How machine learning can be used to automate scientific literature collection?

Machine learning is being used to automate many laborious tasks in the scientific community. Systematic reviews are periodically conducted within the community to summarize and analyze existing literature. Systematic reviews can be used to create curated document databases. In this blog post, we will discuss systematic reviews in more detail and also talk about the methods that can be used to automate scientific literature collection.

Systematic Reviews

Systematic reviews are a type of research review that involves summarizing existing scientific literature on a specific research or topic. They are essential to evidence-based practice in various fields. Some people in the medical research field, conduct systematic literature to create medical curated document databases. Some of the key characteristics of conducting a systematic literature review is the following:

1. Clearly Defined Research Questions: In the case of any systematic review, there needs to be a clear definition of research questions. A clear definition of research questions would ensure that the next stage of a systematic review is narrowed down to a specific field of interest.

2. Inclusion and Exclusion Criteria: Researchers and Scientists establish clear criteria for including or excluding research papers in a conducted systematic review. Inclusion criteria is based on the public date, author name and title of the research studies in the systematic review.

3. Search Strategy: Systematic review conducts a thorough investigation and searching of various public domain-specific databases. A search strategy needs to be well-documented and clearly defined.

4. Study Selection: Once the search strategy has been defined, the next step is to screen the relevant studies according to pre-defined exclusion and inclusion criteria. This involves two or more reviewers carefully selecting studies according to the

5. Data Extraction: Researchers extract relevant data from the selected studies. Data extracted from selected studies include study design, sample size, interventions or exposures, outcomes, and statistical results.

6. Quality Assessment: This stage involves assessing the quality of the data extracted. Quality Assessment is usually performed by domain experts like doctors in the medical domain.

7. Data Synthesis: This step involves summarizing and synthesizing the findings from the included studies. Depending on the nature of the research, data synthesis may include a meta-analysis (statistical pooling of results) or a narrative synthesis (qualitative summary).

8. Public Bias Assessment: This step involves assessing the bias introduced by a domain expert in a systematic review. Such public bias assessment involves analyzing studies with positive results are more likely to be published.

9. Interpretation and conclusion: This step involves interpreting the results obtained from the systematic reviews. These conclusions should be objective and based on the strength of the available evidence.

10. Reporting: The results of the systematic review are typically reported following established guidelines, such as PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The report should provide transparency about the methods used and the findings.

Machine Learning application to systematic reviews

Machine learning can be applied to systematic reviews for literature collection and various other tasks as well. Some of how machine learning methods have been explored to streamline and enhance the systematic review process include:

1. Automated Literature research: Machine learning algorithms can be used to automate literature search. This includes using methods like support vector regression and neural networks. Such methods can help identify relevant studies or relevant articles in a systematic literature review.

2. Title and Abstract Screening: Supervised machine learning methods can be trained on titles and abstracts from scientific literature collection. Such training requires a huge amount of labelled data. Such labeled data is obtained from manual systematic reviews. Text classification algorithms can be applied on labeled data to automate title and abstract screening

3. Risk of bias assessment: Automating risk assessment in systematic literature reviews is essential to the overall process itself. Machine learning methods can be utilized to automate such risk assessment and increase time and save resources as well.

4. Data Extraction: We can employ machine learning methods to automate data extraction for systematic literature reviews. Generative models like ChatGPT can be used to automate data extraction for systematic literature reviews. This can help in increasing the efficiency of the overall systematic review process

5. Meta-Analysis: Automating the stage of meta-analysis in a systematic review process can bring numerous benefits. Meta-analysis involves statistically synthesizing data from multiple studies to provide a more robust estimate of the treatment effect or outcome of interest.

6. Public Bias Detection: Advanced machine learning techniques can be used to automate the stage of public bias detection in systematic literature reviews. Public bias detection involves selective reporting and this can be automated using machine learning but there should be some human involvement in this stage as well.

7. Quality Assessment: Automating quality assessment in systematic reviews using machine learning involves developing algorithms and models that can evaluate the quality and risk of bias of individual studies included in the review.

8. Topic Modelling: Automating topic modeling in a systematic review can streamline the process of identifying and summarizing key themes and topics within a large corpus of text, making it easier for reviewers to organize and synthesize the information.