machine learning for health informatics

LNAI 9605 Machine Learning for Health Informatics available

NEW – just appeared – NEW

Holzinger, A. (ed.) 2016. Machine Learning for Health Informatics: State-of-the-Art and Future Challenges. Cham: Springer International Publishing, doi:10.1007/978-3-319-50478-0

[book homepage]

Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization.

Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence.

This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.

NIPS 2016

NIPS 2016 is over

A crazy 5700-people event is over: NIPS 2016 in Barcelona. Registration on Sunday, 4th December, on Monday, 5th traditionally the tutorials were presented concluded by the first keynote talk given by Yann LeCun (now director at Facebook AI research) and completed by the official opening and the first poster presentation.  On Tuesday, Dec 6th, after starting with a keynote by Drew Purves (Google Deep Mind), parallel tracks on clustering and graphical models took place concluded by a keynote given by Saket Nevlakha (The Salk Institute) and completed by parallel tracks on deep learning and machine learning theory and poster sessions and demonstrations. Wednesday was openend by a keynote from Kyle Cranmer (New York University), the award talk “matrix completion has no spurious local min” and dominated by parallel tracks on algorithms and applications, concluded by a keynote by Marc Raibert (Boston Dynamics) who presented advances in latest robotic learning, and parallel tracks on deep learning and optimization, completed by the poster sessions with cool demonstrations. The Thursday was opened by a keynote fromm Irina Rish (IBM) and Susan Holmes (Stanford), followed by parallel tracks on interpretable models and cognitive neuroscience, concluded by various symposia until 21:30! Friday and Saturday were the whole day workshops – the sunday was reserverd for recreation on the sand beach of Barcelona 🙂

NIPS is definitely the most exciting conference with amazing variety on topics and themes revolving in machine learning with all sorts of theory and applications.

nips-2016-barcelona-machine-learningnips-2016-barcelona-machnine-learning-gamification

Machine Learning with Fun

Google Research hosts a number of very interesting so-called A.I. experiments, which let you play with machine learning algorithms in a very amusing way, e.g. Quick Draw, where a neural network learns to recognize hand drawn sketches (called doodles), see:

https://quickdraw.withgoogle.com

which is part of the A.I. Experiments platform:

https://aiexperiments.withgoogle.com

and here the explanatory video:
https://www.youtube.com/watch?v=oOwfiYnRi5c

 

Visualization of High Dimensional Data

Google is doing experiments with visualization of high dimenisonal data. This experiment helps visualize what’s happening in machine learning. It allows coders to see and explore their high-dimensional data. The goal is to eventually make this an open-source tool within TensorFlow, so that any coder can use these visualization techniques to explore their data.
Built by Daniel Smilkov, Fernanda Viégas, Martin Wattenberg, and the Big Picture team at Google:
This work is based on a method developed by Laurens van der Maaten & Geoffrey Hinton in 2008:
Maaten, L. V. D. & Hinton, G. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 11, 2579-2605, http://www.jmlr.org/papers/v9/vandermaaten08a.html
t-Distributed Stochastic Neighbor Embedding (t-SNE, spoken: Disney) is a (prize-winning) nonlinear technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional data sets into R2 or R3. The technique can be implemented via Barnes-Hut approximations, allowing it to be applied on large real-world datasets (“big data”).
For details please refer directly to:
Compare this method to our own work on subspace clustering:
Neural Information Processing Systems

Holzinger Group at NIPS

Our crazy iML-Concept has been accepted at the CiML 2016 workshop (organized by Isabelle Guyon, Evelyne Viegas, Sergio Escalera, Ben Hammer & Balazs Kegl) at NIPS 2016 (December, 5-10, 2016)  in Barcelona:

https://docs.google.com/viewer?a=v&pid=sites&srcid=Y2hhbGVhcm4ub3JnfHdvcmtzaG9wfGd4OjFiMGRmNzQ5MmM5MTZhYzE

Obama on humans-in-the-loop

How artificial intelligence will affect jobs

In an discussion on how artificial intelligence will affect jobs, by President Barack OBAMA, the Wired Editor Scott DADICH, and MIT Media Lab Director Joi ITO,  the president demonstrates good understanding of the field and indicates the importance of the humans-in-the-loop, despite all progress of fully automatic approaches.

