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https://github.com/leandromineti/awesome-healthmetrics

A curated list of awesome resources at the intersection of healthcare and AI
https://github.com/leandromineti/awesome-healthmetrics

List: awesome-healthmetrics

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A curated list of awesome resources at the intersection of healthcare and AI

Awesome Lists containing this project

README 

        
# Awesome Healthmetrics [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)



A curated list of awesome resources at the intersection of healthcare and AI. 



**Table of Contents**  *generated with [DocToc](https://github.com/thlorenz/doctoc)*



- [Awesome Healthmetrics *](#awesome-healthmetrics-)

  - [General](#general)

    - [Articles](#articles)

    - [Books](#books)

    - [Courses](#courses)

    - [Datasets](#datasets)

    - [Packages](#packages)

    - [Competitions](#competitions)

    - [Conferences and events](#conferences-and-events)

  - [Cardiology](#cardiology)

    - [Articles](#articles-1)

    - [Books](#books-1)

    - [Datasets](#datasets-1)

    - [Packages](#packages-1)

    - [Competitions](#competitions-1)

  - [Epidemiology & infectology](#epidemiology--infectology)

    - [Articles](#articles-2)

    - [Books](#books-2)

    - [Datasets](#datasets-2)

    - [Packages](#packages-2)

    - [Competitions](#competitions-2)

  - [Genetics](#genetics)

    - [Articles](#articles-3)

    - [Books](#books-3)

    - [Datasets](#datasets-3)

    - [Packages](#packages-3)

    - [Competitions](#competitions-3)

    - [Conferences and events](#conferences-and-events-1)

  - [Neurology](#neurology)

    - [Articles](#articles-4)

    - [Books](#books-4)

    - [Datasets](#datasets-4)

    - [Packages](#packages-4)

    - [Competitions](#competitions-4)

    - [Conferences and events](#conferences-and-events-2)

  - [Oncology](#oncology)

    - [Articles](#articles-5)

    - [Competitions](#competitions-5)

  - [Ophthalmology](#ophthalmology)

    - [Articles](#articles-6)

    - [Competitions](#competitions-6)

  - [Orthopedicts](#orthopedicts)

    - [Articles](#articles-7)

    - [Datasets](#datasets-5)

    - [Competitions](#competitions-7)

  - [Pharmacology](#pharmacology)

    - [Articles](#articles-8)

    - [Datasets](#datasets-6)

    - [Competitions](#competitions-8)

  - [Psychiatry](#psychiatry)

    - [Articles](#articles-9)

    - [Books](#books-5)

    - [Competitions](#competitions-9)

  - [Pulmonology](#pulmonology)

    - [Competitions](#competitions-10)

  - [Research](#research)

    - [Articles](#articles-10)

    - [Packages](#packages-5)

    - [Datasets](#datasets-7)

    - [Competitions](#competitions-11)



## General



### Articles



- [Representation Learning for Networks in Biology and Medicine: Advancements, Challenges, and Opportunities](https://arxiv.org/abs/2104.04883v1) - 2021

- [Adversarial attacks on medical AI: A health policy challenge](https://cyber.harvard.edu/story/2019-03/adversarial-attacks-medical-ai-health-policy-challenge) - 2019

- [Transfusion: Understanding Transfer Learning with Applications to Medical Imaging](https://arxiv.org/abs/1902.07208) - 2019

- [High-performance medicine: the convergence of human and artificial intelligence](https://www.nature.com/articles/s41591-018-0300-7) - 2019

- [Big data and machine learning in health care](https://www.dropbox.com/s/q1cixzmsdugq3vy/Beam_BigData_ML.pdf?dl=0) - 2018

- [Opportunities in Machine Learning for Healthcare](https://arxiv.org/abs/1806.00388) - 2018

- [A Survey on Deep Learning in Medical Image Analysis](https://arxiv.org/abs/1702.05747) - 2017

- [Translating Artificial Intelligence Into Clinical Care](https://www.dropbox.com/s/4o1va07tqwvrxsn/Beam_TranslatingAI_2016.pdf?dl=0) - 2016



### Books



- [Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again](https://www.amazon.com/Deep-Medicine-Artificial-Intelligence-Healthcare/dp/1541644638/ref=sr_1_1?ie=UTF8&qid=1547245092&sr=8-1)

