Category: Artificial Intelligence

Using Machine learning to help Manage Diabetes

I participated in the PennApps hackathon in Philadelphia this weekend. While most of the city was struck with a bad snow storm, a group of hackers holed up inside the Penn engineering buildings to work on some cool hacks. My team consisting of three other hackers: Daniel, Alex and Madhur, decided to work on an app that could predict blood glucose levels of diabetes patients by building machine learning models.

Our proof-of-concept. We have our own logo!
We have our own logo!

We used the OneTouch Reveal API to gather some data provided by the Johnson & Johnson’s company. They are the manufacturers of OneTouch glucose monitors for diabetes patients. They also give their patients an app for tagging events like exercise (light, moderate, heavy etc.), when they eat food and use insulin (different kinds – fast acting, before/after meals etc.). Our team thought that it might be a good idea to hack on this dataset to find out whether we could predict patients’ glucose levels without them having them to punch a hole in their fingers. A real world use case for this app would be to alert a patient when we predicted unusual glucose levels or have them do an actual blood test when the confidence on our predictions falls low.

We observed mixed results for the patients in our dataset. We did reasonably well for those with more data, but others had very few data points to make good predictions. We also saw that our predictions became more precise as we considered more data. Another issue was that the OneTouch API did not give sufficient information about food and exercise events for any of the patients – mostly without additional event tagging. As a result, our models were not influenced much by them.

The trend line is our blood glucose prediction based on past data. The shaded region indicates our prediction range. Whenever this region is broader, our confidence in prediction goes down.
The pink trend-line is our blood glucose predictions based on prior data. The shaded region indicates the prediction range. Whenever this region is broader, our confidence in prediction goes down.

We believe that in the near future, it would be common for the patients to have such monitors communicate with other wearable sensors such as smart watches. Such systems would be able to provide ample information about one’s physical activity etc., to make more meaningful predictions possible. Here’s a video demonstrating our proof-of-concept:

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Interactive Natural Language Processing for Legal Text

Update: We received the best student paper award for our paper at JURIX’15!

In an earlier post, I talked about my work on Natural Language Processing in the clinical domain. The main idea behind the project is to enable domain experts to build machine learning models for analyzing text. We do this by designing usable tools for NLP without really having the need to send datasets to machine learning experts or understanding the inner working details of the algorithms. The post also features a demo video of the prototype tool that we have built.

I was presenting this work at my program’s bi-weekly meetings where Jaromir, a fellow ISP graduate student, pointed out that such an approach could be useful for his work as well. Jaromir also holds a degree in Law and works on building AI systems for legal applications. As a result, we ended up collaborating on a project on using the approach for statutory analysis. While, the main topic of discussion in the project is on the framework in which a human experts cooperate with a machine learning text classification algorithm, we also ended up augmenting our approach with a new way of capturing and re-using knowledge. In our tool datasets and models are treated separately and our not tied together. So, if you were building a classification model for say statutes from the state of Alaska, when you need to analyze laws from Kansas you need not start from scratch. This allows us to be in a better starting place in terms of all the performance measures and build a model using fewer training examples.

The results of the cold start (Kansas) and the knowledge re-use (Alaska) experiment. In the Figure KS stands for Kansas, AK for Alaska, 1p and 2p for the first (ML model-oriented) and second (interaction-oriented) evaluation perspectives, P for precision, R for recall, F1 for F1 measure, and ROC with a number for an ROC curve of the ML classifier trained on the specified number of documents.
The results of the cold start (Kansas) and the knowledge re-use (Alaska) experiment. In the Figure KS stands for Kansas, AK for Alaska, P for precision, R for recall, F1 for F1 measure, and ROC with a number for an ROC curve of the ML classifier trained on the specified number of documents.

We will be presenting this work at JURIX’15 during the 28th year of the conference focusing on legal information systems. Previously, we had presented portions of this work at the AMIA Summit on Clinical Research Informatics and at the ACM IUI Workshop on Visual Text Analytics.

References

Jaromír Šavelka, Gaurav Trivedi, and Kevin Ashley. 2015. Applying an Interactive Machine Learning Approach to Statutory Analysis. In Proceedings of the 28th International Conference on Legal Knowledge and Information Systems (JURIX ’15). Braga, Portugal. [PDF] – Awarded the Best Student Paper (Top 0.01%).

Machines learn to play Tabla

Update: This post now has a Part 2.

