ArticleBioengineering (Basel, Switzerland)2024
Data Flow-Based Strategies to Improve the Interpretation and Understanding of Machine Learning Models.
Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data flow-based strategies that seek to improve the understanding of A.I.-based results are examined here by carefully curating and monitoring the flow of data into, for example, artificial neural networks and random forest supervised models. While these models possess structures and related fitting procedures that are highly complex, careful restriction of the data being utilized by these models can provide insight into how they interpret data structures and associated variables sets and how they are affected by differing levels of variation in the data. The goal is improving our understanding of A.I.-based supervised modeling-based results and their stability across different data sources. Some guidelines are suggested for such first-stage adjustments and related data issues.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.