The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care

Interesting work looking at how AI could suggest optimal treatment for sepsis. Sepsis is a life threatening complication of infection and many deaths could be prevented with earlier identification and more targeted therapies.

Link: https://www.nature.com/articles/s41591-018-0213-5

#medical #health

Prototypical Clustering Networks for Dermatological Disease Diagnosis

Paper will be presented at the ML4D workshop at #NIPS2018

Link: https://arxiv.org/abs/1811.03066

#nn #bio #medical

Nice paper from the #GoogleAI team, grading prostate cancer in prostatectomy specimens.

The model outperforms humans on the silver standard labels (panel of experts), but there is no clear winner for outcome prediction in the K-M plot/c-index.

«the mean accuracy among 29 general pathologists was 0.61. The DLS achieved an... accuracy of 0.70 (p=0.002) and trended towards better patient risk stratification»

Post: https://ai.googleblog.com/2018/11/improved-grading-of-prostate-cancer.html
ArXiV: https://arxiv.org/abs/1811.06497

#DL #medical #cancer

​​Deep learning for chest X-rays

Important work on chest X-Ray analysis.

ArXiV: https://arxiv.org/abs/1711.05225

#DL #medical #bioinformatics

A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain

Computer Vision can detect Alzheimer’s Disease in brain scans SIX YEARS before a diagnosis. Uses PET scans, which are common & cheaper. 82% specificity at 100% sensitivity. Can pick out signs hard to see with the naked eye.

Link: https://pubs.rsna.org/doi/10.1148/radiol.2018180958

#CV #DL #Alzheimer #medical

​​Weakly supervised mitosis detection in breast histopathology images using concentric loss

Weakly-supervised mitosis detection in breast histopathology images shows that only using one-click annotation can obtain the best performances on three challenging datasets.

Link: https://www.sciencedirect.com/science/article/abs/pii/S1361841519300118?dgcid=author

#healthcare #medical #CV #cancer #DL

​​Generative Image Translation for Data Augmentation in Colorectal Histopathology Images

#GAN that generates near-real #histology images according to a Turing test with 4 pathologists. The results can be used for training #DL models for detecting rare histological patterns.

ArXiV: https://arxiv.org/abs/1910.05827
Code: https://github.com/BMIRDS/HistoGAN

#CV #healthlearning #biolearning #medical

Using AI to Understand What Causes Diseases

An overview on applying data science in healthcare

Poster: https://info.gnshealthcare.com/hubfs/Publications_2019/ESMO_GI_Final_Poster_Printed_PD_20.pdf
Link: https://hbr.org/2019/11/using-ai-to-understand-what-causes-diseases

#meta #biolearning #dl #medical #healthcare

The female problem: how male bias in medical trials ruined women's health

Intersting article on #bias in #medical trials and how proper #statistics training is still important.

Link: https://www.theguardian.com/lifeandstyle/2019/nov/13/the-female-problem-male-bias-in-medical-trials

ProteinNet: a standardized data set for machine learning of protein structure

Link: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2932-0
Github: https://github.com/aqlaboratory/proteinnet

#biolearning #medical #dl

​​How climate change, air pollution, and provider shortages are making things worse for allergy-sufferers

Analytical research (including #interactive maps) of connection of air pollution to allergy reactions in the U.S.

Link: https://medium.com/ro-co/how-climate-change-air-pollution-and-provider-shortages-are-making-things-worse-for-90e0f8d4a36b

#eda #explorative #healthcare #medical

🏥Self-supervised Learning for Medical images

Due to standard imaging procedures, medical images (X-ray, CT scans, etc) are usually well aligned.
This paper gives an opportunity to utilize such an alignment to automatically connect similar pairs of images for training.

GitHub: https://github.com/fhaghighi/TransVW
ArXiV: https://arxiv.org/abs/2102.10680

#biolearning #medical #dl #pytorch #keras

Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study


Some time ago in a different world one of the channel editors shared permmission to use data from sleep & activity tracker Oura Ring to develop an algorithm for COVID-19 prediction.

Results of this study continue to arrive. Today team shared the second manuscript from the first TemPredict Study in Nature Scientific Reports. This manuscript details an algorithm designed to detect COVID-19 using data from the Oura Ring. Alogirthm publication: www.nature.com/articles/s41598-022-07314-0 

The first publication from the first TemPredict Study will continue to be available online for you to access at any time, at this link: https://www.nature.com/articles/s41598-020-78355-6

The first publication from the second TemPredict Study (correlations between data from the Oura Ring and data from a LabCorp antibody blood test) will also continue to be available online for you to access at any time, at this link: https://www.mdpi.com/2076-393X/10/2/264

That's the power of the international collaboration 💪

#oura #covid #biolearning #medical #health