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Deep learning meets rheumatology: new methods to predict disease progression in arthritis
Maria Hügle
Friday, Feb. 1, 2019 · 2:38 p.m. · 18m 25s
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so good i know no one ah i'm really happy to
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talk about how to combine the learning environment apology today
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because over the last couple of years i kept telling to mass that that beep learning is really
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hot topic and powerful tool and and that you should try to apply to what methodology
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of course somehow this uh you should try became we should try and basically that's where i'm standing here right now
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so end of last year we started the project together with class bond was also here today
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was writing is master thesis about how to put the that is useful
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question enough devices with machine learning methods and especially keep learning methods
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so i'll in my talk i want to introduce you to deep learning
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so what is steep learning how does it work in principle
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and how could you use it and how could you use it in the future foreman told she
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and in the second part of my talk i will show you some preliminary results we have in the ongoing work with class
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so i'll i'll motivation to try to put the the disease progression the fight is this
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that for specific patient we don't know how and how fast the disease will develop
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so a better prediction model would help us to choose
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i personally i stopped nice treatment uh for for patients
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and we're interested in trying to put it uh the clinical question oh of uh f. right is
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so like pain or swelling um and there we we we use the past twenty eight score
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but we also interested in uh putting the um weight your
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graphic question like bone evolution swear use the atoms cool
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lot degenerative changes where the catalogue score is quite interesting and
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we want to do this with machine learning methods
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so i'll let us machine learning um machine learning means that you allow
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computers to act without any hardcoded rules so you give the computer
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a lot of training training examples and he's able to learn from these
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examples to improve and two channel lies to new unseen data
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and um machine consists of a different sub fields and one of them is called supervised learning
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and there you try to learn the function from labelled examples so for
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example if you wanna do disease detection on x. ray images
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then you need let's say a couple of thousand images and you need an expert it
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doctor who lay good these images so if there's just a disease visible or not
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and if you have these label these examples and then you can learn more than you can learn
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this function and you can channel lies to new x. ray images you never saw before
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and i'm not this appeal of machine learning is called unsupervised learning
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and then you try to learn functions and structures and patterns from data
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but you don't have a teacher who tells you what these patterns are beforehand so you wanna find them in the first place
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and uh for a interesting area which i'm usually working on is that
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is reinforcement learning so you're right about that already in the top
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and yeah you try to learn strategy how to into acting a
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certain situation in order to maximise you expected future we churned
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and what this return is you have to define by yourself so you
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need to feedback of how good or how bad your action was
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and at this feedback can also be quite late so for example as in chess and when you
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only have one feedback in the end if you we know if you lost the game
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and running is not there's subfield of of machine learning and you can see it as
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a tool uh that you can apply in all these areas i showed you
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and in deep learning you use artificial peep neural networks in
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only two with percent these functions you wanna learn here
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about later more about that so let's first to look at some applications of machine learning
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so one famous one is uh for example the speech recognition like in serial excel we're going now
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but of course they a lot more so what you can do is uh you can do classification
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so that supervised learning uh where you try to classify what's in the
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images to seek and this is the topic is the person
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but you can also do face recognition as the i. phone x. that's when you try uh to unlock the i. phone
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and you can not only and categories you can also
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learn a wheel values for example you can predict
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that the traffic flow of two more or you can predict that the stock price of two more
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and they're um also unsupervised learning methods like
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class doing so you can try to put the recommended system like in m. s. and
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well uh you can try to do outlier detection so you can for
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example try to find anomalies likely nietzsche data or something like that
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what you can try to find yeah the most important information you have in in your data so um
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for example you can try to find what is the most important input feature
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i'm responsible for the decision or a few off your network of your classifier
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oh and finally in reinforcement learning um yeah you can learn strategies
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you can learn to live in strategies you can learn
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to control what what's wise we got already or you can
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learn to play chess or even possible complicated a call
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but um how about grammar told she's of what could we do here so
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um for example we could try to detect f. y. 'cause on x. ray niches
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what we can try to you to put it uh d. b. kyle score from
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what to to compute the kyle score from from x. ray images automatically
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well yeah we call a step further and this is our project what we're currently working on
