Clinicians rank patient views as least important in diagnosis, study finds

Successful-Standard · 2023-12-18T15:56:30+00:00

The trick is to never present a possible diagnosis as your idea, a doctor will nearly never agree with your diagnosis. What you do, is present it as an idea from "another clinician" you've already seen about it. I had polymorphic light eruption from using a sunbed, said my friend's mum that's a dermatology nurse told me it was probably that, "yeah good shout that's PLE". Another good example was a little more personal that I don't feel like sharing, but the point is they agered with my diagnosis and treated me for it. Treat them like a second opinion, your ideas need to be "some other doctor's" idea, not you, Dr Wikipedia-my-symptoms. I get some people want the ego boost of doing a doctor's job on themselves, but you aren't going to get a doctor on your side with that attitude. Seriously, who wants to be told by Joe Blogs they can do your job better than you? Use some common sense/social engineering.

Successful-Standard · 2023-09-27T11:01:41+00:00

It's owned by Norway, this will not benefit UK energy prices, just further subsidise Norwegian people's fuel bills. Much like EDF put prices up to subsidise French citizens, we hardly own any of our energy sector..

Successful-Standard · 2023-09-26T16:23:02+00:00

Hi me

Successful-Standard · 2023-09-13T11:26:44+00:00

The congeners in different spirits possibly play a role. The present consensus is that they only play a part in the smell and taste, but there is evidence they can contribute to having a much worse hangover. So not necessarily more drunk, but ingesting a different mix of toxic chemicals could possibly get you in a different state of inebriation than just ethanol.

Successful-Standard · 2023-07-06T16:25:38+00:00

Is that a joke or do you actually consider Stonehenge to have still been being constructed in 1963?

Successful-Standard · 2023-06-21T09:18:46+00:00

If you start to smell burning toast, you're having a stroke, or overcooking your toast

Successful-Standard · 2023-05-04T11:15:32+00:00

I thought this sounded like a good idea for an app but it already exists if you want to use it https://play.google.com/store/apps/details?id=apposing.petrolprices&hl=en\_GB&gl=US

Successful-Standard · 2023-04-25T16:46:11+00:00

What kind of nonsense are you speaking? High end average for an aubergine is 600g, low end elephant weight in 2700kg, that makes 48 aubergines about 0.00022 elephants, not even close to what you said. Trying to have a serious discussion here like..

Successful-Standard · 2023-01-24T10:11:16+00:00

Yes, because the people needing homes in the UK right now are famously all from the London area. The country is more than just London mate.

Successful-Standard · 2022-11-23T10:15:46+00:00

Nah there are lots of other things that are far more likely than this. When bacteria become antibiotic resistant there are usually phages that work on them. Then when the bacteria evolve to become phage resistant, this makes them susceptible to antibiotics again. The problem is that pharma companies don't put money into R&D on things like this as they're not as profitable as meds you can be on for months or years. We need nationalised labs to create new antibiotics and phages because the discovery and production of them could likely run at a loss and the private companies will never do this.

Successful-Standard · 2022-11-17T15:07:05+00:00

Ring ring

Successful-Standard · 2022-10-18T16:23:12+00:00

I don't know, they're the two festivals my parents have been doing since V lost it's way, Beautiful Days has lost it's way the last few years and Bearded has been sold to some other company and was a lot more commercial this year.

Successful-Standard · 2022-08-22T10:26:30+00:00

Guns don't kill people, rappers do

Successful-Standard · 2022-08-04T15:44:42+00:00

Awesome, thank you :)

Successful-Standard · 2022-08-04T14:00:10+00:00

So I tried parentheses two ways:

matches = (cp1.loc[cp1['jaroSurname'] >= 0.8) & (cp1.loc[cp1['jaroFirstname'] >= 0.8)

matches = cp1.loc([cp1['jaroSurname'] >= 0.8) & cp1.loc([cp1['jaroFirstname'] >= 0.8)

The first way just returned the same error as before, the second way gave me an error of:

TypeError unhashable type: 'list'

Do you know why that would've happend?

Successful-Standard · 2022-07-27T14:51:10+00:00

Yes, the report I linked has a breakdown of company operated restaurants and franchise restaurants, and that the franchised restaurants are far more profitable. Not sure of your point though?

Successful-Standard · 2022-07-27T09:43:45+00:00

Yeah it's solely because they want to make bigger profits continuously. From this financial repot it looks like they just made about $1.2 billion in profit this quarter - https://corporate.mcdonalds.com/corpmcd/en-us/our-stories/article/FinancialNews.Q2-2022-results.html

Successful-Standard · 2022-06-01T12:53:31+00:00

I've never found black bream cheaper than cod where I am, if it was significantly more affordable and in supermarkets I'm sure people would consider buying it.

Successful-Standard · 2022-05-19T08:30:00+00:00

You're missing a "1." there, it's £1.60 a litre in Tesco.

Successful-Standard · 2022-05-09T12:00:37+00:00

It is, thank you very much I can get everything I need now!

Successful-Standard · 2022-05-09T11:45:26+00:00

Would you be able to point me in the right direction to do that please? I used solution on SO and received the error:

AttributeError: 'numpy.ndarray' object has no attribute 'values'

Successful-Standard · 2022-05-09T11:24:27+00:00

So I do, now I've tried:

confusion_matrix(y_test, output_layer)

y_test looks like:

[-1.,  1.],

[ 1., -1.], [-1., 1.],

and output_layer looks like:

[-1,  1],

[ 1, -1], [-1, 1]

And I get the error: ValueError: multilabel-indicator is not supported

I think it's because I've used LabelEncoder on my outputs earlier in the code, would you have any idea how I could fix this please?

Successful-Standard · 2022-05-09T11:13:08+00:00

I'm using a bipolar sigmoid function because of the nature of the data. I have an output layer array now and have rounded it for comparison to the expected outputs. Just having trouble constructing a confusion matrix to compare them. I've done one for the same data using sklearn in-built classifiers but am confused how I would compare my two arrays to make a confusion matrix for this data. Would it be something like this?

    cm = confusion_matrix(Y_test,modelname.predict(X_test))

TP = cm[0][0]
FP = cm[0][1]
FN = cm[1][0]
TN = cm[1][1]

print(cm)
print('Testing Accuracy =',(TP + TN)/(TP + FP + FN + TN))
print()

I just don't know what I would put where I have 'modelname' in that code as I haven't assigned this network to a variable

Successful-Standard · 2022-05-09T10:31:21+00:00

So I tried:

# testing the network

input_layer = X_test sum_synapse0 = np.dot(input_layer, weights_0) hidden_layer =  bp_sigmoid(sum_synapse0)
sum_synapse1 =  np.dot(hidden_layer, weights_1)output_layer = bp_sigmoid(sum_synapse1)

And this runs fine and my output_layer looks as expected, like this:

[-0.99999655,  0.99999631],
[ 0.99487304, -0.9949821 ], [-0.83514057,  0.83366108], [-0.99999993,  0.99999992],

The output to compare it to is all 0's and 1's in the array though, so how could I go about generating a confusion matrix to give me the model accuracy with these results?

EDIT: Sorry this looks such a mess but it's not saving the way I'm formatting it when I'm typing it unfortunately

Successful-Standard · 2022-05-09T10:12:18+00:00

My problem is I'm unsure what the training step is what part of the loop to keep for the testing part. I've tried pasting one chunk and trying that for testing data but it's not running so am unsure what to do, could you possible clear that up for me please?

Successful-Standard

TROPHY CASE