If you follow machine learning topics in the news, I am sure by now you would have come across Andrej Karpathy‘s blog post on The Unreasonable Effectiveness of Recurrent Neural Networks.[1] Apart from the post itself, I have found it very fascinating to read about the diverse applications that its readers have found for it. Since then I have spent several hours hacking with different machine learning models to compose tabla rhythms:

Although Tabla does not have a standardized musical notation that is accepted by all, it does have a language based on the bols (literally, verbalize in English) or the sounds of the strokes played on it. These bols may be expressed in written form which when pronounced in Indian languages sound like the drums. For example, the theka for the commonly used 16-beat cycle – Teental is written as follows:

Dha | Dhin | Dhin | Dha | Dha | Dhin | Dhin | Dha 
Dha | Tin  | Tin  | Ta  | Ta  | Dhin | Dhin | Dha

For this task, I made use of Abhijit Patait‘s software – TaalMala, which provides a GUI environment for composing Tabla rhythms in this language. The bols can then be synthesized to produce the sound of the drum. In his software, Abhijit extended the tabla language to make it easier for users to compose tabla rhythms by adding a square brackets after each bol that specify the number of beats within which it must be played. You could also lay more emphasis on a particular bol by adding ‘+’ symbols which increased their intensity when synthesized to sound. Variations of standard bols can be defined as well based on different the hand strokes used:

Dha1 = Na + First Closed then Open Ge

Now that we are armed with this background knowledge, it is easy to see how we may attempt to learn tabla like a language model using Natural Language Processing techniques. Predictive modeling of tabla has been previously explored in "N-gram modeling of tabla sequences using variable-length hidden Markov models for improvisation and composition" (Avinash Sastry, 2011). But, I was not able to get access to the datasets used in the study and had to rely on the compositions that came with the TaalMala software.[2] This is comparatively a much smaller database than what you would otherwise use to train a neural network: It comprises of 207 rhythms with 6,840 bols in all. I trained a char-rnn and sampled some compositions after priming it with different seed text such as “Dha”, “Na” etc. Given below is a minute long composition sampled from my network. We can see that not only the network has learned the TaalMala notation but it has also understood some common phrases used in compositions such as the occurrence of the phrase “TiRa KiTa“, repetitions of “Tun Na” etc.:

Ti [0.50] | Ra | Ki | Te | Dha [0.50] | Ti [0.25] | Ra | Ki
| Ta | Tun [0.50] | Na | Dhin | Na 
| Tun | Na | Tun | Na | Dha | Dhet | Dha | Dhet | Dha | Dha
| Tun | Na | Dha | Tun | Na | Ti | Na | Dha | Ti | Te | Ki |
Ti | Dha [0.50] | Ti [0.25] | Ra | Ki | Te | Dhin [0.50] |
Dhin | Dhin | Dha | Ge | Ne | Dha | Dha | Tun | Na | Ti
[0.25] | Ra | Ki | Ta | Dha [0.50] | Ti [0.25] | Ra | Ki |
Te | Dha [1.00] | Ti | Dha | Ti [0.25] | Ra | Ki | Te | Dha
[0.50] | Dhet | Dhin | Dha | Tun | Na | Ti [0.25] | Ra | Ki
| Ta | Dha [0.50] | Ti [0.25] | Ra | Ki | Te | Ti | Ka | Tra
[0.50] | Ti | Ti | Te | Na [0.50] | Ki [0.50] | Dhin [0.13]
| Ta | Ti [0.25] | Ra | Ki | Te | Tra | Ka | Ti [0.25] | Ra
| Ki | Te | Dhin [0.50] | Na [0.25] | Ti [0.25] | Ra | Ki |
Te | Tra | Ka | Dha [0.34] | Ti [0.25] | Ra | Ki | Ta | Tra
| Ka | Tra [0.50] | Ki [0.50] | Tun [0.50] | Dha [0.50] | Ti
[0.25] | Ra | Ki | Ta | Tra | Ka | Ta | Te | Ti | Ta | Kat |
Ti | Dha | Ge | Na | Dha | Ti [0.25] | Ra | Ki | Te | Dha
[0.50] | Dhin | Dhin | Dhin | Dha | Tun | Na | Ti | Na | Ki
| Ta | Dha [0.50] | Dha | Ti [0.50] | Ra | Ki | Te | Tun
[0.50] | Tra [0.25] | Ti [0.25] | Ra | Ki | Te | Tun | Ka |
Ti [0.25] | Ra | Ki | Te | Dha [0.50] | Ki [0.25] | Ti | Dha
| Ti | Ta | Dha | Ti | Dha [0.50] | Ti | Na | Dha | Ti
[0.25] | Ra | Ki | Te | Dhin [0.50] | Na | Ti [0.25] | Ra |
Ki | Te | Tra | Ka | Dha [0.50] | Ti [0.50] | Ra | Ki | Te |
Tun [0.50] | Na | Ki [0.25] | Te | Dha | Ki | Dha [0.50] |
Ti [0.25] | Ra | Ki | Te | Dha [0.50] | Ti [0.25] | Ra | Ki
| Te | Dha [0.50] | Tun | Ti [0.25] | Ra | Ki | Te | Dhin
[0.50] | Na | Ti [0.25] | Te | Dha | Ki [0.25] | Te | Ki |
Te | Dhin [0.50] | Dhin | Dhin | Dhin | Dha | Dha | Tun | Na
| Na | Na | Ti [0.25] | Ra | Ki | Ta | Ta | Ka | Dhe [0.50]
| Ti [0.25] | Ra | Ki | Te | Ti | Re | Ki | Te | Dha [0.50]
| Ti | Dha | Ge | Na | Dha | Ti [0.25] | Ra | Ki | Te | Ti |
Te | Ti | Te | Ti | Te | Dha [0.50] | Ti [0.25] | Te | Ra |
Ki | Te | Dha [0.50] | Ki | Te | Dha | Ti [0.25]