- [The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age](https://www.amazon.com/Digital-Doctor-Hope-Medicines-Computer/dp/0071849467/ref=sr_1_1?ie=UTF8&qid=1547245287&sr=8-1)



### Courses



- [Machine Learning for Healthcare - MIT](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s897-machine-learning-for-healthcare-spring-2019/)



### Datasets



- [Awesome Bio Datasets](awesome-bio-datasets)

- [CheXpert: A Large Chest X-Ray Dataset And Competition](https://stanfordmlgroup.github.io/competitions/chexpert/)

- [Medical Image Net](http://langlotzlab.stanford.edu/projects/medical-image-net/)



### Packages



- [Awesome Healthcare](https://github.com/kakoni/awesome-healthcare)

- [healthcareai: R tools for healthcare machine learning](https://github.com/HealthCatalyst/healthcareai-r)

- [Image Segmentation with Pytorch](https://github.com/LeeJunHyun/Image_Segmentation)

- [medpy: medical image processing in Python](https://github.com/loli/medpy)



### Competitions



- [Grand Challenges in Biomedical Image Analysis](https://grand-challenge.org/)

- [Predict Blood Donations](https://www.drivendata.org/competitions/2/warm-up-predict-blood-donations/) - DrivenData - open

- [Countable Care: Modeling Women's Health Care Decisions](https://www.drivendata.org/competitions/6/countable-care-modeling-womens-health-care-decisions/) - DrivenData - 2015

- [Heritage Health Prize: Identify patients who will be admitted to a hospital within the next year using historical claims data](https://www.kaggle.com/c/hhp) - Kaggle - 2012



### Conferences and events



- [Conference on Artificial Intelligence in Medicine](http://aime19.aimedicine.info/)

- [Deep Learning in Healthcare Summit](https://www.re-work.co/events/deep-learning-in-healthcare-summit-boston-2019)

- [Machine Learning for Healthcare](https://www.mlforhc.org/)



## Cardiology



### Articles



- [Getting to the Heart of it: How Deep Learning is Transforming Cardiac Imaging](https://medium.com/stanford-ai-for-healthcare/getting-to-the-heart-of-it-how-deep-learning-is-transforming-cardiac-imaging-22d34bf91a4e) - 2018

- [Artificial Intelligence in Cardiology](https://www.sciencedirect.com/science/article/pii/S0735109718344085) - 2018

- [Cardiac imaging: working towards fully-automated machine analysis & interpretation](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5450918/) - 2017



### Books 



- [ECG Signal Processing, Classification and Interpretation: A Comprehensive Framework of Computational Intelligence](https://www.amazon.com/Signal-Processing-Classification-Interpretation-Comprehensive/dp/0857298674/ref=sr_1_8?ie=UTF8&qid=1547309109&sr=8-8)



### Datasets



- [Cardiac MRI dataset](http://www.cse.yorku.ca/~mridataset/)

- [Congenital Heart Disease (CHD)](https://data.gov.uk/dataset/f13fbd0e-fc8a-4d42-82ef-d40f930e4b70/congenital-heart-disease-chd)

- [SPECT - Heart Dataset](http://archive.ics.uci.edu/ml/datasets/SPECT+Heart)

- [Stanford’s South African Heart Disease Dataset](https://web.stanford.edu/~hastie/ElemStatLearn//datasets/SAheart.data)

- [Sunnybrook Cardiac Data](http://www.cardiacatlas.org/studies/sunnybrook-cardiac-data/)

- [UCI - Heart Disease Dataset](https://archive.ics.uci.edu/ml/datasets/heart+Disease)



### Packages



- [cardIO: data science research of heart signals in Python](https://github.com/analysiscenter/cardio)



### Competitions



- [Machine Learning with a Heart: predicting heart disease](https://www.drivendata.org/competitions/54/machine-learning-with-a-heart/) - DrivenData - open

- [II Annual Data Science Bowl: transforming how we diagnose heart disease](https://www.kaggle.com/c/second-annual-data-science-bowl) - Kaggle - 2016



## Epidemiology & infectology



### Articles



- [Artificial Intelligence in Public Health and Epidemiology](https://www.ncbi.nlm.nih.gov/pubmed/30157525) - 2018

- [Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology](https://ehjournal.biomedcentral.com/track/pdf/10.1186/s12940-018-0386-x) - 2018

