{"payload":{"pageCount":1,"repositories":[{"type":"Public","name":"stable-prediction","owner":"AIMedLab","isFork":true,"description":"","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":1,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-01-24T15:26:18.443Z"}},{"type":"Public","name":"Drug_Similarity","owner":"AIMedLab","isFork":false,"description":"","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":0,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2023-07-07T16:03:22.644Z"}},{"type":"Public","name":"MultimodalPred","owner":"AIMedLab","isFork":true,"description":"","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":1,"forksCount":2,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2023-05-01T05:31:45.159Z"}},{"type":"Public","name":"T4","owner":"AIMedLab","isFork":true,"description":"Code for paper \"Estimating Trustworthy Treatment Effects for Antibiotic Stewardship in Sepsis\"","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":2,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2023-03-06T14:50:23.895Z"}},{"type":"Public","name":"FATDM","owner":"AIMedLab","isFork":true,"description":"Fairness and Accuracy Transfer by Density Matching","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":1,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2023-02-28T22:21:44.425Z"}},{"type":"Public","name":"IPTW-TBI","owner":"AIMedLab","isFork":true,"description":"","allTopics":[],"primaryLanguage":{"name":"R","color":"#198CE7"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":1,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2022-09-08T22:03:32.514Z"}},{"type":"Public","name":"DeepDRiD","owner":"AIMedLab","isFork":true,"description":"Datasets for the paper \"DeepDRiD: Diabetic Retinopathy—Grading and Image Quality Estimation Challenge\", published on Patterns 2022.","allTopics":["deep-learning","medical-imaging","diabetic-retinopathy","fundus-image","deepdrid"],"primaryLanguage":null,"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":18,"license":"Creative Commons Attribution Share Alike 4.0 International","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2022-05-21T02:41:12.743Z"}},{"type":"Public","name":"TAME","owner":"AIMedLab","isFork":false,"description":"Code and Datasets for the paper \"Identifying Sepsis Subphenotypes via Time-Aware Multi-ModalAuto-Encoder\", published on KDD 2020.","allTopics":["electronic-health-record","auto-encoder","sepsis","data-imputation","patient-subtyping"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":1,"issueCount":0,"starsCount":0,"forksCount":2,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2022-05-09T03:52:14.633Z"}},{"type":"Public","name":"FAME","owner":"AIMedLab","isFork":false,"description":"Code for the paper \"FAME: Fragment-based Conditional Molecular Generation for Phenotypic Drug Discovery\", published on SDM 2022.","allTopics":["variational-autoencoder","conditional-generation","contrastive-learning","molecular-generation","phenotypic-drug-discovery","chemical-induced-gene-expression"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":1,"starsCount":6,"forksCount":2,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2022-03-16T13:58:43.435Z"}},{"type":"Public","name":"DAC","owner":"AIMedLab","isFork":true,"description":"Code and Datasets for the paper \"Deconfounding actor-critic network with policy adaptation for dynamic treatment regimes\", published on KDD 2022.","allTopics":["reinforcement-learning","mimic-iii","electronic-health-record","mechanical-ventilation","causal-reinforcement-learning","actor-critic-model","dynamic-treatment-regimens"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":2,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2022-02-20T04:29:44.910Z"}},{"type":"Public","name":"CAT-LSTM","owner":"AIMedLab","isFork":true,"description":"Code and Datasets for the paper \"Predicting Age-Related Macular Degeneration Progression with Contrastive Attention and Time-Aware LSTM\", published on KDD 2022.","allTopics":["deep-learning","amd","medical-imaging","fundus-image","age-related-macular-degeneration","patient-subtyping","disease-progression-model"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":2,"forksCount":1,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2022-02-18T16:22:28.694Z"}},{"type":"Public","name":"CIGER","owner":"AIMedLab","isFork":false,"description":"Code and Datasets for the paper \"Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing\", published on Patterns in 