Evidence map›Paper›PMID 34862546›Full record

ReviewActa neurochirurgica. Supplement2022

Overview of Algorithms for Natural Language Processing and Time Series Analyses.

James Feghali, Adrian E Jimenez, Andrew T Schilling, Tej D Azad

Abstract readReview
PubMed Publisher
In one paragraph

Review in Acta neurochirurgica. Supplement, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Machine Intelligence in Cerebrovascular and Endovascular Neurosurgery.Advances in experimental medicine and biology · 2024
    Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

James FeghaliDepartment of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Adrian E JimenezDepartment of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Andrew T SchillingDepartment of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Tej D AzadDepartment of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA. tazad1@jhmi.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A host of machine learning algorithms have been used to perform several different tasks in NLP and TSA. Prior to implementing these algorithms, some degree of data preprocessing is required. Deep learning approaches utilizing multilayer perceptrons, recurrent neural networks (RNNs), and convolutional neural networks (CNNs) represent commonly used techniques. In supervised learning applications, all these models map inputs into a predicted output and then model the discrepancy between predicted values and the real output according to a loss function. The parameters of the mapping function are then optimized through the process of gradient descent and backward propagation in order to minimize this loss. This is the main premise behind many supervised learning algorithms. As experience with these algorithms grows, increased applications in the fields of medicine and neuroscience are anticipated.

Indexed as

AlgorithmsNatural Language ProcessingMachine LearningNeural Networks, ComputerTime FactorsDeep learningMachine learningNatural language processingTime series analysis

Identifiers

PMID34862546

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.