- [Machine-learned epidemiology: real-time detection of foodborne illness at scale](https://www.nature.com/articles/s41746-018-0045-1) - 2018

- [Machine learning spots treasure trove of elusive viruses](https://www.nature.com/articles/d41586-018-03358-3)

- [Big Data for Infectious Disease Surveillance and Modeling](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5181547/)



### Books 



- [Epidemiology: Study Design and Data Analysis](https://www.amazon.com/Epidemiology-Analysis-Chapman-Statistical-Science/dp/1439839700/ref=sr_1_7?ie=UTF8&qid=1547310088&sr=8-7)



### Datasets



- [Cancer Registration: Epidemiology of lung cancer tumours in England 2009 to 2013]()

- [University Library: Epidemiology and Health Statistics](https://researchguides.uic.edu/c.php?g=252253&p=1683071)



### Packages



- [epipy: python tools for epidemiology](https://github.com/cmrivers/epipy)

- [Epi: statistical analysis in epidemiology in R](https://cran.r-project.org/web/packages/Epi/index.html)



### Competitions



- [DengAI: predicting disease spread](https://www.drivendata.org/competitions/44/dengai-predicting-disease-spread/) - DrivenData - open

- [Predict HIV Progression: predict the likelihood that an HIV patient's infection will become less severe, given a small dataset and limited clinical information](https://www.kaggle.com/c/hivprogression) - Kaggle - 2010

- [West Nile Virus Prediction: predict West Nile virus in mosquitos across the city of Chicago](https://www.kaggle.com/c/predict-west-nile-virus) - Kaggle - 2015



## Genetics



### Articles



- [A primer on deep learning in genomics](https://www.nature.com/articles/s41588-018-0295-5) - 2019

- [Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania](https://www.nature.com/articles/s41467-018-08089-7) - 2019

- [Machine learning applications in genetics and genomics](https://www.nature.com/articles/nrg3920) - 2015



### Books



- [Statistics in Human Genetics](https://www.amazon.com/Statistics-Human-Genetics-Pak-Sham/dp/0470689285/ref=sr_1_6?ie=UTF8&qid=1547310890&sr=8-6)



### Datasets



- [Clinical Genomic Database](https://research.nhgri.nih.gov/CGD/)

- [Genotype-Tissue Expression](https://commonfund.nih.gov/GTEx/)

- [Project Achilles](https://portals.broadinstitute.org/achilles)

- [The drug gene interaction database](http://www.dgidb.org/)



### Packages



- [biopython: python tools for computational molecular biology](https://github.com/biopython/biopython)

- [pyGeno: personalized Genomics and Proteomics](https://github.com/tariqdaouda/pyGeno)



### Competitions



- [Gene Expression Prediction: Predicting gene expression from histone modification signals](https://www.kaggle.com/c/gene-expression-prediction) - Kaggle - 2017



### Conferences and events



- [International Conference on Bioinformatics Research and Applications (ICBRA)](http://www.icbra.org/)

- [International Symposium on Bioinformatics Research and Applications (ISBRA)](http://alan.cs.gsu.edu/isbra19/)



## Neurology



### Articles



- [Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks](https://www.nature.com/articles/s41591-019-0715-9) - Nature Medicine - 2020

- [A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain](https://pubs.rsna.org/doi/10.1148/radiol.2018180958) - 2018

- [High-precision automated reconstruction of neurons with flood-filling networks](https://www.nature.com/articles/s41592-018-0049-4) - 2018

- [Machine learning in neurology: what neurologists can learn from machines and vice versa](https://www.ncbi.nlm.nih.gov/pubmed/30073503) - 2018

- [AI and Neuroscience: A virtuous circle](https://deepmind.com/blog/ai-and-neuroscience-virtuous-circle/)

- [Artificial Intelligence and Neurology](https://www.omicsonline.org/open-access/artificial-intelligence-and-neurology-.php?aid=84397) - 2016



### Books



- [Advanced Data Analysis in Neuroscience: Integrating Statistical and Computational Models](https://www.amazon.com/Advanced-Data-Analysis-Neuroscience-Computational/dp/3319599747/ref=sr_1_15?ie=UTF8&qid=1547312406&sr=8-15)

