Saravanan666/pypi-org huggingface dataset:
Package: medmodels
MedModelsUsing High-End Machine Learning to Enhance Medical Data AnalysesTable of ContentsWhy do you need MedModels?What is MedModels?Who is MedModels aimed at?What does MedModels offer?How do you get MedModels?Why do you need MedModels?The use of medical data in connection with AI is a rapidly growing field of research.
However, there is a significant gap between the methodology that is published in scientific papers and the techniques that are used in the medical industry. Currently, companies have to adapt the latest findings to their individual set-up. With MedModels, we close this gap by offering all users an intuitive Python framework that provides the methods from current research publications in a directly usable manner.What is MedModels?MedModels is a Python-based software framework for the analysis of real-world evidence data for the healthcare sector. MedModels makes complex analyses and predictions based on medical data significantly faster, more precise, reliable, and more cost-effective.The vision is to combine the key expertise of research companies and science in order to gain the greatest possible benefit for patients from the data. With MedModels, we close the clear innovation gap between academic research and industrial application by providing the latest scientific methods as an application-oriented framework.Who is MedModels aimed at?MedModels is aimed at a wide range of users, including medical care institutions (e.g., clinics and hospitals), research institutions (e.g., universities and cancer registers), insurance companies (e.g., health insurance and accident insurance), pharmaceutical companies as well as regulatory institutions such as drug administrations.What does MedModels offer?Treatment Effect EstimationTreatment effect estimations are used to compare the effects of treatment and control groups in non-experimental observational studies.Patient MatchingStatistical methods as well as innovative machine learning algorithms help identify similar patients in treatment and control groups to account for confounding variables.Medical Data SynthesisGenerative synthetic patient data closes data gaps and makes representative patient data available while ensuring data privacy.Medical Concept EmbeddingsMedical concept embeddings pre-process medical raw data into compact representations that depict temporal and causal relationships of the concepts (e.g., diagnosis, treatment, medications, ...).Predictive ModelingMachine learning models predict individual patient-level risks (e.g., diagnostics, events, treatment chances, ...) based on EHR data.Explainable AICounterfactual explanations and other techniques make black box forecasts comprehensible and interpretable.How do you get MedModels?Limebit hosts the official open source code on GitHub at:MedModels GitHub RepositoryWe recommend to usepipto install the latest version of MedModels:pip install medmodelsFor detailed information on how to use MedModels, please read theMedModels documentation.
Package: mednoise
mednoiseSummarymednoise is the fundamental package needed for medical image (pre)processing with Python.Website:https://mednoise.github.ioPyPI:https://pypi.org/project/mednoise/Documentation:https://mednoise.github.io/documentationSource code:https://github.com/mednoise/mednoiseBug reports:https://github.com/mednoise/mednoise/issuesIt contains:various algorithms to handle and pre-process large amounts of medical image metadataa highly tuneable interface for a variety of  usecasesan inference-compatible structure that allows for easy integration into workflow/pipeline management systems (ex. airflow, luigi, snakemake)Installation:mednoise usespip.  The installation can be run with:pip install mednoiseLicensemednoise is licensed under theApache 2.0 License
Package: mednsktest
https://github.com/idlesign/mednsktestЧто этоТесты с курсов НГМУ. Консольное приложение.При помощи этого приложения можно готовиться к тестам, являющимся частью программ обучения медработников,
проводящимся на базе Новосибирского Госудраственного Медицинского Университета.В комплекте следующие наборы тестов:1. Ультразвуковая диагностикаТесты 2013 года.Подтверждённая актуальность: январь 2014.Имя вопросника: 201401_uzd2. Актуальные вопросы акушерства и гинекологииТесты 2012 года.Подтверждённая актуальность: февраль 2014.Имя вопросника: 201402_gyne3. Ультразвуковая диагностикаТесты 2018 года.Подтверждённая актуальность: декабрь 2018.Имя вопросника: 201812_uzdКак установить$pipinstallmednsktestКак запуститьПриложение запускается из консоли при помощи команды:$mednskteststart<имя_вопросника>Например, запускаем вопросник по УЗИ:$mednsktest201401_uzdПо умолчанию выдаются 50 вопросов, выбранных случаным образом из общего количества, в случайном порядке.Количество вопросов можно ограничить при помощи ключа--qlimit.Поддерживаются  и другие ключи. Помощь по ключам, которые принимает приложение, можно получить так:$mednskteststart--helpДополнительная информацияФайлы с тестами - обычные текстовые файлы, которые можно распечатать. Хранятся они в директории с приложением.Приложение умеет разбирать тесты из файлов, если текст в них отформатирован следующим образом:#Вопрос 2
Ультразвук - это звук, частота которого не ниже:
#Варианты к вопросу 2
№ 1. 15 кГц;
№ 2. 20000 Гц;
№ 3. 1 МГц;
№ 4. 30 Гц;
№ 5. 20 Гц.
