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context
https://github.com/monkeygobah/bone-metastasis
공식 코드Prediction of Lung Metastasis from Hepatocellular Carcinoma using the SEER Database
https://github.com/mrc-ide/covid-sim
공식 코드https://github.com/mrc-ide/referenced-TI
공식 코드https://github.com/mspakkanen/integral-equations
공식 코드Unifying incidence and prevalence under a time-varying general branching process
https://github.com/ngmlgroup/koopman-tgnn-interpretability
공식 코드Interpreting Temporal Graph Neural Networks with Koopman Theory
https://github.com/nkdinsdale/challenges_review
공식 코드Challenges for machine learning in clinical translation of big data imaging studies
https://github.com/nsaph-software/cre
공식 코드Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects
https://github.com/ottotabell/clustering-pruning
공식 코드https://github.com/pholme/exact-importance
공식 코드Three faces of node importance in network epidemiology: Exact results for small graphs
https://github.com/robert-graf/totalvibesegmentator
공식 코드TotalVibeSegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank
https://github.com/rose-stl-lab/hierarchical-neural-processes
공식 코드https://github.com/sandialabs/Bayesian-calibration-of-stochastic-agent-based-model-via-random-forest
공식 코드Bayesian calibration of stochastic agent based model via random forest
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.