2022.","allTopics":["deep-learning","l1000","drug-repurposing","learning-to-rank","graph-neural-network","chemical-induced-gene-expression"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":3,"forksCount":1,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2022-02-14T00:30:23.471Z"}},{"type":"Public","name":"Age-Risk-Identification","owner":"AIMedLab","isFork":false,"description":"Code and Datasets for the paper \"A Computational Framework for Identifying Age Risks in Drug-Adverse Event Pairs\", published on AMIA 2022 Informatics Summit.","allTopics":["drug-safety","pharmacovigilance","faers","adverse-drug-events"],"primaryLanguage":{"name":"R","color":"#198CE7"},"pullRequestCount":0,"issueCount":0,"starsCount":1,"forksCount":0,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2022-01-07T18:56:00.665Z"}},{"type":"Public","name":"MuViTaNet","owner":"AIMedLab","isFork":false,"description":"Code for the paper \"Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learning\", published on ICDM 2021.","allTopics":["electronic-health-record","multi-task-learning","multi-view-learning","risk-prediction","cardiac-complication","cancer-survivor"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":0,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2021-10-01T15:28:42.648Z"}},{"type":"Public","name":"TC-EMNet","owner":"AIMedLab","isFork":false,"description":"Code for the paper \"Temporal clustering with external memory network for disease progression modeling\". Accepted by ICDM 2021.","allTopics":["electronic-health-record","alzheimers-disease","parkinsons-disease","disease-progression-model","disease-staging","temporal-clustering"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":0,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2021-09-29T02:40:59.736Z"}},{"type":"Public","name":"CCMDR","owner":"AIMedLab","isFork":false,"description":"Code for the paper \"Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases\". Accepted by BMC Medical Informatics and Decision Making, 2021","allTopics":["drug-repurposing","electronic-health-record","real-world-evidence","nhanes","drug-repositioning"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":3,"forksCount":0,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2021-08-01T14:16:08.456Z"}},{"type":"Public","name":"TransICD","owner":"AIMedLab","isFork":false,"description":"Code and Datasets for the paper \"TransICD: Transformer Based Code-wise Attention Model for Explainable ICD Coding\", accepted by AIME 2021.","allTopics":["transformer","clinical-notes","interpretable-deep-learning","icd-coding"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":5,"forksCount":1,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2021-03-29T15:27:29.223Z"}},{"type":"Public","name":"BAGAU-Net","owner":"AIMedLab","isFork":false,"description":"Code and Datasets for the paper \"Brain atlas guided attention u-net for white matter hyperintensity segmentation\", published on AMIA 2021 Informatics Summit.","allTopics":["deep-learning","mri","medical-imaging","magnetic-resonance-imaging","u-net","wmh-segmentation","white-matter-hyperintensities","cerebral-small-vessel-disease"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":1,"starsCount":6,"forksCount":0,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2020-10-21T13:40:51.211Z"}},{"type":"Public","name":"ecg-diagnosis","owner":"AIMedLab","isFork":false,"description":"Code and Datasets for the paper \"Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram\", published on iScience in 2021.","allTopics":["interpretability","cardiac-arrhythmia","ecg","electrocardiogram","interpretable-deep-learning"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":1,"starsCount":11,"forksCount":2,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2020-10-21T03:28:34.551Z"}},{"type":"Public","name":"DSW","owner":"AIMedLab","isFork":false,"description":"Code and Datasets for the paper \"Estimating Individual Treatment Effects with Time-Varying Confounders\", published on ICDM 2020.","allTopics":["deep-learning","time-varying-confounding","electronic-health-record","causal-inference","sepsis","individual-treatment-effects","deep-sequential-weighting"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":1,"starsCount":3,"forksCount":2,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2020-09-05T14:56:51.187Z"}},{"type":"Public","name":"DGViz","owner":"AIMedLab","isFork":false,"description":"Code and Datasets for the paper \"DG-Viz: Deep Visual Analytics with Domain Knowledge Guided Recurrent Neural Networks on Electronic Health Records\", published on Journal of Medical Internet Research (JMIR) in 2020.","allTopics":["data-visualization","electronic-health-record","heart-failure","interpretable-deep-learning"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":3,"forksCount":0,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2020-08-21T17:59:59.424Z"}},{"type":"Public","name":"DeepCE","owner":"AIMedLab","isFork":false,"description":"Code