#Ответ 2Или так (правильный вариант ответа помечен*****):#Вопрос 2
Ультразвук - это звук, частота которого не ниже:
#Варианты к вопросу
№ 1. 15 кГц;
№ 2. 20000 Гц; *****
№ 3. 1 МГц;
№ 4. 30 Гц;
№ 5. 20 Гц.
#Ответ 0
Package: medpc2excel
medpc2excelmedpc2excel is a Python package for convert single or multiple medpc data file into Excels.
medpc2excel can also return a pandas DataFrame for further analysisInstallationI recommend installing anAnacondadistribution of Python -- Choose Python >=3.7.Use the package managerpipto install medpc2excel. Current version is 3.10.0.
It now allows to read both "*.txt" and non-extension files.
Fix the bug that read unexisting array from data file.
Remove mplcursor. The installation is no longer depended on mplcursor.pipinstallmedpc2excelTo upgrade medpc2excel (package here), run the following in the environment:pipinstallmedpc2excel--upgradeVersion updating noteAdd data explorer to visualize timestampe dataConfigure *.MPC filePlease include a medpc protocol file (*.MPC) that you used for behavior task.
The file name of this MPC file should be the same as in the medpc data file.
The medpc2excel will open the medpc data file and automatically search the used *.MPC file in the same directory.
In *.MPC file, please explicitly declare each array as below:<... your MPC code...>

    DIM C =9999  \ Levertype                     
    DIM D =9999  \ PelHLON                       
    DIM E =9999  \ PelHLOFF   

<... your MPC code ...>Running medpc2excelThe most quickest way to start is to open the GUi from a command line terminal in anaconda cmd prompt:python -m medpc2excelYou also can import this module and use the function called medpc_read as followingfrommedpc2excel.medpc_readimportmedpc_readf=<filepath>ts_df,log=medpc_read(f,override=True,replace=False)# return a timestamp dataframe and a log stringPlease download theExamplefolder to your local disk and run themedpc2excel_example.pyto give a try.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriateDependenciesPandasNumpydillmplcursorsopenpyxl
Package: medplexity
MedplexityMedplexity explorer•Frontend GitHub repository•SubstackMedplexity is a python library to help with evaluation of LLMs for medical applications.It is designed to help with the following tasks:Evaluating performance of LLMs on existing medical datasets and benchmarks. E.g. MedQA, PubMedQA, etc.Comparing performance of different prompts, models, and architectures.Exporting results of evaluation for visualisation and further analysis.The goal is to help answer questions like "How much better would GPT-4 perform given a vector database to load certain resources?".🔧 Quick installpipinstallmedplexity📖 DocumentationDocumentation can be foundhere.ExampleSee our"Getting Started" notebookfor a full example with MedMCQA dataset.ContributionsContributions are welcome! Check out the todos below, and feel free to open a pull request.
Remember to installpre-committo be compliant with our standards:pre-commitinstallFeel free to raise any questions onDiscordTodosSome initial todos include:Multiple-Choice datasetsAdd MedMCQA datasetAdd PubMedQA datasetAdd MedQA datasetAdd MMLU datasetLong-form question answering datasetsAdd HealthSearchQA datasetAdd MedicationQA datasetAdd LiveQA datasetExplore datasets for multi-modality, specifically vision tasks for GPT-4V.LLMsWrapper for OpenAIWrapper for deepinfraWrapper for Google PALMWrapper for HuggingFace text-genJupyter notebook quickstartExample with langchain integrationVisualisation of resultsAdd export of evaluationsFrontend for exploring exported resultsExplorerIn addition to the library, we are also building a web app to explore the results of evaluations.
The explorer is available atmedplexityai.com.
It's also open-sourced, see thefrontend repository.📜 LicenseMedplexity is licensed under the MIT License. See the LICENSE file for more details.
