Task: Implement LangGraph Information Extraction Specific Case

Project Update: @ibevers @hvgazula @puja-trivedi @tekrajchhetri

Ran preliminary experiments using LangGraph for RAG, search, and multi-agent interaction to strengthen domain knowledge. POV: These approaches might be too complex and advanced to automate portions of the PRISMA framework in systematic literature reviews.
Defined a minimal use case. This is from my systematic literature review on AI-driven autism assessment using behavioral data. Our objective is to guess whether our literature review should include some papers based on their title, abstract, and keywords.

INCLUSION CRITERIA:

Relevance to Assessment: Papers must directly address assessment, screening, diagnosis, or detection methods related to autism.
Primary Resources: Only primary research articles will be considered, excluding secondary sources like meta-analyses and systematic reviews.
Target Population: Subjects should include individuals diagnosed with autism spectrum disorder (ASD) or suspected to have ASD.
AI Application: The study must involve applying artificial intelligence (AI) techniques for behavioral analysis.
Recent Publications: Articles must have been published within the last 10 years to ensure currency (2013-2024).
Study design: Papers are included no matter what is the study design(s) and setting(s), and duration of follow-up

EXCLUSION CRITERIA:

Treatment Focus: Papers only focusing on treatment approaches will be excluded to maintain the assessment-oriented scope.
Literature Reviews: Studies that are literature reviews or systematic reviews will not be included to maintain a primary research focus.
Theoretical Papers: Studies with only theoretical content and no empirical data will be excluded.
Non-Human Subjects: Studies involving animal subjects or computational simulations without human data will be excluded to ensure human relevance.
Subjects only suspected to have autism: high-risk subjects (e.g., siblings) will not be included in the literature review, unless they are assessed/diagnosed.
AI application on manually extracted features: Studies feeding ML models with manually extracted features are excluded to focus on entirely automatic processes.
Lack of Peer Review: Papers lacking peer review will be excluded to ensure a certain level of research quality. (e.g., book chapters)
Abstract Papers: Abstract papers will be excluded
Lack of Behavioral Data: Papers that lack behavioral data or analysis will not be included to ensure relevance to the behavioral analysis aspect.
Non-English Papers: Papers not in English will be excluded due to language limitations.
Non-AI-based Assessment: Use of AI-based Agents for interacting with the subjects, but not for assessment

PAPERS: Let's consider the following arxiv papers as a start.: {'arxiv_id': '2406.13470', 'should_include': True} {'arxiv_id': '2201.00927', 'should_include': True} {'arxiv_id': '2401.04088', 'should_include': False}

Created a script for extracting titles, abstracts, and keywords from arXiv (following @satra 's suggestion to start there). While I understand his reasoning, this approach may limit the project's utility. However, I'm proceeding with it (at least as a first step).
Tested specific inclusion/exclusion questions using several open-source models (e.g., smollm, phi-3), but responses weren't consistent or at least contextually relevant. GPT4-o1-mini provided better results. Concerns: how much can we pay for that?

Code is available in the respective branch under the experiments folder (including the preliminary experiments).

sensein / senseable-agents

Task: Implement LangGraph Information Extraction Specific Case #2

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