Converting weighted value to probability For a disease classification problem I have trained a deep learning model to predict the probability score of the disease based on images only. During training time I had access to labeled images only without any other patient related data. The model gives a probability score (in range 0 to 1) based on an input image. Now, I want to make the prediction more robust by incorporating expert knowledge as a subjective probability. For that reason we have created a set of questionnaires that will be asked to the patient to assist the image based analysis. We have collected opinion from human experts (doctors)
and they already assigned weights to the answers (in range [-1,3], where, -1 means low probability of the disease, 3 means high probability). A sample is shown in following table:

Now, during actual classification I can get a weighted value based on the answer of the user using following equation:

where, Symptom can be either 0 or 1 (present or absent). The result will be in the range [-1,3], but I want to convert it to a probability score in the range [0,1]. Is it sufficient to convert the obtained value to [0,1] range using:
OldRange = (OldMax - OldMin)  
NewRange = (NewMax - NewMin)  
NewValue = (((OldValue - OldMin) * NewRange) / OldRange) + NewMin 

Or is there any other better approach? So, in the end, I want two probabilities: one from image based deep learning model, another from subjective expert knowledge based on questionnaires and make the final prediction based on these two probabilities. Thanks in advance for your feedback.

The numbers you have are meaningless. It is a well-known fact that people when asked to rate something do this subjectively. It was researched by economists studying utility of the money. In the case of money, the same amount of money would mean different things to different people, but also the utility of the money does not raise linearity with the amount of money. A similar phenomenon was observed for probabilities. People differ in what they consider as "high" or "low" probability. Below you can see a plot summarizing one of the studies asking people to name the probabilities, as you can see, the answers vary a lot.

You may argue that in your case you have the -1 to 3 scale, but the same problem remains since you do not have any unambiguous mapping from the numbers to actual probabilities. This can't be done by averaging, or taking a weighted sum of the numbers.
What you can do is to use a statistical model that would map the ratings to the probabilities. A class of such models was proposed by psychometrists and is called Item Response Theory. A typical use case of such models is when you have multiple students taking an exam, where the exam sheets are each rated by multiple raters. The simplest IRT model is the Rasch model, where the $i$-th student's response, rated by $j$-th rater is modeled by considering the student's ability $\theta_i$ and the rater's tendency $\beta_j$ to over- or under-estimate.
$$ P(X_{ij} = 1) = \frac{\exp(\theta_i - \beta_j)}{1-\exp(\theta_i - \beta_j)} $$
Such a model lets you find the "true" ability $\theta_i$. When considering your data you need a model that is able to do something similar.
In practice, what this means for you is that you want your algorithm to learn the weights when taking a weighted sum of the ratings of the symptoms. You don't only want to assign different weights for each symptom, but also, similar to in the Rasch model, you want the algorithm to be able to correct for the tendencies of the individual experts to over- or under-estimate the probabilities. The simplest case is considering the relationships between those parameters as a linear function, but one can easily imagine how the mappings between the ratings could have different non-linear forms for different experts (e.g. a person who marks everything in black-and-white categories where everything is either low probability or high probability).
You can do this by building a model that predicts the presence or absence of the disease based on all the ratings of the experts of the symptoms.
The question remains, how do you want to "incorporate" those probabilities into the model? You could either (1) have a separate model that translates the ratings to probabilities and uses those probabilities as a feature, or (2) have the raw answers by the experts as inputs to the model along with the images so that the model does both the weighting and classification, or (3) make your model predict the probabilities instead of labels, (4) or use the probabilities as weights (as in survey weights) for the labels. Notice that in (1)-(2) to make predictions from the model you would need not only the images but also the ratings, so the model stops being an image classifier. In (3)-(4) you are making an assumption that the ratings by the experts are more valuable than the labels themselves, or at least that they bring additional value to the labels. For example, someone judged by the expert as having a higher probability is considered as "sicker" as compared to a person with a positive label but judged as having a small probability--in such a case, your model can accidentally learn to copy the biases that the experts have and learn to misclassify "strange" cases.
I'm not giving you here the final answer on how to do this. It is your decision and it needs an additional, in-depth understanding of your data and the problem you are trying to solve. As said above, there is no single way how this could be solved as well. It is worth noticing that you have three non-trivial problems to solve here: how to use the information from the raw ratings by experts (psychometrics), how to classify the images (image classification), and how (or if) the two can be combined (problem definition, model architecture).
