Six months ago we registered a new project on Hackerday and some other places.
The idea was to detect early the heart failure condition, because it is a condition that affects most of us as we age, and there was a lot of material online thanks to various challenges about this subject, like this one:
To create a proof of concept we used a low cost fetal doppler ($50) and a Linux box and were able to record heart sounds without using gel on an adult. So one of the requirement for medical devices was filled: To be ready to be used in seconds.
In most medical devices, there is an implicit requirement: To make the output understandable, it must offer an explanation of the medical statement. So using a black box ML à la Kaggle, is out of question.
In heart sound competition like Physionet 2016, they train HMM in order to create a statistical model of the heart sounds of some condition, HMM can “explain” their internal model by showing the probability a appearance of each state, for example the probability of arrival of a S2 sound at some time after a S1 sound in a particular sequence of heart sounds.
So a HMM model can be used to classify a new sequence of heart sounds either as quite similar to the trained model or not.
One might ask why not using deep learning as it seems to have made wide steps forward recently and as very nice software are available like TensorFlow.
There is a big internal difference between ML using CNNs à la TensorFlow and ML using HMMs, in “an ideal world” a CNN finds its feature without human intervention where a HMM needs that each observation is “tagged” with some human knowledge with a Viterbi or similar function. The tagging is part of what makes the resulting model understandable, however automatic tagging (as in unsupervised learning) is indeed hard.
In truth there is similarity between the design of successful CNNs and HMMs, they have a cost function, however CNNs cost functions do not create meaning.
Designing the cost function of a CNN or the Viterbi function of a HMM is the most important part of any ML setup. All claim that we heard about effectiveness of ML are due to the design of those functions, not to some fancy ML algorithm.
It is a very hard job, far above the state of art.
In order to circumvent this problem, most ML proposals use another ML setup to create the cost function as in most Physionet 2016 challenges or in a recent article that is highly considered in the domain of skin cancer detection: http://www.nature.com/nature/journal/v542/n7639/abs/nature21056.html.
Indeed if one uses ML to create the cost function, the resulting model becomes highly opaque, and medical policy maker, scientists, or specialists will find it useless or even dangerous.
On the long term this practice of using a ML derived cost function will be discouraged, but I suppose this is part of the current hype curve about “deep learning”. It is worse when using small signals ML like in Deep Forest algorithm, where it becomes impossible (today) to reverse engineer the ML model by disturbing it. In addition deep learning cannot be done with a $50 device, it mandates huge computing facilities.
So we created our own Viterbi function for our HMM, and it is quite efficient while being quite simple. The next steps are to improve it, make it more informative and able to move from the Linux box to a microcontroller. Stay tuned.