Leading publications and more for machine learning in healthcare linked here.
2019 Year in Review:Machine Learning in Healthcare General
BrainX Community
A review of 3000+ articles on machine learning in healthcare published in 2019 with specialist reviews.
A BrainX Community exclusive!
2018 Year in Review: Machine Learning in Healthcare. General
BrainX Community
A review of 2000+ articles on machine learning in healthcare published in 2018.A BrainX Community exclusive!
Explainable machine learning models to understand determinants of COVID-19 mortality in the United States
Personalized predictions of patient outcomes during and after hospitalization using artificial intelligence General
C. Beau Hilton,Alex Milinovich et al.
Preoperative CT-based Deep Learning Model for Predicting Disease-Free Survival in Patients with Lung Adenocarcinomas Imaging
Hyungjin Kim,Jin Mo Goo et al.
Combined use of clinical and imaging data for predictions.
Perioperative intelligence: applications of artificial intelligence in perioperative medicine Surgery/Anesthesiology
Kamal Maheshwari, Kurt Ruetzler, Bernd Saugel
Commentary highlighting use of AI in perioperative medicine.
A Multicenter, Scan-Rescan, Human and Machine Learning CMR Study to Test Generalizability and Precision in Imaging Biomarker Analysis General/Cardiology
Anish N. Bhuva, MBBS et al
Interesting approach to assess accuracy of ML algorithms and their reproducibility/generalizability
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis General
Xiaoxuan Liu, et al.
A challenge for ML in healthcare is to demonstrate external validation,generalizability and scalability.A detailed review with some great insights.
How to Read Articles That Use Machine Learning Users’ Guides to the Medical Literature General
Yun Liu, PhD Po-Hsuan Cameron Chen, PhD Jonathan Krause, PhD ; et al
Excellent review article on interpreting ML research literature.
High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP) General
Yichi Zhang,et al.
A semisupervised pipeline for generation of diagnostic groups from electronic health records.
Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram Cardiology
Zachi I. Attia,Paul A. Friedman,et al.
Neural network to screen ECGs to discover asymptomatic heart failure.
Human–machine partnership with artificial intelligence for chest radiograph diagnosis Imaging
Bhavik N. Patel
Novel use of swarm AI to improve prediction of chest x-day diagnosis.
Dissecting racial bias in an algorithm used to manage the health of populations General
Ziad Obermeyer,Brian Powers,Christine Vogeli,Sendhil Mullainathan
Bias in algorithms can be at all levels.This article highlights a few mechanisms.
Multimodal Machine Learning for Automated ICD Coding General
Genomic Biomarkers to Predict Resistance to Hypomethylating Agents in Patients With Myelodysplastic Syndromes Using Artificial Intelligence Cancer
Aziz Nazha, MD et al.
Use of genetic markers to predict treatment efficacy using AI.
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis General
Alex Zhavoronkov,et al.
Drug development using machine learning-a real life example
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study General
Livia Faes, MD,Siegfried K Wagner, BMBCh Dun Jack Fu, PhD, et al.
Evaluation of of AutoML for medical image classification models.
An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction Cardiology
Zachi I Attia, MS,Peter A Noseworthy, MD,et al.
Identification of patients with episodes of Atrial Fibrillation during normal sinus rhythm using convolutional neural network.
A clinically applicable approach to continuous prediction of future acute kidney injury Nephrology
Nenad Tomašev,Xavier Glorot
An artificial Intelligence algorithm to identify patients at risk for acute kidney injury as early as 48 hours before onset.
Artificial Intelligence in Critical Care Critical care
Piyush Mathur MD, FCCM; Michael L. Burns, MD, PhD
A primer on AI and review of AI research/applications in critical care.
Artificial Intelligence for Clinical Trial Design Research
Stefan Harrer,Pratik Shah,Bhavna Antony,Jianying Hu
Comprehensive review on use of machine learning for clinical research and trials.
An image-based deep learning framework for individualising radiotherapy dose: a retrospective analysis of outcome prediction Cancer
Bin Lou, PhD ,et al.
Deep Learning to Assess Long-term Mortality From Chest Radiographs Imaging/Radiology
Michael T. Lu, MD, MPHAlexander Ivanov, BSThomas Mayrhofer, PhD; et al
Use of Deep Learning for prognosis using Chest X-Rays.
Machine learning as a supportive tool to recognize cardiac arrest in emergency calls Emergency Medicine
Fredrik Folke,Annette Kjær Ersbøll,Helle Collatz Christensen,et al.
Use of machine learning to recognize out of hospital cardiac arrests from dispatcher calls.