 

Wired is a monthly tech magazine which reports since 1993 on how emerging technologies may affect culture, politics, economics. Very interesting to note is that Wired is known for coning the popular terms “long tail” and “crowdsourcing”.

More information see:

https://www.wired.com

 

 

 

 

Google releases their Syntactic Parser Open Source

Google researchers spend a lot of time thinking about how computer systems can read and understand human language in order to process it in intelligent ways. On May, 12, 2016 Slav Petrov (expertise) based in New York and leading the machine learning for natural language group (Slav Petrov Page), announced that they released SyntaxNet as an open-source neural network framework implemented in TensorFlow that provides a new foundation for Natural Language Understanding (NLU) . The release includes all code needed to train new SyntaxNet models on own data, as well as Parsey McParseface, an English parser that the Googlers have trained and that can be used to analyze English text. Parsey McParseface is built on powerful machine learning algorithms that learn to analyze the linguistic structure of language, and that can explain the functional role of each word in a given sentence.

Read more:
http://googleresearch.blogspot.co.at/2016/05/announcing-syntaxnet-worlds-most.html

Literature:

Andor, D., Alberti, C., Weiss, D., Severyn, A., Presta, A., Ganchev, K., Petrov, S. & Collins, M. 2016. Globally normalized transition-based neural networks. arXiv preprint arXiv:1603.06042.

Petrov, S., Mcdonald, R. & Hall, K. 2016. Multi-source transfer of delexicalized dependency parsers. US Patent 9,305,544.

Weiss, D., Alberti, C., Collins, M. & Petrov, S. 2015. Structured Training for Neural Network Transition-Based Parsing. arXiv:1506.06158.

Vinyals, O., Kaiser, Ł., Koo, T., Petrov, S., Sutskever, I. & Hinton, G. Grammar as a foreign language. Advances in Neural Information Processing Systems, 2015. 2755-2763.

Deep Learning Playground openly available

TensorFlow – part of the Google brain project – has recently open sourced on GitHub a nice playground for testing and learning the behaviour of deep learning networks, which also can be used following the Apache Licence:

http://playground.tensorflow.org

Background: TensorFlow is an open source software library for machine learning. There is a nice video “large scale deep learning” by Jeffrey Dean.  TensorFlow is  an interface for expressing machine learning algorithms along with an implementation for executing such algorithms on a variety of heterogeneous systems, ranging from smartphones to high-end computer clusters and  grids of thousands of computational devices (e.g. GPU). The system has been used for research in various areas of computer science (e.g. speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, computational drug discovery). The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license on 9th November 2015 and is available at www.tensorflow.org

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J. & Devin, M. 2016. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv preprint arXiv:1603.04467.

It is also discussed on episode 24 of talking machines.

 

 

Human-in-the-Loop

Interactive machine learning for health informatics: when do we need the human-in-the-loop?

Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.” This “human-in-the-loop” can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

We define iML-approaches as algorithms that can interact with both computational agents and human agents *) and can optimize their learning behavior through these interactions.

*) In active learning such agents are referred to as the so-called “oracles”

From black-box to glass-box: where is the human-in-the-loop?

The first question we have to answer is: “What is the difference between the iML-approach to the aML-approach, i.e., unsupervised learning, supervised, or semi-supervised learning?”

Scenario D – see slide below – shows the iML-approach, where the human expert is seen as an agent directly involved in the actual learning phase, step-by-step influencing measures such as distance, cost functions, etc.

Obvious concerns may emerge immediately and one can argue: what about the robustness of this approach, the subjectivity, the transfer of the (human) agents; many questions remain open and are subject for future research, particularly in evaluation, replicability, robustness, etc.

Human-in-the-loop - Interactive Machine Learning

The iML-approach

Read full article here:
https://link.springer.com/article/10.1007/s40708-016-0042-6/fulltext.html
https://www.mendeley.com/catalog/interactive-machine-learning-health-informatics-we-need-humanintheloop