[disabled]

Honestly, it is a lot of work. I searched for years, whenever I had "free time" in engineering school, because my problems never completely added up and I could never put my finger on it. I very well felt like I was on borrowed time and I knew I was in a lot of trouble, health-wise. It took me about 4 years to figure it out.

You have to keep an open mind, and PUBMED [1] is your friend. Also, if you do not have institutional access to journal articles, Sci-Hub [2] is your friend. Libgen [3] can also be your friend too.

Searching Google Scholar with advanced key terms also helps, but I find it important to keep a paper log of my queries, so I can also follow my thought process.

Ultimately, you have to learn how to be able to "play" with bits and pieces of information that you get from the articles, and make it (relate it) into something meaningful to you. It requires a lot of intuition, and if you find yourself bored, then you are not doing it right. Some people just have this sort of intuition, but anyone honestly can do it. Just focus on never getting bored with the information, and you will eventually learn this skill.

Anyways, the rare disease I have is called autoimmune autonomic ganglionopathy [4]. I wrote a story [5] on how I figured it out awhile back. People seem to enjoy reading it.

I guess the best way to answer your question: I basically searched everything I could about autonomic neuropathy (autoimmune autonomic ganglionopathy is a form of autonomic neuropathy--and I had already been diagnosed with "diabetes-realted autonomic neuropathy"), to see if there was anything that I could possibly relate to my situation. When the situation really got blown out of proportion, I started looking hardcore at really rare stuff.

[1] PUBMED: https://pubmed.ncbi.nlm.nih.gov/

[2] Sci-Hub: https://sci-hub.se/

[3] Libgen: http://libgen.rs/

[4] US Government Information Page on Autoimmune Autonomic Ganglionopathy: https://rarediseases.info.nih.gov/diseases/11917/autoimmune-...

[5] My Story on How I got Diagnosed with Autoimmune Autonomic Ganglionopathy: https://rareandextraordinarycom.wordpress.com/2016/05/14/fir...

[Greek0]

Thank you for your in-depth answer. This really seems like a problem in search of a better solution.

Thanks as well for writing up your story, it was indeed a good read. I with you all the best with your health!

[disabled]

You're very welcome! :-)

I also hope this helps.

If you are looking in terms of software-based solutions for an issue like this, the best resource is likely via Stanford University's SNAP group, which publishes BioSNAP datasets [1], which can be used for scaling.

For example, in the US, a lot of people are on a bunch of prescription drugs. This is called polypharmacy. Using AI, the SNAP group created AI to identify side-effects when on several drugs [2]. There is excellent sample code for this via the link I provided, that can be viewed on GitHub. Generally this is the case for all of the BioSNAP repositories.

There are also AI tools which Stanford has created which can help augment a deep search through the literature for a rare disease. For example, this "Disease-function association network" [3] can give useful outputs to help one direct a search for finding a certain rare disease.

> "This is a disease-function association network that contains information on relationships between diseases and cellular functions. Cellular functions capture biological processes (e.g., pathways made up of the activities of multiple proteins such as cell communication), cellular components (e.g., components where gene products are active such as mitochondria), and molecular functions (e.g., molecular activities of gene products such as drug binding). Nodes represent diseases and functions, and edges indicate associations between them."

The problem with AI is that it is intellectually bankrupt: it will tell you what it thinks, but it will not tell you why. So, it is critical to develop excellent intuition as an individual.

[1] Stanford BioSNAP Repositories: http://snap.stanford.edu/biodata/index.html

[2] Polypharmacy side-effect association network: http://snap.stanford.edu/biodata/datasets/10017/10017-ChChSe...

[3] Disease-function association network: http://snap.stanford.edu/biodata/datasets/10019/10019-DF-Min...