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um we try to to predict the disease progression so we try to
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put it how these targets course we develop in the future
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and um there are a lot of other method you paid to us an unsupervised
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learning but today i want to focus on unsupervised learning and if you i
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the chip would take how the deceased will develop maybe then you also uh
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able to put take how the disease will develop under a certain medication
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and then um then it's not far away from from learning a a
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good treatment stellar optimised treatment strategy uh with reinforcement learning and
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this uh would be yeah local they actually however this is hard to
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evaluate because all you would need a wheel control group for that
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so i'll coming back to keep learning um so we had already
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that uh in the planning you use a deep neural networks
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to learn functions from more input data so it's nothing else but
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it but a function that maps some input to some output
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and it consists of of many layers of artificial onions and that the subdivision humans
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are simply my uh loosely motivated by the new ones in the human right
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some have some input you bake these inputs then you
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compute uh the weighted sample wall inputs and
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let's say you're using a binary step function su activation function then the new and fires
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if there's some the weighted sum exceeds a certain certain threshold and if not it doesn't fly
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so today we are using more complicated activation functions that are
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unknown in yeah and daddy why people but you can
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you can see like this so the principle but more input the more comes out the more opinion files
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and then you um whatever these new ones in a neural network which you can see on the white side so this
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is a fully connected neural network revenue and i never really is connected to every new in in the next meeting
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and then we make these networks to to learn found data by adapting the
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these weights w. that that at every connection here you can see
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and we try to to minimise scenario function is ever function is dependent
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on on your problem is you have to define by yourself
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and you can see that you have a lot of weight here um especially if if your
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network gets deep and this is the reason why people earning became successful this late
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um because we need a lot of computing power for that and really good cheap news
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so but how can we apply this to to a disease progression in production in
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the fight is so we use up patients data collected informal was it
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we feel this into neural network and train it to output the targets cross so this can be
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the cause score that atoms call all the dust and a scroll off the next reads it
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so all we can say for quite bastion yeah i put h. and uh we use uh uh like the age of the patients
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away with what my fact your number of swollen joints and so
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on a for all the receipts we we have information about
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and for that we can take also the the um the medication into account so what'd you take and for how long
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and so on and then we feed this into a neural network and train
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it to output it as trendy age for um for the next receipt
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so i'll out what it is that um is the s. e. g. m. database
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and this consists of um yeah a lot of data um about um of
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about uh i thousand patients and forty five thousand which visits and um
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for training and evaluation be use five for cross validation so this means
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that uh we train our network let's say five times of of course you can also
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say ten times um and train it on the first twenty percent of the patients
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and this is no sorry and tested on the first twenty percent of the
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patients this is our testing set as training set we use the rest
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so in the next trend we use not the testing set and we trained on all the other patients and so on and so on
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and this avoids over fitting a two very small testing set and then
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we just use the mean without of all these different falls
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and um as baselines we just use a very naive
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baseline very simply assume that the target score doesn't
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change so fast and it stays the same so this does that is useful or not develop
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um and all for the um you heard about this also did
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they use a rainforest which is not the machine learning method
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so um now i want to show you some preliminary results so again this is
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um ongoing work and we still have to work a lot of it
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uh on it but yeah we to be honest we had a really hard time
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trying to put it um that the progression and this is professional fighters
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um it's not that easy but um we do it broadcast twenty eight
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um and um transform to trek classification problem
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so we can put date if the disease will uh get better or worse
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uh right now with the one seventy percent accuracy so
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set to seventy percent um our predictions are correct
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and the same what uh we use uh right now a fixed number of let's say the lies last five for that
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and the last two medications and we trained that on over a trendy thousand receipts and um
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yeah and then i would it is um with the neck where when forced on the
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network and compared then to to the naive baseline by computing the mean squared error
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and this is nothing else but the the mean deviation from
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our predictions to the true well use uh we get
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and you can see that um the network is outperforming the others uh with the means fear of zero point eight
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and yeah this is good but actually we want to yeah to get better of course