and Datasets for the paper \"A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing\", published on Nature Machine Intelligence in 2021.","allTopics":["l1000","drug-repurposing","multi-head-attention","graph-neural-network","covid-19","phenotypic-drug-discovery","chemical-induced-gene-expression"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":3,"forksCount":2,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2020-08-16T18:09:59.046Z"}},{"type":"Public","name":"LP-SDA","owner":"AIMedLab","isFork":false,"description":"Code and Datasets for the paper \"Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports\", published on BMC Medical Informatics and Decision Making in 2019.","allTopics":["signal-detection","label-propagation","pharmacovigilance","adverse-drug-reaction","faers"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":4,"forksCount":1,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2020-08-16T01:34:54.746Z"}},{"type":"Public","name":"PAVE","owner":"AIMedLab","isFork":false,"description":"Code and Datasets for the paper \"An Interpretable Risk Prediction Model for Healthcare with Pattern Attention\", published on BMC Medical Informatics and Decision Making.","allTopics":["electronic-health-record","interpretable-deep-learning","value-embedding","sepsis","mortality-prediction","pattern-attention"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":4,"forksCount":1,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2020-08-16T01:08:59.217Z"}},{"type":"Public","name":"DG-RNN","owner":"AIMedLab","isFork":false,"description":"Code and Datasets for the paper \"Domain Knowledge Guided Deep Learning with Electronic Health Records\", published on ICDM 2019.","allTopics":["knowledge-graph","electronic-health-record","heart-failure","interpretable-deep-learning"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":2,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2020-08-16T00:44:17.918Z"}},{"type":"Public","name":"DeepIPW","owner":"AIMedLab","isFork":false,"description":"Code and Datasets for the paper \"A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data\", published on Nature Machine Intelligence in 2021.","allTopics":["deep-learning","drug-repurposing","causal-inference","real-world-evidence","clinical-trial-emulation"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":3,"forksCount":1,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2020-08-15T15:26:45.047Z"}},{"type":"Public","name":"clinical-fusion","owner":"AIMedLab","isFork":false,"description":"Code and Datasets for the paper \"Combining structured and unstructured data for predictive models: a deep learning approach\", published on BMC Medical Informatics and Decision Making in 2020.","allTopics":["electronic-health-record","data-fusion","time-series-forecasting","deep-learning"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":1,"starsCount":16,"forksCount":8,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2020-08-15T03:44:52.028Z"}},{"type":"Public","name":"DII-Challenge","owner":"AIMedLab","isFork":true,"description":"One of the top solutions for The 2019 DII National Data Science Challenge: <a href=\"https://sbmi.uth.edu/dii-challenge/\" rel=\"nofollow\">https://sbmi.uth.edu/dii-challenge/</a>. More details in the paper \"An interpretable deep-learning model for early prediction of sepsis in the emergency department\", published on Patterns 2021.","allTopics":["electronic-health-record","sepsis","interpretable-deep-learning","emergency-department","dii-challenge"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":9,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2020-07-31T18:00:13.047Z"}},{"type":"Public","name":"BioNEV","owner":"AIMedLab","isFork":true,"description":"Graph Embedding Evaluation / Code and Datasets for \"Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations\" ","allTopics":["link-prediction","protein-protein-interaction","graph-embedding","protein-function-prediction","node-classification","drug-drug-interaction","semantic-type-detection","drug-disease-associations"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":1,"forksCount":76,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2019-09-19T21:25:25.469Z"}}],"repositoryCount":29,"userInfo":null,"searchable":true,"definitions":[],"typeFilters":[{"id":"all","text":"All"},{"id":"public","text":"Public"},{"id":"source","text":"Sources"},{"id":"fork","text":"Forks"},{"id":"archived","text":"Archived"},{"id":"template","text":"Templates"}],"compactMode":false},"title":"AIMedLab repositories"}