Assessment of Deep Natural Language Processing in Ascertaining Oncologic Outcomes From Radiology Reports Cancer
Kenneth L. Kehl, MD, MPHHaitham Elmarakeby, PhDMizuki Nishino, MD, MPH; et al
Use of natural language processing to predict outcomes more efficiently in cancer patients.
Outcome Prediction in Postanoxic Coma With Deep Learning Critical care
Tjepkema-Cloostermans,et al.
Outcome prediction using EEG waveform derived deep learning model in patients who had cardiac arrest.
Intensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems Critical care
Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature General
Triantafyllidis AK, Tsanas A
An exhaustive review of literature on real life interventions using machine learning applications.
Multitask Learning and Benchmarking with Clinical Time Series Data General
Hrayr Harutyunyan, Hrant Khachatrian, David C. Kale, Greg Ver Steeg, Aram Galstyan
Interesting use of multimodal architecture and feature engineering for developing prediction models using MIMIC data.
Radiology:Artificial Intelligence Imaging/Radiology
Radiology Society of North America's journal on Artificial Intelligence.Key Imaging publications on AI published here periodically.
The Lancet Digital Health General
Lancet's digital health focused Journal with new publications posted periodically.
Machine Learning in Medicine General
Alvin Rajkomar, M.D.,Jeffrey Dean, Ph.D., Isaac Kohane, M.D., Ph.D.
A basic but thorough review of machine learning modeling for healthcare and its challenges.
Applications of machine learning in drug discovery and development General
Jessica Vamathevan,et al.
An overview of machine learning applications and challenges in drug discovery.
Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs Imaging/Radiology
Eui Jin Hwang, MD; Sunggyun Park, MS; Kwang-Nam Jin, MD; et al
Moving from development to validation and generalizability of deep learning algorithms for Chest X-Ray interpretation.
Artificial intelligence in healthcare: past, present and future General
Fei Jiang, et al.
Review of ML in healthcare.A good primer and interesting data on ML applications.
An investigation of patterns in hemodynamic data indicative of impending hypotension in intensive care Critical care
Joon Lee, Roger G Mark
Prediction of hypotension in ICU using waveform and other data.
Ease of adoption of clinical natural language processing software: An evaluation of five systems General
KaiZheng V.G. , et al.
Excellent evaluation of NLP in healthcare and its challenges.
Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions General
Wei-Hsuan Lo-Ciganic, PhD ; James L. Huang, PhD ; Hao H. Zhang, PhD ; et al
Machine learning models for prediction of at risk population for opioid overdose.
Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data Neurology
Nikhil Bhagwat ,Joseph D. Viviano,Aristotle N. Voineskos,M. Mallar Chakravarty
Modeling of symptoms to predict trajectory of Alzheimer's disease.
Fast and accurate view classification of echocardiograms using deep learning Cardiology
Ali Madani,Ramy Arnaout,Mohammad Mofrad Rima Arnaout
Accurate echocardiogram view classification using deep learning
Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification Neurology
Igor O. Korolev, Laura L. Symonds, Andrea C. Bozoki
Using multimodal technique to predict progression of Alzheimer's dementia.
Machine learning in healthcare: Nature biomedical engineering General
Continuously updated Nature journal's collection of machine learning in healthcare articles.
KenSci publications General
A comprehensive list of publications of research articles from experts at KenSci.Ranging from predictive analytic applications in healthcare to various frameworks for value based healthcare using machine learning.
Also watch webinar on : "Explainable Machine Learning models for Healthcare AI."
https://www.youtube.com/watch?v=4pgLsDzrlB8
High-performance medicine: the convergence of human and artificial intelligence General
Eric J.Topol
Excellent article from Dr.Topol,describing advances made in AI/ML application amongst various medical specialities.
BrainX Community's 2018 AI/ML Year in Review is a great supplement to this article.
The reproducibility crisis in the age of digital medicine General
Aaron Stupple,David Singerman,Leo Anthony Celi
Reproducibility and validation issues with AI/ML in healthcare addressed in this article.
Machine learning and medical education General
Vijaya B. Kolachalama,Priya S. Garg
Perspective on integrating ML training into medical curriculum.
An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets Neurology
Hyunkwang Lee,Sehyo Yune,Mohammad Mansouri,Myeongchan Kim,Shahein H. Tajmir,Claude E. Guerrier,Sarah A. Ebert,Stuart R. Pomerantz,Javier M. Romero,Shahmir Kamalian,Ramon G. Gonzalez,Michael H. Lev & Synho Do
Deep learning for ICH detection, classification with explainability.
