{"id":37642552,"url":"https://github.com/simula-complex/medet","last_synced_at":"2026-01-16T11:20:17.691Z","repository":{"id":198844421,"uuid":"698565882","full_name":"Simula-COMPLEX/MeDeT","owner":"Simula-COMPLEX","description":"Medical Devices Digital Twins with Meta-Learning","archived":false,"fork":false,"pushed_at":"2024-09-08T15:13:51.000Z","size":41,"stargazers_count":4,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-09-04T19:46:48.194Z","etag":null,"topics":["digital-twins","healthcare","iot-applications","meta-learning"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Simula-COMPLEX.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2023-09-30T09:46:52.000Z","updated_at":"2025-01-16T14:32:59.000Z","dependencies_parsed_at":"2025-04-11T10:39:50.154Z","dependency_job_id":"c5b5c637-36f2-45ba-ab3c-c68a13150167","html_url":"https://github.com/Simula-COMPLEX/MeDeT","commit_stats":null,"previous_names":["simula-complex/medet"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Simula-COMPLEX/MeDeT","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Simula-COMPLEX%2FMeDeT","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Simula-COMPLEX%2FMeDeT/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Simula-COMPLEX%2FMeDeT/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Simula-COMPLEX%2FMeDeT/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Simula-COMPLEX","download_url":"https://codeload.github.com/Simula-COMPLEX/MeDeT/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Simula-COMPLEX%2FMeDeT/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28478202,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-16T06:30:42.265Z","status":"ssl_error","status_checked_at":"2026-01-16T06:30:16.248Z","response_time":107,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["digital-twins","healthcare","iot-applications","meta-learning"],"created_at":"2026-01-16T11:20:17.132Z","updated_at":"2026-01-16T11:20:17.686Z","avatar_url":"https://github.com/Simula-COMPLEX.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MeDeT: Medical Devices Digital Twins Generation with Meta-learning\n\nThe MeDeT approach focuses on building, adapting, and operating high-fidelity digital twins (DTs) of medical devices, employing few-shot meta-learning techniques. These medical devices DTs are designed to streamline testing automation for healthcare IoT applications. \n\nMeDeT works in six phases: (i) _Data Generation_ - generates raw data for medical devices, (ii) _Data Preparation_ - preprocesses raw data for training, (iii) _Meta-learning_ - creates meta dataset \u0026 taskset, determines model architecture, and trains/fine-tunes with MAML algorithm, (iv) _Build DTs_ - creates model clones, storage, APIs, and JSON objects, (v) _DT Request Handler_ - processes requests from a healthcare IoT application during testing, and (vi) _DTs to Device\nCommunication_ - establishes DTs communication with medical devices. \n\nThis work is a part of the Welfare Technology Solution (WTT4Oslo) project (#309175) funded by the Research Council of Norway.\n\n\n\n\n\n\n[//]: # (The repository contains open-source implementation)\n\n\n## Basic Requirements\n\n* Machine: minimum 16GB RAM and 8-core processor \n* OS: MacOS or Windows 10\n* IDE: PyCharm\n* Python: 3.8 or higher \n\n## Dependencies\n\n* PyTorch: 2.0.1\n* learn2learn: 0.2.0\n* scikit-learn: 1.3.0\n* Pandas: 2.0.3\n* Flask: 2.2.3\n* Flask-RESTful: 0.3.9\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsimula-complex%2Fmedet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsimula-complex%2Fmedet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsimula-complex%2Fmedet/lists"}