- [Computational Neurology and Psychiatry](https://www.amazon.com/Computational-Neurology-Psychiatry-Springer-Neuroinformatics-ebook/dp/B01N4UFZJ7/ref=sr_1_1?ie=UTF8&qid=1547312382&sr=8-1)

- [Handbook of Functional MRI Data Analysis](https://www.amazon.com/Handbook-Functional-MRI-Data-Analysis-ebook/dp/B009019PHY/ref=sr_1_8?ie=UTF8&qid=1547312474&sr=8-8)

- [Neural Data Science: A Primer with MATLAB and Python](https://www.amazon.com/Neural-Data-Science-MATLAB%C2%AE-PythonTM-ebook/dp/B06XCW39WX/ref=sr_1_10?ie=UTF8&qid=1547312474&sr=8-10)

- [The Statistical Analysis of Functional MRI Data](https://www.amazon.com/Statistical-Analysis-Functional-Statistics-Biology/dp/0387781900/ref=sr_1_3?ie=UTF8&qid=1547312474&sr=8-3)

- [Visual Cortex and Deep Networks: Learning Invariant Representations](https://www.amazon.com/Visual-Cortex-Deep-Networks-Representations-ebook/dp/B01M15Z4VN/ref=sr_1_6?ie=UTF8&qid=1547312406&sr=8-6)



### Datasets



- [Allen Brain Atlas](http://portal.brain-map.org/)

- [BrainCloud](http://braincloud.jhmi.edu/)

- [The Human Connectome Project](http://www.humanconnectomeproject.org/)

- [UCI - EEG Database Data Set](http://archive.ics.uci.edu/ml/datasets/EEG+Database)



### Packages



- [MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python](https://github.com/mne-tools/mne-python)

- [nilearn: machine learning for neuroimaging in Python](https://github.com/nilearn/nilearn)

- [visbrain: brain data visualization in Python](https://github.com/EtienneCmb/visbrain)



### Competitions



- [PREPARE: Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA Challenge](https://www.drivendata.org/competitions/253/competition-nih-alzheimers-adrd-1/) - DrivenData - 2023.

- [Ultrasound Nerve Segmentation: identify nerve structures in ultrasound images of the neck](https://www.kaggle.com/c/ultrasound-nerve-segmentation) - Kaggle - 2016

- [Melbourne University AES/MathWorks/NIH Seizure Prediction: predict seizures in long-term human intracranial EEG recordings](https://www.kaggle.com/c/melbourne-university-seizure-prediction) - Kaggle - 2016

- [Grasp-and-Lift EEG Detection: identify hand motions from EEG recordings](https://www.kaggle.com/c/grasp-and-lift-eeg-detection) - Kaggle - 2015

- [CONNECTOMICS: reconstruct the wiring between neurons from fluorescence imaging of neural activity](https://www.kaggle.com/c/connectomics) - Kaggle - 2014

- [DecMeg2014 - Decoding the Human Brain: predict visual stimuli from MEG recordings of human brain activity](https://www.kaggle.com/c/decoding-the-human-brain) - Kaggle - 2014

- [UPenn and Mayo Clinic's Seizure Detection Challenge: detect seizures in intracranial EEG recordings](https://www.kaggle.com/c/seizure-detection) - Kaggle - 2014

- [American Epilepsy Society Seizure Prediction Challenge: predict seizures in intracranial EEG recordings](https://www.kaggle.com/c/seizure-prediction) - Kaggle - 2014

- [Predicting Parkinson's Disease Progression with Smartphone Data](https://www.kaggle.com/c/predicting-parkinson-s-disease-progression-with-smartphone-data) - Kaggle - 2013



### Conferences and events



- [International Conference on Brain Informatics](https://uta.engineering/conferences/bi-2018/index.php)



## Oncology



### Articles



- [Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach](https://arxiv.org/abs/1912.11027v2) - 2019

- [Improved Grading of Prostate Cancer Using Deep Learning](https://ai.googleblog.com/2018/11/improved-grading-of-prostate-cancer.html) - 2018



### Competitions



- [Histopathologic Cancer Detection: identify metastatic tissue in histopathologic scans of lymph node sections](https://www.kaggle.com/c/histopathologic-cancer-detection) - Kaggle - 2019

- [Data Science Bowl 2017: Can you improve lung cancer detection?](https://www.kaggle.com/c/data-science-bowl-2017) - Kaggle - 2017

- [Intel & MobileODT Cervical Cancer Screening: which cancer treatment will be most effective?](https://www.kaggle.com/c/intel-mobileodt-cervical-cancer-screening) - Kaggle - 2017

- [Personalized Medicine: redefining cancer treatment](https://www.kaggle.com/c/msk-redefining-cancer-treatment) - Kaggle - 2017



## Ophthalmology



### Articles



- [Deep Learning for Detection of Diabetic Eye Disease](https://ai.googleblog.com/2016/11/deep-learning-for-detection-of-diabetic.html) - Google - 2016.