Publications from Laboratory of Quantitative Imaging and Artificial Intelligence (QIAI) General/Imaging
Excellent and exhaustive research from Daniel L. Rubin, MD, MS lab at Stanford University, especially focused on Imaging.
Article collection on ML/AI with focus on Imaging/Radiology Imaging
Good collection of ML articles posted by Canadian Association of Radiologists.Some really good as primers.
Image reconstruction by domain-transform manifold learning Imaging
Bo Zhu, Jeremiah Z. Liu, Stephen F. Cauley, Bruce R. Rosen & Matthew S. Rosen
AUTOMAP:Innovative image reconstruction process using machine learning.
An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU Critical Care
Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG.
AISE(Artificial Intelligence Sepsis Expert) for Sepsis prediction.
Machine learning for real-time prediction of complications in critical care: a retrospective study Critical Care
Alexander Meyer, Dina Zverinski, Boris Pfahringer, Jörg Kempfert, Titus Kuehne, Simon H Sündermann, Christof Stamm, Thomas Hofmann, Volkmar Falk, Carsten Eickhoff
Machine learning model for prediction of complications in critically ill patients.
The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care Critical Care
Matthieu Komorowski,Leo A. Celi,Omar Badawi,Anthony C. Gordon & A. Aldo Faisal
Machine learning to optimize management of sepsis.
Identify and monitor clinical variation using machine intelligence: a pilot in colorectal surgery Anesthesiology/Surgery
Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression. Behavioral sciences
Nikolaos Koutsouleris, MD; Lana Kambeitz-Ilankovic, PhD; Stephan Ruhrmann, MD; et al
Machine learning predictive model using multimodal data for prediction of functional outcomes in patients with psychiatric disorders.
Informatics, Data Science, and Artificial Intelligence General
Lisha Zhu, PhD ; W. Jim Zheng, PhD
Viewpoint on relationship of health informatics, data and AI.
Vascular Network Organization via Hough Transform (VaNgOGH): A Novel Radiomic Biomarker for Diagnosis and Treatment Response Cancer/Imaging
Radiomic biomarkers for oncologic diagnosis and treatment management.
Feature Driven Local Cell Graph (FeDeG): Predicting Overall Survival in Early Stage Lung Cancer Cancer/Imaging
New feature based histopathological approach to predict survival in Lung cancer.
Spatial architecture and arrangement of tumor-infiltrating lymphocytes for predicting likelihood of recurrence in early-stage non-small cell lung cancer Cancer/Imaging
,et al.
More and better features being utilized for oncologic disease management.
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning Cancer/Pathology
Nicolas Coudray, Paolo Santiago Ocampo, Theodore Sakellaropoulos, Navneet Narula, Matija Snuderl, David Fenyö, Andre L. Moreira, Narges Razavian & Aristotelis Tsirigos
Use of convolutional neural networks for classification in Pathology.
Anesthesiology, October 2018 edition focused on Artificial Intelligence.Articles with links below:
Artificial Intelligence for Anesthesia: What the Practicing Clinician Needs to Know: More than Black Magic for the Art of the Dark Anesthesiology
Michael R. Mathis, M.D.; Sachin Kheterpal, M.D., M.B.A.; Kayvan Najarian, Ph.D.
Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality Anesthesiology
Christine K. Lee, M.S., Ph.D.; Ira Hofer, M.D.; Eilon Gabel, M.D.; Pierre Baldi, Ph.D.; Maxime Cannesson, M.D., Ph.D.
Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis Anesthesiology
Feras Hatib, Ph.D.; Zhongping Jian, Ph.D.; Sai Buddi, Ph.D.; Christine Lee, M.S.; Jos Settels, M.S.; et al
Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension Anesthesiology
Samir Kendale, M.D.; Prathamesh Kulkarni, Ph.D.; Andrew D. Rosenberg, M.D.; Jing Wang, M.D., Ph.D.
AIMed Magazine #04 – Cloud Computing & Big Data General
AIMed magazine edition focused on healthcare data.
- Data is the new fuel: a note by Dr Anthony Chang, MD, MBA, MPH, MS
- The challenge of data protection diversity for AI research by Leo Anthony Celi, MD
- Great expectations for machine learning in EHRs by Piyush Mathur, MD, FCCM,Ashish Khanna,MD,FCCM
- Empathy, Values + AI by Jules Sherman, MFA
- Building a strong future: Diversity & Inclusion in AIMed by Crystal Valentine, PhD; Fran Ayalasomayajula, MPH, PMP; Maxine Mackintosh, PhD; Claire Novorol, PhD
Value of Neighborhood Socioeconomic Status in Predicting Risk of Outcomes in Studies That Use Electronic Health Record Data General
Nrupen A. Bhavsar, PhD; Aijing GaO, MS, Matthew Phelan, MS; et al
While most studies show the importance of socioeconomic determinants this one demonstrates no value over EHR data.
Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges Cancer/Imaging
Hesham Elhalawami et al.
Lessons learnt from challenge based research in head and neck oncology machine learning applications.
The eICU Collaborative Research Database, a freely available multi-center database for critical care research General
Tom J. Pollard, Alistair E. W. Johnson, Jesse D. Raffa, Leo A. Celi, Roger G. Mark & Omar Badawi
Article from Nature Journal on description of the EICU Collaborative Research database.
Workshop on Artificial Intelligence in Medical Imaging to foster innovative collaborations in applications for diagnostic medical imaging held by National Institute of Biomedical Imaging and Bioengineering (NIBIB). General
Watch the videos from the workshop via links below for day 1 and day 2..
Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices Ophthalmology
Michael D. Abràmoff Philip T. Lavin, Michele Birch, Nilay Shah and James C. Folk
From retrospective research to pivotal trial leading to FDA approval.Landmark article for use of AI in healthcare, especially using imaging modalities.
Scalable and accurate deep learning with electronic health records General
Alvin Rajkomar, et al.
Novel methods to use electronic health record(EHR) data for predictive analytics.A must read for anyone using EHR data.
Deep Lesion Imaging
NIH release of a annotated 32,000 CT scan image dataset from 4400 unique patients for research and development.
DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning
Ke Yan; Xiaosong Wang; Le Lu; Ronald M. Summers
Deep Learning: A Primer for Radiologists Imaging
Detailed basic review on application of machine learning in Radiology.
Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI Cancer
Nathaniel M. Braman, Maryam Etesami , Prateek Prasanna , Christina Dubchuk , Hannah Gilmore , Pallavi Tiwari , Donna Pletcha and Anant Madabhushi
Radiomics using DCE-MRI for prediction of chemotherapy response.
Prostate shapes on pre-treatment MRI between prostate cancer patients who do and do not undergo biochemical recurrence are different: Preliminary Findings Cancer/Imaging
Soumya Ghose, Rakesh Shiradkar, Mirabela Rusu , Jhimli Mitra, Rajat Thawani , Michael Feldman, Amar C. Gupta, Andrei S. Purysko, Lee Ponsky & Anant Madabhushi
Using MRI to look at Prostate shapes and predict recurrence of Prostate Cancer.
Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma Cancer/Imaging
Niha Beig, Jay Patel, Prateek Prasanna, Virginia Hill, Amit Gupta, Ramon Correa, Kaustav Bera, Salendra Singh, Sasan Partovi, Vinay Varadan, Manmeet Ahluwalia, Anant Madabhushi & Pallavi Tiwari
Use of radiomic features on MRI to determine prognosis amongst Glioblastoma patients.
Coregistration of Preoperative MRI with Ex Vivo Mesorectal Pathology Specimens to Spatially Map Post-treatment Changes in Rectal Cancer Onto In Vivo Imaging Cancer/Imaging/Pathology
Artificial Intelligence in Cardiology Cardiology
, , , , , , and
Detailed discussion of various machine learning techniques and applications in context of cardiovascular disease.Very informative for clinicians who are learning the basics of machine learning and its application in healthcare.
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs Ophthalmology
Varun Gulshan, PhD; et al.
A landmark article demonstrating high accuracy of image recognition technology to arrive at a diagnosis.
Machine learning in cardiovascular medicine: are we there yet? Cardiology
Shameer K,Johnson KW,Glickberg BS,Dudley JT,Sengupta PP
New publications coming everyday exploring the opportunities for application of machine learning in cardiovascular medicine.Opportunities and challenges discussed in this article.
Improving Palliative Care with Deep Learning General
This article discusses use of electronic health record data to predict mortality and thereby identifying patients who would benefit from palliative care.
What This Computer Needs Is a Physician. Humanism and Artificial Intelligence General
Abraham Verghese, MD; Nigam H.Shah,MBBS,PhD;Robert A. Harrington, MD
View point in JAMA,discussing the need for improving Electronic Health Records exploring possibilities using Machine learning and Artificial Intelligence while also addressing their pitfalls.
Artificial intelligence applications in the intensive care unit
Critical Care
C. William Hanson III, MD, FCCM; Bryan E. Marshall, MD, FRCP, FRCA
Review from 2001 of AI applications in data rich extremely complex environment of Intensive Care Unit.Opportunities to apply advanced technologies in healthcare environment were identified many years ago.