I guess the most "suitable" approach is to look up research papers on ML/AI/Stats based methods on bipolar disorder mood swings prediction/regression etc. Focus on the abstract, intro/related works and conclusion. Find out why the method is proposed, what the well-known approaches are, what the intuition for the proposed methods are. Find out the fundamental resources cited on the intro/related works. From the intro and related works, look up the references and skim them.
As for the theoretical basis, the math and the proposed method, just skim them quick, next time you got time/the feels you can deepen them. Utilize sci-hub or lib-gen or similar webs if you/your institution is not subscribed to the publishers. Bonus points: some papers also include github/links to their implementation source code.
Quick search on google scholar with the query "bipolar mood swing prediction machine learning" resulted in cool (at least the titile) research papers. For example The impact of machine learning techniques in the study of bipolar disorder: a systematic review, and Review on Machine Learning Techniques to predict Bipolar Disorder.
Why do we go with this approach? Because your domain is specific, vast and complex it its own way. Most of the time, they already tried the "basics" prediction/regression/classification on your domain and published the methods as well as the results, so you can start from there and gain even more because of the additional knowledges/references from the papers.