{"id":24102144,"url":"https://github.com/rubynixx/process_mining_python_healthcare","last_synced_at":"2026-02-06T04:02:28.855Z","repository":{"id":271629325,"uuid":"914062645","full_name":"RubyNixx/Process_Mining_Python_Healthcare","owner":"RubyNixx","description":"Example python notebook \u0026 resources to get started using the pm4py library.","archived":false,"fork":false,"pushed_at":"2025-01-10T11:40:59.000Z","size":4938,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-12T01:39:26.015Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"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/RubyNixx.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-08T21:54:44.000Z","updated_at":"2025-01-10T11:41:02.000Z","dependencies_parsed_at":null,"dependency_job_id":"139ec2e9-2136-4e26-980d-a77000feb84b","html_url":"https://github.com/RubyNixx/Process_Mining_Python_Healthcare","commit_stats":null,"previous_names":["rubynixx/process_mining_python_healthcare"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/RubyNixx/Process_Mining_Python_Healthcare","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RubyNixx%2FProcess_Mining_Python_Healthcare","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RubyNixx%2FProcess_Mining_Python_Healthcare/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RubyNixx%2FProcess_Mining_Python_Healthcare/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RubyNixx%2FProcess_Mining_Python_Healthcare/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/RubyNixx","download_url":"https://codeload.github.com/RubyNixx/Process_Mining_Python_Healthcare/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RubyNixx%2FProcess_Mining_Python_Healthcare/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29149590,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-06T02:39:25.012Z","status":"ssl_error","status_checked_at":"2026-02-06T02:37:22.784Z","response_time":59,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: 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":[],"created_at":"2025-01-10T17:36:15.970Z","updated_at":"2026-02-06T04:02:28.824Z","avatar_url":"https://github.com/RubyNixx.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"**Introduction**\n\npm4py is a python library that supports (state-of-the-art) process mining algorithms in python. It is open source (licensed under GPL) and intended to be used in both academia and industry projects. pm4py is a product of the Fraunhofer Institute for Applied Information Technology.\n\nThis repo contains links to process mining training, links to supporting documentation to support with installing libraries \u0026 how to use the library, and an example python notebook that you can run with the artifical healthcare data provided.\n\n**Presentation**\n\nI presented on process mining in the East of England at a few different communities, presentation below:\n\n[Presentation](https://github.com/RubyNixx/Process_Mining_Python_Healthcare/blob/main/Process_Mining.pdf)\n\n**Recommended training in process mining**\n\n[Coursera Process Mining](https://www.coursera.org/learn/process-mining)\n\n**Process Mining in Python - Youtube Videos**\n\n[pm4py tutorials - tutorial #1: What is Process Mining?](https://www.youtube.com/watch?v=XLHtvt36g6U)\nThis video covers what is process mining; examples, definition of process mining, event log, main tasks of process mining, process discovery, conformance checking, process enhancement\n\n[pm4py tutorials - tutorial #2: Importing CSV Files](https://www.youtube.com/watch?v=bWOKVx0PO6g)\nThis video covers example process; how to read graphical representation of processes, example data in CSV format #(can be downloaded [here](https://processintelligence.solutions/static/data/getting_started/running-example.csv), importing CSV data in Python using pandas library, importing CSV data, reformatting the data into event log using format_dataframe function and obtaining start and end activities using get_start_activities and get_end_activities functions from pm4py library.\n\n[pm4py tutorials - tutorial #3: Importing XES Files](https://youtu.be/pmpN3A_h2sQ)\nThis video covers case level attributes, XES format; tools supporting XES format, how XES looks, example XES file, XES - extensions, standard extensions of XES (website), extensions on log level, trace level and event level, XES public datasets, globals (default values) in XES files, classifiers; meta information in XES files, reading XES files using read_xes function from pm4py library and getting start and end activities.\n\n[pm4py tutorials - tutorial #4: Playing with Event Data; Lambda Functions](https://www.youtube.com/watch?v=48p_LP0c3g8\u0026list=PLkWuoFn9UEb5l41T4CMKPYHyRcL5ojI9Z\u0026index=4)\n\n[pm4py tutorials - tutorial #5: Playing with Event Data; Shipped Filters](https://www.youtube.com/watch?v=alkZkhK2mAo\u0026list=PLkWuoFn9UEb5l41T4CMKPYHyRcL5ojI9Z\u0026index=5)\n\n[pm4py tutorials - tutorial #6 exporting event data](https://www.youtube.com/watch?v=gVnfG6xLIxI\u0026list=PLkWuoFn9UEb5l41T4CMKPYHyRcL5ojI9Z\u0026index=6)\n\n[pm4py tutorials - tutorial #7 process discovery](https://www.youtube.com/watch?v=BJMp763Ye_o\u0026list=PLkWuoFn9UEb5l41T4CMKPYHyRcL5ojI9Z\u0026index=7)\n\n[pm4py tutorials - tutorial #8 conformance checking](https://www.youtube.com/watch?v=0YNvijqX3FY\u0026list=PLkWuoFn9UEb5l41T4CMKPYHyRcL5ojI9Z\u0026index=8)\n\n**Resources Available:**\n\n***Official pm4py***\n\n[pm4py official documentation](https://processintelligence.solutions/static/api/2.7.11/index.html)\n\n[Process Intelligence website](https://processintelligence.solutions/pm4py)\n\n[pm4py installation support](https://processintelligence.solutions/static/api/2.7.11/install.html)\n\n[YouTube Videos](https://www.youtube.com/embed/XLHtvt36g6U)\n\n[Process Mining - Data Science in Action Book](https://link.springer.com/book/10.1007/978-3-662-49851-4)\n\n***dcr4py - supporting documentation***\n\n[dcr4pydocs - extension of pm4py documentation](https://paul-cvp.github.io/dcr4pydocs/api.html#overall-list-of-methods)\n\n\n[pm4py-dcr](https://github.com/paul-cvp/pm4py-dcr)\n\n**Example Publications using pm4py**\n\nhttps://processintelligence.solutions/pm4py/publications\n\n**Structure of this repo**\n\nPython Notebook - open in google colab\nExample artificial healthcare data\n\n**Additional example datasets for healthcare \u0026 process mining**\n\n[Ambulance Data](https://github.com/nhsengland/ProcessMining/tree/main/Data)\n\nReal life log of a Dutch academic hospital, originally intended for use in the first Business Process Intelligence Contest (BPIC 2011) \nUploaded to this repo.\n\n[Data.XML](https://github.com/RubyNixx/Process_Mining_Python_Healthcare/blob/main/DATA(1).xml)\n[Hospital_log.xes.gz](https://github.com/RubyNixx/Process_Mining_Python_Healthcare/blob/main/Hospital_log.xes.gz)\n\n**Requirements**\n\npm4py depends on some other Python packages, with different levels of importance:\n\nEssential requirements: numpy, pandas, deprecation, networkx\n\nNormal requirements (installed by default with the pm4py package, important for mainstream usage): graphviz, intervaltree, lxml, matplotlib, pydotplus, pytz, scipy, stringdist, tqdm\n\nOptional requirements (not installed by default): scikit-learn, pyemd, pyvis, jsonschema, polars, openai, pywin32, python-dateutil, requests, workalendar\n\nSource: https://github.com/paul-cvp/pm4py-dcr\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frubynixx%2Fprocess_mining_python_healthcare","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frubynixx%2Fprocess_mining_python_healthcare","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frubynixx%2Fprocess_mining_python_healthcare/lists"}