Package: medpro
MEDPro (MED post PROcessing)Medcoupling based post processing toolsThis project has been created to simplify the manipulation ofMEDfiles.Code examples:import medpro

# Read a med file, extract mesh, field, group information
fp = medpro.MEDFilePost("./tests/examples/box_with_depl.rmed")
mesh = fp.meshes_by_name["mesh"]
g1 = mesh.get_group_by_name("G1")
depl_evol = fp.fieldevols_by_name["reslin__DEPL"]

# Extract a subpart of a field based on a group 
depl_g1 = depl_evol.extract_group("G1")

# Create a new MED file, write it back
fpnew = medpro.MEDFilePost()
fpnew.add_fieldevol(depl_g1)
fpnew.write("/tmp/output.rmed")
Package: medreaders
Readers for medical imaging datasetsThe goal of this project is to help researchers and practitioners working with medical imaging datasets to reduce an amount of routine work.The package contains the functions for reading a dataset into memory and for auxiliary tasks:resizing images with their ground truth masks;saving images and their ground truth masks slice by slice.In order to use this package you should download a dataset that you need fromGrand Challenges in Biomedical Image Analysis.First time the focus will be on datasets for cardiac image segmentation problem.Currently the package provides the means for working withACDC dataset.RequirementsPython>=3.5Installationpip3 install medreadersDocumentationDocumentation is available athttps://medreaders.readthedocs.ioProject Structure.
├── docs
│   ├── Makefile
│   ├── conf.py
│   ├── index.rst
│   └── make.bat
├── medreaders
│   ├── ACDC.py
│   └── __init__.py
├── tests
│   └── ACDC.py
├── .gitignore
├── LICENSE
├── README.md
├── requirements.txt
└── setup.py

3 directories, 12 filesCorresponding AuthorOlga Senyukovaolga.senyukova@graphics.cs.msu.ru
Package: meds
Medical Event Data StandardThe Medical Event Data Standard (MEDS) is a draft data schema for storing streams of medical events, often sourced from either Electronic Health Records or claims records.The core of the standard is that we define apatientdata structure that contains a series of time stamped events, that in turn contain measurements of various sorts.The Python type signature for the schema is as follows:Patient=TypedDict('Patient',{'patient_id':int,'events':List[Event],})Event=TypedDict('Event',{'time':datetime.datetime,'measurements':List[Measurement],})Measurement=TypedDict('Measurement',{'code':str,'text_value':NotRequired[str],'numeric_value':NotRequired[float],'datetime_value':NotRequired[datetime.datetime],'metadata':NotRequired[Mapping[str,Any]],})We also provide ETLs to convert common data formats to this schema:https://github.com/Medical-Event-Data-Standard/meds_etl
Package: medseglib
MEDSEGLIBThis package designed for segmentation of oncology by CT and MRI images.ModelsCurrently, the models DeepLabV3(bb: resnet50), DeepLabV3(bb: resnet101) are used.
During the training, these models were used, as well as UNet and TransUnet, but DeepLabV3 showed the best results (dice and iou).Supported types of researchBrainLiverHow to useimportnumpyasnpfrommegseglib.sdkimportModelct=np.zeros((1,3,512,512),dtype=np.single)model=Model(model_name="deeplab_v3_resnet101",model_type="liver")result=model.forward(ct)# orresult=model(ct)print(type(result),result.shape)# <class 'numpy.ndarray'> (1, 1, 512, 512) - segmentation mask
Package: med-signal
Med-Signal package for biological signals processing and simulationThis module focuses on the simulation and specific processing of known biological signals, such as EEG and ECG. This module is aimed for the study and processing of biological signals by the usage of known methods.ContentsMed-Signal package for biological signals processing and simulationContentsEEG moduleIzhikevich simulation moduleInstallationPackage RequirementsEEG moduleThis module contains the necesary actions for the processing and evaluation ofEEGsignals contained in anumpyarray. The main functionality of this module is to let the user process analyze and generate simulations ofEEGsignals in a single package. This module implements actions such as:Read and store the signal found from a valid.txt,.csvfile containing the signal.Calculate the main frequencies and relevant information from the signal.Retrieve the relevant information and calculate the wave type based on Polar Statistics.Izhikevich simulation moduleThis module focuses on generating a model in which the user can use the Izhikevich neurons and visualize their behavior either as asingle unitor as anetwork of multiple units. The simulation includes settings such as input value, neuron positions andField Voltage Response, as well asSingle Voltage Response. The functionality of this module is based in three data-classes functioning as models to describe the neural response based on theIzhikevich Equationsfor neural response. The classes are listed as follow:NeuronTypes:Provides the neural constants which determine the behavior of the neuron model.Neuron:The actual neural model descrved by Izhikevich. An object with this type will represent a neuron model with constants provided by the the NeuronTypes class. This is the unit that will provide theSingle Voltage Responsewith a given time and input.Network:Represents a group ofNeuronsinterconnected with each other. An object with this type provides aField Voltage Responsewith its given neurons and connections.InstallationIn order to install and make use of this package, just enterpython -m pip install med-signaland you will be good to go! This package uses various dependencies for its optimal functions. These requirements are listed in thesection below.
