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thank you very much a constant dollars for inviting me to this wonderful meeting
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uh you do have the digital los for what's a hot i think
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and i think we are privileged here because this will be the first off i think many meetings
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in the future on this topic this is really going to be groundbreaking in my a few
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um so we've been involved in a year project and um it left
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on the how to predicts disease flies in patients with rheumatoid arthritis
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using routinely collect it a clinical data and this really
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i think brings home the message that was uh just uh brought up the importance of data collection
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um should do next
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it's not ah sir you okay so uh it's just one disclosure i do
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also have some other consultants these activities i didn't uh mentioned then
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my main disclosure is that i'm really a clinical roommate always just and even though don't from my say on the
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best i'm not an a. i. expert so i work
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in a team with a i experts and statisticians
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i really do not understand too much of what they do behind the computer
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but i do know the general principles and of course i
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can hopefully explain the results of our efforts to you
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so this is for those of you were not remote all this just to their
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rheumatoid arthritis is the most common inflammatory joint
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disease it's a relapse ingrid meeting
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this is in many patients and it's very difficult to predict less and is really
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has a negative impact on treatment because you would ideally like to prevent flats
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and tape or your medication your anti rheumatic medication accordingly
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it's also a chronic disease so you really need to manage this disease long
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term i need to be aware of potential long term side effects
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um and then it's important to note that disease activity there's no golden stand that we are using
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all sorts of imaging technologies these days to try to come up with the gold standard
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but the gold standard is still the clinical assessments by
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a doctor or trains notice of joint activity
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and the reporting of the patients off disease activities
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are very crude old fashions uh tools
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that word developed by the way by a paid for real in a
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nine making about twenty years ago so for about twenty years
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we've been using this old fashioned clinical to and this serves as best better than
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all sorts of novel in the imaging technology it's it's worth remembering that
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and this is what we think we could uh see in the
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future when we have a proper to a computer tool
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that can serve as a um clinical decision support system
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so patient comes to the clinic disease activity is assessed and then
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the data from the clinical disease activity are entered into the a patient record of the patients
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and the computer comes back with a kind of a risk score which pretext
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the risk of a flare let's say in the coming three months
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and then you can maybe treat your patience accordingly and up the
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um the medication if the risk of a flat is high
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or maybe you can tape or the medication if the risk of larry slow or continue with the same those
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and i we think this could really have a a clinical use in our packed this
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and then we can give to patients tailored advice i don't think the computer
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as was said previously will ever replace a doctor doctors have been around for thousands of
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years and human beings need another human being good to communicate that's the basic fact
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so our hypothesis was that a flare of disease can
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be predicted based on information about disease activity
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about patient disease characteristics and treatment already present in
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the uh clinical uh electronic patient files
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so these are the potential scenarios that can be um the use for this production
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model you could start tapering patients on full dose of lotus is activity
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or you could stop tapering impatience you all ready tapered and in this case biology goals
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are mentioned because these are expensive trucks uh about ten thousand years per year
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and so if you can tailor to doze back to it may also the talk to work cost savings
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and the last and there is that you could increase the dose and patience you already tape it a biological
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but haven't an acceptable risk of flaring and then of course the question is what is
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an unacceptable risk of flaring and i think that's very patient and doctor specific
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and you can also use this to plan the next visit if the risk of less very low you could say to
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the patient will come back in six months or twelve months because you risk of a flare is very low
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and again essays uh the patient travel time and also cost for the hospital
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so this is what we did we have a a big uh research data
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platform in it where where all routine clinical data are automatically entered
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and we extract this data from patients with the diagnosis
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of rheumatoid arthritis you it started biological treatments
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and to became eligible for tapering or stopping biological
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if they're disease activity uh met certain criteria
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and as an outcome measure we useful as now this is another uh a little uh difficulty because
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the uh this definition of flare is very subjective
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and only recently has an international task force
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come up with some kind of definition for flare but mind you
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this is again dependent on a lot of subjective parameters
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and this is what we had we uh found seventeen hundred seventy
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eight r. i. patience in our uh recess take the platform
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the ones with biological or five hundred and eighty eight the ones eligible for tapering were three hundred to fourteen
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and the patients with sufficient number of data on these parameters were three hundred
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and mind you there were some limitations because a bass this is active the score was not done
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it all patients at all this is in fact in only forty percent of the patient visits
