“Hiring a Machine Learning engineer or Data Scientist in Silicon Valley is becoming like hiring a professional athlete. That’s how demanding it is” — The New York Times
Identifying palliative care patients based on deep learning
Stanford ML Group built a program that uses deep learning algorithms to identify hospitalized patients at high risk of dying in the next 3-12 months based on Electronic Health Record (EHR) data. Early warning information for these patients is sent to the palliative care team, which helps the palliative care team to intervene and provide services earlier.
Palliative care (Palliative Care, translated as palliative medicine in Japan and Taiwan) originated from the hospice movement and originated in the fourth century AD. According to the definition of the World Health Organization, palliative care emphasizes the control of pain and patient-related symptoms, and pays attention to psychological, social and spiritual problems, in order to obtain the best quality of life for patients and their families.
The prediction model is an 18-layer deep neural network, the input parameter is the EHR data of a patient, and the output is the probability of death in the next 3-12 months. The training data uses historical data from the Stanford Hospital EHR database, which contains data from more than 2 million patients. The EHR data includes the patient's diagnosis conclusions, treatment procedures, prescriptions and related details (desensitized and technically processed, and represented in the form of surrogate codes) for the past 12 months, and all data are converted into 13654-dimensional feature vectors. The trained model achieves an AUROC score of 0.93 and an average cross-validation accuracy of 0.69.
For machine learning systems, enabling users to act on predictions requires providing detailed explanations of the predictions, which is critical to building user confidence. Stanford's program can automatically generate a report that highlights items in the patient's EHR data that are important for predicting outcomes.
Classification
- Image Manipulation
- Style Transfer
- Image Classification
- Face Recognition
- Video Stabilization
- Object Detection
- Self Driving Car
- Smart recommendation Recommendation Al
- Smart Game Gaming Al
- Smart Chess Chess Al
- Smart Medicine Medical Al
- Smart Speech Speech Al
- Smart Music Music Al
- Natural Language Processing
- Intelligent Prediction
Mybridge AI selected the top 50 out of 20,000 articles on creating machine learning applications. Learning from data scientists with hands-on experience is a great way to share lessons learned about building and operating machine learning applications. The 50 articles can be roughly divided into 15 topics as follows:
Recommended Learning
- The Beginner’s Guide to Building an Artificial Intelligence in Unity.
- Deep Learning and Computer Vision A-Z™: Learn OpenCV, SSD & GANs and create image recognition apps.
Image Manipulation
- High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
- Using Deep Learning to Create Professional-Level Photographs
- High Dynamic Range (HDR) Imaging using OpenCV (Python)
Style Transfer
- Visual Attribute Transfer through Deep Image Analogy
- Deep Photo Style Transfer: A deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style
- Deep Image Prior
Image Classification
- Feature Visualization: How neural networks build up their understanding of images
- An absolute beginner's guide to Image Classification with Neural Networks
- Background removal with deep learning
Face Recognition
- Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression
- Eye blink detection with OpenCV, Python, and dlib
- DEAL WITH IT in Python with Face Detection
Video Stabilization
Object Detection
- How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow, Keras & React Native
- Object detection: an overview in the age of Deep Learning
- How to train your own Object Detector with TensorFlow’s Object Detector API
- Real-time object detection with deep learning and OpenCV
Self Driving Car
- Self-driving Grand Theft Auto V with Python : Intro [Part I] - Sentdex
- Recognizing Traffic Lights With Deep Learning: How I learned deep learning in 10 weeks and won $5,000
Intelligent recommendation Recommendation AI
- Spotify’s Discover Weekly: How machine learning finds your new music
- Artwork Personalization at Netflix
Smart Game Gaming AI
- MariFlow - Self-Driving Mario Kart w/Recurrent Neural Network
- OpenAI Baselines: DQN. Reproduce reinforcement learning algorithms with performance on par with published results.
- Reinforcement Learning on Dota 2 [Part II]
- Creating an AI DOOM bot
- Phase-Functioned Neural Networks for Character Control
- The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI - Stanford University
- Introducing: Unity Machine Learning Agents – Unity Blog
Smart Chess AI
- Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
- AlphaGo Zero: Learning from scratch | DeepMind
- How Does DeepMind's AlphaGo Zero Work?
- A step-by-step guide to building a simple chess AI
Intelligent MedicineMedical AI
- CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
- Can you improve lung cancer detection? 2nd place solution for the Data Science Bowl 2017.
- Improving Palliative Care with Deep Learning - Andrew Ng
- Heart Disease Diagnosis with Deep Learning
Intelligent Speech Speech AI
- Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model - Data Scientists at Google
- Sequence Modeling with CTC
- Deep Voice: Real-time Neural Text-to-Speech - Baidu Silicon Valley AI Lab
- Deep Learning for Siri’s Voice: On-device Deep Mixture Density Networks for Hybrid Unit Selection Synthesis - Apple
Smart Music Music AI
- Computer evolves to generate baroque music!
- Make your own music with WaveNets: Making a Neural Synthesizer Instrument
Natural Language Processing
- Learning to communicate: Agents developing their own language - OpenAI Research
- Big Picture Machine Learning: Classifying Text with Neural Networks and TensorFlow
- A novel approach to neural machine translation - Facebook AI Research
- How to make a racist AI without really trying
Prediction
- Using Machine Learning to Predict Value of Homes On Airbnb
- Engineering Uncertainty Estimation in Neural Networks for Time Series Prediction at Uber
- Using Machine Learning to make parking easier
- How to Predict Stock Prices Easily - Intro to Deep Learning #7
Further reading: "The Machine Learning Master"
- Machine Learning (1): Intelligent recognition of pet pedigree based on TensorFlow
- Machine Learning (2): Using OpenCV with Node.js for Intelligent Pet Recognition
- Machine Learning: Machine Learning Projects
- Machine Learning: Machine Learning Algorithms
- Machine Learning: Machine Learning Book List
- Machine Learning: Machine Learning Reference Collection
- Machine Learning: Machine Learning Technology and Intellectual Property Law
- Machine Learning: AI Media Coverage Collection
- Data visualization (3) Programmatic drawing based on Graphviz
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