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Comparative Topic Modeling for Determinants of Divergent Report Results Applied to Macular Degeneration Studies | | Lucas Cassiel Jacaruso
; | | Date: |
1 Sep 2023 | | Abstract: | Topic modeling and text mining are subsets of Natural Language Processing
with relevance for conducting meta-analysis (MA) and systematic review (SR).
For evidence synthesis, the above NLP methods are conventionally used for
topic-specific literature searches or extracting values from reports to
automate essential phases of SR and MA. Instead, this work proposes a
comparative topic modeling approach to analyze reports of contradictory results
on the same general research question. Specifically, the objective is to find
topics exhibiting distinct associations with significant results for an outcome
of interest by ranking them according to their proportional occurrence and
consistency of distribution across reports of significant results. The proposed
method was tested on broad-scope studies addressing whether supplemental
nutritional compounds significantly benefit macular degeneration (MD). Eight
compounds were identified as having a particular association with reports of
significant results for benefitting MD. Six of these were further supported in
terms of effectiveness upon conducting a follow-up literature search for
validation (omega-3 fatty acids, copper, zeaxanthin, lutein, zinc, and
nitrates). The two not supported by the follow-up literature search (niacin and
molybdenum) also had the lowest scores under the proposed methods ranking
system, suggesting that the proposed method’s score for a given topic is a
viable proxy for its degree of association with the outcome of interest. These
results underpin the proposed methods potential to add specificity in
understanding effects from broad-scope reports, elucidate topics of interest
for future research, and guide evidence synthesis in a systematic and scalable
way. | | Source: | arXiv, 2309.00312 | | Services: | Forum | Review | PDF | Favorites | |
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