### Competitions



- [Diabetic Retinopathy Detection: identify signs of diabetic retinopathy in eye images](https://www.kaggle.com/c/diabetic-retinopathy-detection) - Kaggle - 2015



## Orthopedicts



### Articles



- [Automation of reading of radiological features from magnetic

resonance images (MRIs) of the lumbar spine without human

intervention is comparable with an expert radiologist](http://www.robots.ox.ac.uk/~vgg/publications/2017/Jamaludin17/jamaludin17.pdf) - 2017

- [SpineNet: Automated classification and evidence visualization in

spinal MRIs](http://www.robots.ox.ac.uk/~vgg/publications/2017/Jamaludin17b/jamaludin17b.pdf) - 2017



### Datasets



- [Vertebral Column Data Set](http://archive.ics.uci.edu/ml/datasets/vertebral+column)



### Competitions



- [MURA: Bone X-Ray Deep Learning Competition](https://stanfordmlgroup.github.io/competitions/mura/) - Stanford ML Group - open



## Pharmacology



### Articles



- [Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19](https://arxiv.org/abs/2004.07229v1) - 2020

- [Meta-Learning Initializations for Low-Resource Drug Discovery](https://arxiv.org/abs/2003.05996v1) - 2020



### Datasets



- [The Databases for Drug Discovery](https://github.com/LeeJunHyun/The-Databases-for-Drug-Discovery)



### Competitions



- [Merck Molecular Activity Challenge: help develop safe and effective medicines by predicting molecular activity](https://www.kaggle.com/c/MerckActivity) - Kaggle - 2012

- [Predicting a Biological Response: predict a biological response of molecules from their chemical properties](https://www.kaggle.com/c/bioresponse) - Kaggle - 2012



## Psychiatry



### Articles



- [Feeling Anxious? Perceiving Anxiety in Tweets using Machine Learning](https://arxiv.org/abs/1909.06959v1) - 2019

- [A novel machine learning based framework for detection of Autism Spectrum Disorder (ASD)](https://arxiv.org/abs/1903.11323v3) - 2019



### Books



- [Computational Neurology and Psychiatry](https://www.amazon.com/Computational-Neurology-Psychiatry-Springer-Neuroinformatics-ebook/dp/B01N4UFZJ7/ref=sr_1_1?ie=UTF8&qid=1547312382&sr=8-1)



### Competitions



- [MLSP 2014 Schizophrenia Classification Challenge: diagnose schizophrenia using multimodal features from MRI scans](https://www.kaggle.com/c/mlsp-2014-mri) - Kaggle - 2014

- [Psychopathy Prediction Based on Twitter Usage](https://www.kaggle.com/c/twitter-psychopathy-prediction) - Kaggle - 2012



## Pulmonology



### Competitions



- [RSNA Pneumonia Detection Challenge: can you build an algorithm that automatically detects potential pneumonia cases?](https://www.kaggle.com/c/rsna-pneumonia-detection-challenge) - Kaggle - 2018



## Research



### Articles



- [ProteinNet: a standardized data set for machine

learning of protein structure](https://arxiv.org/abs/1902.00249v1) - 2019



### Packages



- [MiniFold](https://github.com/EricAlcaide/MiniFold)



### Datasets



- [ProteinNet: a standardized data set for machine learning of protein structure](https://github.com/aqlaboratory/proteinnet)



### Competitions



- [Human Protein Atlas Image Classification: classify subcellular protein patterns in human cells](https://www.kaggle.com/c/human-protein-atlas-image-classification) - Kaggle - 2019

- [2018 Data Science Bowl: find the nuclei in divergent images to advance medical discovery](https://www.kaggle.com/c/data-science-bowl-2018) - Kaggle - 2018