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and i hope we will so on the white side you can see um
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the differences between the protection and the true das trendy eight well you
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have the next with it for the baseline into the wall
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and uh for the network in in all um so what we can see is that
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we have trouble else i'm detecting small changes in the weather here but um
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larger changes in in the task or we we can um detect right um correct
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yeah and and then we we did the same for the kyle score um
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there we had a lot less training examples the only four thousand
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i say only because what keep learning methods it's not that much um
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yeah and um they are uh we actually we out performing the knife
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baseline with both methods but other when enforce showed the best weasels
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but yeah we're working on that and again here we we had some troubles because
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and actually thought for most of the patients other squat doesn't change oil
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only change very slightly so it's hard to put it something else
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um so one other thing i want to show you is that
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if you're using machine learning methods or for example ever enforced
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you can compute how important your input features ah for the decision off of your classifier
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so um we don't know yet which maybe kate kate mitigation is sufficient or not
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or which one could be could be the best but what we know is
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that when you look at the feature importance is for different medications
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and that they played different goals in the next receipt and our goal is to go
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from there and to find almost out okay which one could be the best
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so finally i just want to show you some examples of our predictions so you can
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um see the disease development of the patients um
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all bought six to um twelve years
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into you can see uh the the um should but so the the actual
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development of options disease and all when she can see um our predictions
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so we have troubles i as i said detecting small changes
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but um in channel uh i'm largely trends we we are predicting already um correctly
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yeah so you can see also see that the predictions are pretty late but um i think in principle we showed
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um that is a is a good approach and it uh that it has hypertension
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so out just to conclude my talk um i think we we took care
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of the first steps towards the person my son optimise treatments veggie
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um in in one ontology with machine learning methods and with the
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s. if you haven't database um we have a large
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enough and and i hope it high quality they just sat on to apply a machine learning and keep learning methods
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of course there's still um a lot of 'em missing data we could also consider lied
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like utilities and source or a patient reports was for sound and so on
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but we still have a lot of work to do um with the data we have like we have to improve
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our our models our network we have to to find the right yeah um input features maybe together with thomas
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uh we want to include x. y. images and finally want to use reinforcement learning
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so um i think that it's a it's a really promising approach and i'm
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really looking forward to see how far we can get with it
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uh thank you very much for your tension ah
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propose to perk or mm reproducing prefer married or are really question for more not really
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f.
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but should it's a high quality treatable sure how how many do it
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with a secret shop for breast want to start with an issue
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or with the i mean the more related to the things that you listed you yeah so i think for best friend did we we could
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oh actually train on um on a trendy thousand samples were a bit more so
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um this is quite enough i would say of course we have the same problems um but uh mentioned uh that
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we mentioned before was so they're missing values so missing of features so we have to deal with that and
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yeah and i'm not the point is for example okay for for every time
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point you have of for every patient you have a different number of
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of course is you have to consider and these are all problems uh where
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we have to find the right model that can deal with this
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and at the right network architecture and yeah i i hope um
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i hope that um yeah we have enough data actually
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there is a question but rubber your finger remote for reduction to a
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maturing and the wizard return and you know very easy to to
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understand for one um you just have a question as regards oh
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joe training so because um sometimes remote you learning algorithm structures
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people are you have a huge amount of multiple parameters right yeah exactly the tool
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to most of them you know just a little bit about the musician
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mainly due to a different two completely different muscles or have your reviewed so
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you some some of these kind of irritated now actually not so
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uh we we just started and which is using a very simple
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feed forward neural network so very simple architecture where we can't you with a variable number of
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of a form of visits and of medications but we just wanted to see it
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um that you get first results and see how far we can get uh now we have to work on that
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i mean and and then of course i permit optimisation will be uh another topic
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we have to deal with yes thank you figure much any other question
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okay so reason more temperate
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from police were comparable so pull off slur her with her for a pretty
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division carpentry term or as you know our target has to be
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efficient mood also so you've so you would prefer also are really diverse
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