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and state on smoking where difficult to retrieve and that's
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because our electronic the uh a data file
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allows doctors to find ten spots to enter the data so this is crazy this is a national
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software developer who made i think a a basic error to allow too many options in the database so
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it's very difficult to extract data from distant places and we decided to basically not do it
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and also data on be a miser body mass index we're
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missing also a lot and that also always puzzles me
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that doctors do not always measure length or height and weight anymore or blood pressure
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i don't know whether this is a unique experience itself but i'm i'm just if i suspect it's a generational thing
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so we we decide it's too would do a little cup competition this was
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a joint effort with a siemens health in yes from holland and germany
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and uh we have a whisk it it it acts parker well sing with a last uh uh by statistician
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and the expert in joint modelling and this is what joint modelling is but essentially it uses
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data from the past and the present to predict the future so it's not only
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you're doing a kind of cross sectional data analysis of the patients it it also
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takes all the data from the parts of this patients into the future
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and you can do sophisticated joint modelling to predict a disease course in the future
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the um the downside of the system is that it's a it's a laborious
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and it doesn't allow you to enter too many parameters so there's a maximum
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kind of number of parameters that you can enter in the system
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on the other hand we had um a awesome yeah from a siemens who is another was good from
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um is pa country always a lot of uh which 'cause they're um
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who moved to germany and he is an expert in machine learning
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and see you use independence as so he didn't independent study using the same database
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using the difference technique to come up with different similar kind of uh parameters
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and this is uh what the models look like on the right
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you see the kind of the the test uh models results
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i would show the variation if you select certain uh uh um sub populations
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from the whole population so in a sense you're looking at how robust
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is my model to predict um the future um you know
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if i do a a similar study it another population
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and on the left you see a table with all the parameters that were used in as i
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mentioned that were more parameters used in machine learning because that essentially allows you to do anything
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with any sort of data and on the right you see the parameters that
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were selected by parker was by the way experienced rheumatoid arthritis research or
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so he knows which parameters are importance where's awesome yeah it's
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another remote arthritis specialist to use it as she learned
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or data scientists the just and the steps anything in in in the been a looks at the outcome
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so this is just to give you a flavour of wood what what could happen in in daily life
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um on the uh left access to y. axes use easy disease activity spores
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grey means that the patient is using a biological white means that there's no why logical no truck
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and the red line and reflects the rest of a flare in the patients uh what
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you could see in the middle is that in this patient the biological was tapered
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and you can say that those was type it to zero but in fact the machine or the
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the model would predict the fly all ready before you decide it's too uh wait and say
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so what this means is that uh what this tells you you can essentially
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allow the machine to protect the risk of a recent flare and instructs the doctor not to
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um you know a wait and see but instead and change the medication and aspirations
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now this was like i'm sorry was made by a parker results a user data
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scientists are miller's uh numbers and letters so this is too much i know
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oh i borrow this five from him but this is the validation co what so we tested our model in
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our own coordinate left and we then decided to do
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a validation study in independence clinical trial population
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so these patients were of very well monitored in contrast to the patient from the messy clinics in our hospitals
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and this was a study where patients receive the biological
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an idea where continued on the biological according
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to usual care or what tapered and and hopefully
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the tape it's completely off the medication
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and this study showed that you can actually reduce biological in fifty
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percent of the patients without major harm to the patients
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and if the patient develops a flare you can start a biological again and then disease activity will go down so this
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is one of the landmark studies in remote all okay we
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showed that it you can in patients with stable disease
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tape or biological but it comes at the price and the price is the
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risk of flaring and this price needs to be discussed with the patient
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uh_huh
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and this was the initial results from the the the two models
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of the um the the machine learning and a joint modelling
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and so on the top u. c. d. um uh the results in the u. m. c. u. data
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i'm pretty nice but if you apply to the data to be trance population the validation
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pro what you found we found that the joint modelling approach was still good
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that's the uh data for machine learning we're not so
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good anymore however if we use the dress population
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to train also the machine learning program the result of the machine learning became each week equally good
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so we concluded that the external validation you'll it unexpected pasta
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results for joint modelling when compared to the machine learning
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and we think that this is a hypothesis that a joint modelling maybe better
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with clean data such as a clinical trial with complete data set
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worse machine learning you can handle can handle missing data
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but it imp use these data uh at random or some kind of us algorithms
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um so it may not be as good um if you use this in um in in this other population