In order to successfuly use this package, a set of requirements are needed for its usage. These requirements will be automatically installed or upgraded(if you already have one of the dependencies)as the package is installed. The minimum python version needed to correctly use this package ispython 3.10.The following list provides the required packages with their minimal required version needed to installMedSig.Pandas -1.4.1Matplotlib -3.5.1Scipy -1.8.0Numpy -1.22.3
Package: medspacy-io
medspacy-iomedspacy-io is a collection of modules to facilitate reading text from various sources and writing to various sources.Installationpip install medspacy-ioHow to usePlease refer to the samples in the tests.A Colab notebook demo is available at:https://colab.research.google.com/drive/12EBOaKej9uPmGAb5dV2oB1goEWrwXjSD
Package: medspacy-pna
MedspaCy Pneumonia NLPFlexible medspaCy NLP pipeline for detecting assertions of pneumonia in different clinical notes.OverviewThis package implements amedspaCypipeline for classifying pneumonia diagnoses
in 3 types of clinical notes:Emergency noteRadiology reportDischarge summaryThe system was primarily designed in Veterans Affairs and was then validated in
University of Utah. Rules designed for University of Utah's Epic EHR are provided and can be
added to the pipeline. Custom rules can be designed and added in a similar manner.
Seenotebooks/for detailed documentation and examples.QuickstartInstallationpipinstallmedspacy_pnaOr:pythonsetup.pyinstallYou will also need to install spaCy'sen_core_web_smmodel (or specify a different model
when loading the NLP)python-mspacydownloaden_core_web_smUsagefrommedspacy_pnaimportbuild_nlpfrommedspacy_pna.displayimportcreate_htmlfromIPython.displayimportHTML# Load a model specific to either "emergency", "radiology", or "discharge"nlp_emergency=build_nlp(domain="emergency")note_emergency="""History of Present Illness: Patient presents to ED with cough and fever.Medical decision making: Differential diagnoses include pna and CHF.Will order a chest x-ray to r/o pneumonia.Assessment/Plan:Admit for suspected community-acquired pneumonia."""doc_emergency=nlp_emergency(note_emergency)HTML(create_html(doc_emergency,"emergency",document_classification=True))
Package: medspacy-simstring
# SimString Python PackageOrginal version by [chokkan](https://github.com/chokkan/simstring). New version (compiles under macOS) from [here](https://github.com/blinkhealth/simstring-python-package)Windows/Anaconda/VisualStudio specific steps:Make sure that iconv is available to your desired conda environment:conda install -c conda-forge libiconvThen, open a Visual Studio Developer Prompt (e.g. “VS 2015 x64 Developer Command Prompt” or “VS 2015 x86 Developer Command Prompt”)Now you can any of these commands to build or install:python setup.py build && python setup.py installNOTE: If there is a failure thatrc.execannot be found, add the appropriate WindowKits binary path toPATH. More info on this [here](https://stackoverflow.com/questions/14372706/visual-studio-cant-build-due-to-rc-exe).
Package: medstat
medstat is a library aiming to make basic hypothesis testing as simple as
possible.Getting startedThis project is available on PyPI you can just:pip install medstatQuick ExampleLoad your data in a dataframe using for instancepd.read_csv()orpd.read_excel().data=pd.read_csv("my_data.csv")Test a single hypothesis:>>>medstat.test_hypothesis(data,'sex','age < 30'){'contingency_table':age<30FalseTrueAllsexFemale262248Male24832All503080,'test':'Fisher','p-value':0.06541995357625573,'significant':False}Or test many hypothesis at the same time:result=medstat.analyse_dataset(data,[('sex','age < 30'),('sex','test_a'),('test_a','age > 50'),])It prints the output:-------------------- Test 1 --------------------
Test independence between sex and age < 30.
Use Chi-squared test.
Result is not significant.
p-value: 0.18407215636751517
Contingency table:
 age < 30  False  True  All
sex
Female       21    18   39
Male         29    12   41
All          50    30   80


-------------------- Test 2 --------------------
Test independence between sex and test_a.
Use Chi-squared test.
Result is not significant.
p-value: 0.9539453144224308
Contingency table:
 test_a  negative  positive  All
sex
Female        25        14   39
Male          25        16   41
All           50        30   80


-------------------- Test 3 --------------------
Test independence between test_a and age > 50.
Use Fisher test.