Here’s a loop that I synthesized by pasting a composition sampled 4 times one after the another:

Of course, I also tried training n-gram models and the smoothing methods using the SRILM toolkit. Adding spaces between letters is a quick hack that can be used to train character level models using existing toolkits. Which one produces better compositions? I can’t tell for now but I am trying to collect more data and hope to add updates to this post as and when I find time to work on it. I am not confident if simple perplexity scores may be enough to judge the differences between two models, specially on the rhythmic quality of the compositions. There are many ways in which one can extend this work. One there is a possibility of training on different kinds of compositions: kaidas, relas, laggis etc., different rhythm cycles and also from different gharanas. All of this would required collecting a bigger composition database:

And then there is a scope for allowing humans to interactively edit compositions at places where AI goes wrong. You could also use the samples generated by it as an infinite source of inspiration.

Finally, here’s a link to the work in progress playlist of the rhythms I have sampled till now.

References

  1. Avinash Sastry (2011), N-gram modeling of tabla sequences using variable-length hidden Markov models for improvisation and composition. Available: https://smartech.gatech.edu/bitstream/handle/1853/42792/sastry_avinash_201112_mast.pdf?sequence=1.

Footnotes

  1. If you encountered a lot of new topics in this post, you may find this post on Understanding natural language using deep neural networks and the series of videos on Deep NN by Quoc Le helpful. ^
  2. On the other hand, Avinash Sastry‘s work uses a more elaborate Humdrum notation for writing tabla compositions but is not as easy to comprehend for tabla players. ^

Clinical Text Analysis Using Interactive Natural Language Processing

Update: Here’s our full paper announcement with source-code release…

I am working on a project to support the use of Natural Language Processing in the clinical domain. Modern NLP systems often make use of machine learning techniques. However, physicians and other clinicians, who are interested in analyzing clinical records, may be unfamiliar with these methods. Our project aims to enable such domain experts make use of Natural Language Processing using a point-and-click interface . It combines novel text-visualizations to help its users make sense of NLP results, revise models and understand changes between revisions. It allows them to make any necessary corrections to computed results, thus forming a feedback loop and helping improve the accuracy of the models.

Here’s the walk-through video of the prototype tool that we have built:

At this point we are redesigning some portions of our tool based on feedback from a formative user study with physicians and clinical researchers. Our next step would be to conduct an empirical evaluation of the tool to test our hypotheses about its design goals.

We will be presenting a demo of our tool at the AMIA Summit on Clinical Research Informatics and also at the ACM IUI Workshop on Visual Text Analytics in March.

References

  1. Gaurav Trivedi. 2015. Clinical Text Analysis Using Interactive Natural Language Processing. In Proceedings of the 20th International Conference on Intelligent User Interfaces Companion (IUI Companion ’15). ACM, New York, NY, USA, 113-116. DOI 10.1145/2732158.2732162 [Presentation] [PDF]
  2. Gaurav Trivedi, Phuong Pham, Wendy Chapman, Rebecca Hwa, Janyce Wiebe, Harry Hochheiser. 2015. An Interactive Tool for Natural Language Processing on Clinical Text. Presented at 4th Workshop on Visual Text Analytics (IUI TextVis 2015), Atlanta. http://vialab.science.uoit.ca/textvis2015/ [PDF]