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so um this is the kind of the whole results of this uh study
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so on the top left sorry on the top uh left you see the results from the clinical trial
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and the top uh down you see the results from the uh machine
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learning tapering and the joint mumbling tapering and what what this earth
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slide shows is that you can indeed use these models to reduce the number of less
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and also reduce the number of patients flattering when compared to
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complete paper it so just blindly tapering all patients
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will increase the risk of last and will also increase the number of
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patients flaring and in fact you can use these tools to be
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more specifically identify patients who are going to flare or not so it
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is in in the sense could be a decision support system
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so this could be the future you know all remote all the just these days are female
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and so a male patient comes the dock doctor while i get the flair for arthritis if i stop
00:14:06
my medication doc and the room with all this is clips all the buttons is computers as no
00:14:13
so in conclusion i think that uh i hope that this little study
00:14:18
uh well i'm convinced you that um these technologies these techniques both the
00:14:24
standards sophisticated statistical analysis and machine learning
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can be used to predict flares at least on the short term into in these patients
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and maybe use in clinical support and we're now going to use these models in a
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prospective clinical trial where will use the support system to guide clinical decision making
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but i would like to and my talk with the most important message which
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i think is it is that the strength of a model really depends
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on your data if you don't have the data if you don't have your data
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you know you can't really use machine learning or whatever technique
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or even our to artificial intelligence to predict the future
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and this will be i think the most important practical thing uh because
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it requires a doctors to be trained and to be disciplined
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and and uh this data in a coherent way and this is at least not in my department how doctors operate
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and this this is how the nurses operates they use the follow protocols but doctors like
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that the the freedom and they think that their intuition is better than the machine
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so um we probably need to wait for the next generation of doctors
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who is willing to accept this a technique thank you have
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yep you said on thank you how much that's was wonderful and look into the future we have time for one question
00:15:53
yeah i do you know so you are very nice oh thank you so you
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you you were saying uh you were saying that a good data is needed
00:16:05
so very simple question what he's good data
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so okay as we can go who is do we need in and and would
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most likely as the same question that's on the talk on the big data
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what big that that means how big is big how big is diffusion so
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all i know that uh with machine learning and we can test
00:16:26
like twenty different machine learning test and so on and we have a
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better uh information letterman to talk with the your sister um
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but but at one point we can only feet over learn from small that i said
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and that was something we have some disappointment between what the machine will tell us
00:16:44
and the dining data set versus a test or reality that's a big yeah
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so what good data means okay so good taken means first of all data
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that means no missing data so when i talk about missing data in this group we know that's for
00:17:01
example smoking is a protector of non responsiveness to
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biological so or it's more the disease severity
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we don't have data on smoking and this is what what we found
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out that we are very bad in collecting data on smoking
00:17:15
this is a bit we we're missing a key protector so that's the first thing that we
00:17:20
need the second thing i think is um well regular data collection is important because
00:17:26
both the joint modelling and the machine learning not only use data from the present but also data from the past
00:17:32
so it makes sense to regularly collects data or you could use
00:17:37
maybe wearable seen the futures to to have reliable faithful objective
00:17:42
uh data collection because our data collection on bass for example still subjective based on the uh
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up to be a doctor or nurse who collects the data so this is i think
00:17:52
a process that will require a continued interaction between
00:17:57
d. machine learn a soda machine learning uh expert's uh data scientists and doctors and then you
00:18:03
need to come up with the model that works best at least in your clinic
00:18:07
but in a may need to be a tested and retested in each clinic separately i thank you
00:18:15
thank you for uh for this great uh presentation knees in
00:18:18
question and scenes despite the fact that things many years
00:18:23
you are in many are sent to fix association from march or
00:18:27
call set of data to connect i think that that is
00:18:30
still amazing to see that forty percent of the dance and you just a meeting which is supposed to be the
00:18:35
called standard measure of air re how do you think we can change the game i'm i'm not a
00:18:41
doctor how do you think it's time to promote some kind of near schemata reduced that would be
00:18:47
in their due to you to connect this kind of data you think it's
00:18:50
a social or any question meaning does the healthcare agency should be
00:18:55
get these data are things that i would get to be born more important indeed
00:19:00
we see what i think is the solution because i added to this province
00:19:05
since ninety three t. news anyway we see in every country that army seemed
00:19:11
at the physicians side i yeah i think it's both a behavioural problem
00:19:16
a behavioural uh uh behaviour of doctors and nurses um so um you know i've i'm i've had
00:19:23
i've been head of my department for five over five years and it's very difficult to
00:19:28
change the culture in in in in in the doctor population and
00:19:32
even with this data they remain very spectacle you know we're we're not going to work with this kind of
00:19:38
uh buttons you know what did they think you know i'm i'm living in a different uh planted
00:19:43
but this is going to be the near future i'm sure and i hope
00:19:46
that insurers will pick this up because they have a stake in this
00:19:50
it's in the interests if the costs are cuts and also in the interest
00:19:54
of hospital boards you know if the cost of trucks can be cut
00:19:58
so i think we need to kind of bypass the culture of existing medical culture and make sure it's imposed
00:20:05
by insurers or hospital boards like in the not the country where you if you want to prescribe genetic
00:20:11
you need to report a lot of data are not the country yes ah and if
00:20:15
you want to keep the patient under be allergic you need to regularly report
00:20:19
a certain number of data yes but i know this is the case
00:20:22
in building for example they have a national registry brutality registry
00:20:26
and we met always just will not get reimbursed if they don't at data on bass and another
00:20:32
activity scores in the national database it's very annoying for them
00:20:36
because it cost them fifty more minutes if they're
00:20:39
not i. t. specialist it takes them half an hour more right and they're paid by the hour
00:20:44
so it's a it's a clash between different interests different stakeholders
00:20:49
okay thank you yeah i think what a a matter of time we'll have a coffee break and little physical
00:20:55
am ex uh exercises required so it's in the second floor you can take the elevator all want

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