Result is significant.
p-value: 6.392910983822276e-12
Contingency table:
 age > 50  False  True  All
test_a
negative     46     4   50
positive      5    25   30
All          51    29   80You can also save it to a text file using the file argument.result = medstat.analyse_dataset(data,
                                 [('sex', 'age < 30'),
                                  ('sex', 'test_a'),
                                  ('test_a', 'age > 50'),
                                 ],
                                file='report.txt')Free software: MIT licenseDocumentation:https://medstat.readthedocs.io.
Package: med-terms
Med-TermsMedical Term Dictionary built from MedDRAInstallationpip install med-termsLet Codes SpeakExample 1: Build term dicionaries from MedDRA data in English and Chinese versions, and creating term mapping file.importpicklefrommedterms.MedDRAimport*if__name__=="__main__":# create the dictionary from the MedDRA knowledge base including Chinese and English versionsmeddra_en_root="input/MedDRA_24.0_English_0"meddra_cn_root="input/MedDRA_24_0_Chinese"build_en_dict_from_MedDRA(meddra_en_root,save_path="output/en_dict.pickle")build_cn_dict_from_MedDRA(meddra_cn_root,save_path="output/cn_dict.pickle")# Mapping from english terms to chinese terms according to MedDRA dictionariesen_to_cn(src_en_path='input/covid19_en_terms.pickle',# inputen_dict_path='output/en_dict.pickle',cn_dict_path='output/cn_dict.pickle',# inputtarget_cn_path='output/covid19_cn_terms.pickle',target_en2cn_path='output/covid19_en2cn_terms.pickle')# output# check data files (pickle format)print(pickle.load(open('input/covid19_en_terms.pickle','rb')))print(pickle.load(open('output/covid19_cn_terms.pickle','rb')))print(pickle.load(open('output/covid19_en2cn_terms.pickle','rb')))Example 2: Show word cloud from the entire dictionary or part of the dictionary.frommedterms.wordcloudimport*if__name__=="__main__":src_file="input/covid19_symptoms.pickle"mapping_file="output/covid19_en2cn_terms.pickle"# Mapping from english to chinese# show_word_cloud_with_mapping(src_file,mapping_file)show_word_cloud(src_file)LicenseThemed-termsproject is provided byDonghua Chen.NOTE: This project does not include MedDRA data due to license issue.
Package: medularis-django-utils
Medularis’ open source utilities for use in Django projects.This project is a fork oflu-dj-utils.LicenseThis software is licensed under the OSI-approved “BSD License”. To avoid
confusion with the original BSD license from 1990, the FSF refers to it as
“Modified BSD License”. Other names include “New BSD”, “revised BSD”, “BSD-3”,
or “3-clause BSD”.See the included LICENSE.txt file.Run testsAt the project root directory run:$pythonsetup.pytestTo test the project in all the supported Python environments run:$tox
Package: medusa-network
Medusa NetworkMedusa Network LibraryFeaturesTODOCreditsThis package was created with cookiecutter and modified by Andrea Mucci <andrea@breda.build>History0.0.1 (2020-06-21)First release.0.0.2 (2020-06-22)Modified call to Medusa Protocol package.Added support of tests for TCP Server0.0.3 (2020-06-23)Added UDP Server.Added Client for TCP Server callAdded Client for UDP Server callAdded Tests for TCP and UDP Server
Package: meduse-CERTIC
MeduseOutil en ligne de commande destiné à réaliser des copies statiques des projets du PDN (Pôle Document Numérique).Pré-requisPython 3.6 ou plus récentwgetInstallation hors développementpip install meduse-CERTICPrincipe3 étapes:Aspiration des sites avec wget.Modification éventuelle des pages.Indexation des pages, dans Elasticsearch si présent.UtilisationSuivre les instructions affichées avecmeduse --helpExempleAspirer un site dans le répertoire courant:meduse mirror http://acme.com/Aspirer un site dans un autre répertoire:meduse mirror http://acme.com/ -d /home/johndoe/sites/acmeInstallation de l'environnement pour le développement développement$> python3 -m venv venv
$> . ./venv/bin/activate
$> pip install -r requirements.txt
Package: medussa
Medussa is an easy to use high-level cross-platform audio library for Python
based on Port Audio and libsndfile. You can play NumPy arrays, stream sound
files of various formats from disk, or create pure tones or ‘on-line’ white
or pink noise. There are high-level functions like play_array (similar to
matlab’s old wavplay function but with more features). Or you can access
specific host api’s or devices, create streams as needed, and control them
all asynchronously. Or for the most control, you can access the port audio
library directly. Sweet!