Systems and methods for performing and visualizing semi-automated systematic reviews, ontological hierarchies, and network meta-analysis

A system, comprising a non-transitory computer readable medium for managing medical literature, the non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to perform operations comprising:

• identifying a plurality of electronic documents located in one or more medical literature databases, according to a search criteria, each electronic document comprising:
• a work of authorship;
• and
• a plurality of topics of the work;
• removing duplicates identified with the plurality of electronic documents;
• screening the plurality of electronic documents to separate a plurality of inclusion electronic documents from one or more exclusion electronic documents, wherein the screening comprises:
• training a model to identify screening criteria associated with i) an inclusion electronic document, ii) an exclusion electronic document, or iii) both;
• analyzing the plurality of electronic documents, via the model, according to the screening criteria, so as to apply an inclusion probability for each electronic document of the plurality of electronic documents;
• and
• categorizing each electronic document having an inclusion probability i) equal to or above a threshold as an inclusion electronic document, or ii) below the threshold as an exclusion electronic document;
• wherein training the model comprises associating, using a machine learning algorithm, i) metadata, ii) one or more topics, and iii) contextual information, of a subset of electronic documents of the plurality of electronic documents, with a calculation of the inclusion probability, wherein each electronic document of the subset of electronic documents is identified to be an inclusion electronic document or an exclusion electronic document, such that the model is configured to calculate the inclusion probability based on a combination of the metadata, the one or more topics, and the contextual information for a respective electronic document;
• automatically generating a topic tag hierarchy for the plurality of topics of each inclusion electronic document of the plurality of inclusion electronic documents, via a tag generator, wherein the topic tag hierarchy for one or more topics of the plurality of topics comprises i) a corresponding topic tag that identifies a respective topic of the one or more topics, and ii) one or more subtopic tags, each subtopic tag corresponding to a subtopic identified as a part or division of the respective topic;
• automatically extracting data associated with each topic of the plurality of topics for each inclusion electronic document based on i) a corresponding topic tag, ii) a corresponding subtopic tag, or iii) both, via the tag generator, wherein the tag generator is configured to identify the data related to the corresponding topic tag, the corresponding subtopic tag, or both;
• wherein the tag generator is configured to provide i) a citation, ii) a quotation, or iii) both, of the extracted data from the work of authorship;
• and
• automatically presenting the extracted data as a visual, wherein automatically presenting the extracted data as a visual further comprises generating, via a sunburst diagram generator, a sunburst diagram renderable on an interactable graphical user interface (GUI),
• wherein the sunburst diagram depicts the topic tag hierarchy via a graphical representation of a hierarchical relationship between each topic tag and any corresponding one or more subtopic tags;
• and
• wherein each topic tag and the corresponding one or more subtopic tags are interactable, such that i) selecting a respective topic tag depicts a focused view of the topic tag, or ii) selecting a respective subtopic tag depicts a focused view of the respective subtopic tag.