  3. Gaurav Trivedi, Phuong Pham, Wendy Chapman, Rebecca Hwa, Janyce Wiebe, and Harry Hochheiser. 2015. Bridging the Natural Language Processing Gap: An Interactive Clinical Text Review Tool. Poster presented at the 2015 AMIA Summit on Clinical Research Informatics (CRI 2015). San Francisco. March 2015. [Poster][Abstract]

Learning from multiple annotators

I recently prepared a deck of slides for my machine learning course. In the presentation, I talk about some of the recently proposed methods on learning from multiple annotators. In these methods we do not assume the labels that we get from the annotators to be the ground truth, as we do in traditional machine learning, but try to find “truth” from noisy data.

There are two main directions of work in this area. One focuses on finding the consensus labels first and then do traditional learning, while the other approach is to learn a consensus model directly. In the second approach, we may estimate the consensus labels during the process of building a classifier itself.

Here are the slides for the presentation. I would be happy to receive your comments and suggestions.


Quoc Le’s Lectures on Deep Learning

Update:  Dr. Le has posted tutorials on this topic: Part 1 and Part 2.

Dr. Quoc Le from the Google Brain project team (yes, the one that made headlines for creating a cat recognizer) presented a series of lectures at the Machine Learning Summer School (MLSS ’14) in Pittsburgh this week. This is my favorite lecture series from the event till now and I was glad to be able to attend them.

The good news is that the organizers have made available the entire set of video lectures in 4K for you to watch. But since Dr. Le did most of them on a blackboard and did not provide any accompanying slides, I decided to put the brief content descriptions of the lectures along with the videos here. Hope this will help you navigate the videos better.

Lecture 1: Neural Networks Review

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Dr. Le begins his lecture starting from the fundamentals on Neural Networks if you’d like to brush up your knowledge about them. Otherwise feel free to quickly skim through the initial sections, but I promise there are interesting things later on. You may use the links below to skip to the relevant parts. The links are using an experimental script, let me know in the comments if they don’t work.

Contents

Lecture 2: NNs in Practice

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If you have already covered NN in the past then the first lecture may have been a bit dry for you but the real fun begins in this lecture when Dr. Le starts talking about his experiences of using deep learning in practice.

Contents

Lecture 3: Deep NN Architectures

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In this lecture, Dr. Le finishes his description on NN architectures. He also talks a bit about how they are being used at Google – for applications in image and speech recognition, and language modelling.

Contents

Talk: The Signal Processing Approach to Biomarkers

A biomarker is a measurable indicator of a biological condition. Usually it is seen as a substance or a molecule introduced in the body but even physiological indicators may function as dynamic biomarkers for certain diseases. Dr. Sejdić and his team at the IMED Lab work on finding innovative ways to measure such biomarkers. During the ISP seminar last week, he presented his work on using low-cost devices with simple electronics such as accelerometers and microphones, to capture the unique patterns of physiological variables. It turns out that by analyzing these patterns, one can differentiate between healthy and pathological conditions. Building these devices requires an interdisciplinary investigation and insights from signal processing, biomedical engineering and also machine learning.

Listening to the talk, I felt that Dr. Sejdić is a researcher who is truly an engineer at heart as he described his work on building an Asperometer. It is a device that is placed on the throat of a patient to find out when they have swallowing difficulties (Dysphagia). The device picks up the vibrations from the throat and does a bit of signal processing magic to identify problematic scenarios. Do you remember the little flap called the Epiglotis that guards the entrance to your wind pipe, from your high school Biology? Well, that thing is responsible for directing the food into the oesophagus (food pipe) while eating and preventing it from going into wrong places (like the lungs!). As it moves to cover the wind pipe, it records a characteristic motion pattern on the accelerometer. The Asperometer can then distinguish between regular and irregular patterns to find out when should we be concerned. The current gold standard to do these assessments involve using some ‘Barium food’ and X-Rays to visualize its movement. As you may have realized, the Asperometer is not only unobstrusive but also appears to be a safer method to do so. There are a couple of issues left to iron out though, such as removing sources of noise in the signal due to speech or even breathing through the mouth. We can, however, still use it in controlled usage scenarios in the presence of a specialist.

The remaining part of the talk briefly dealt with Dr. Sejdić’s investigations of gait, handwriting processes and preference detection, again with the help of signal processing and some simple electronics on the body. He is building on work in biomedical engineering to study age and disease related changes in our bodies. The goal is to explore simple instruments providing useful information that can ultimately help to prevent, retard or reverse such diseases.