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Feature engineering with clinical expert knowledge: A case study assessment of machine learning model complexity and performance

PLoS ONE(2020)10.1371/journal.pone.0231300Source: DataRank Database

Feature engineering with clinical expert knowledge: A case study assessment of machine learning model complexity and performance is a research paper published in PLoS ONE (2020). On theSindex it has a DataRank of 1.7. It has been cited 48 times, with 47 citing works in its 1-hop citation network.

N/A

1.7DataRank · unranked

Scored on demand from live citation data

Open Access48 citations · 47 citing works in its network

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (20 grants)Impact (FWCI 3.34)OA: goldTopics (38)IDs (PubMed, PMC, OpenAlex)SDGs (2)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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DataRank Breakdown

Base Score 35%Citation Network 65%

Base Score Contribution

0.584

From this paper's citation signal

Citation Network Contribution

1.1

From 36 citing papers with measurable signal

Learn more about DataRank methodology →

Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 47 citers.

Knowledge-driven feature engineering to detect multiple symptoms using ambulatory blood pressure monitoring data
Computer Methods and Programs in Biomedicine202215 citations29 referencesContributes 0.096
Enhancing GNSS PPP Algorithms with AI: Towards Mitigating Multipath Effects
20251 citations8 referencesContributes 0.087
Assessing and improving the transferability of current global spatial prediction models
Global Ecology and Biogeography202385 citations55 referencesContributes 0.081
SemDaServ: A Systematic Approach for Semantic Data Specification of AI-Based Smart Service Systems
Applied Sciences20217 citations29 referencesContributes 0.072
Rich learning representations for human activity recognition: How to empower deep feature learning for biological time series
Journal of Biomedical Informatics202211 citations36 referencesContributes 0.069

Authors (8)

Vibhu Jawa ORCID,Xiaohan Zhang ORCID,Christopher G. Chute ORCID,Jeremy A. Epstein ORCID,Jordan Matelsky ORCID

Live enrichment

PubMed:32324754 PMC:PMC7179831 OpenAlex:https://openalex.org/W3018911758

National Center for Advancing Translational Sciences

Grant: OT3TR00201901

NCATS NIH HHS

Grant: OT2 TR002517

NCATS NIH HHS

Grant: OT2 TR002520

NCATS NIH HHS

Grant: OT3 TR002025

NCATS NIH HHS

Grant: OT3 TR002027

NCATS NIH HHS

Grant: OT3 TR002020

NCATS NIH HHS

Grant: OT2 TR002584

NCATS NIH HHS

Grant: OT2 TR002514

NCATS NIH HHS

Grant: OT2 TR002515

NCATS NIH HHS

Grant: OT3 TR002026

National Institutes of Health

Grant: 1OT2TR002520-01

Biomedical Data Translator Technical Feasibility Assessment of Reasoning Tool: Oregon State University

National Institutes of Health

Grant: 3OT2TR002514-01S1

Biomedical Data Translator Technical Feasibility Assessment of Reasoning Tool: University of North Carolina at Chapel Hill

National Institutes of Health

Grant: 3OT3TR002020-01S2

Biomedical Data Translator Technical Feasibility Assessment and Architecture Design

National Institutes of Health

Grant: 3OT2TR002584-01S1

Biomedical Data Translator Technical Feasibility Assessment of Reasoning Tool: Broad Institute

National Institutes of Health

Grant: 1OT2TR002517-01

Biomedical Data Translator Technical Feasibility Assessment of Reasoning Tool: University of Alabama at Birmingham

National Institutes of Health

Grant: 3OT3TR002025-01S1

Biomedical Data Translator Technical Feasibility Assessment and Architecture Design

National Institutes of Health

Grant: 3OT3TR002019-01S4

Biomedical Data Translator Technical Feasibility Assessment and Architecture Design

National Institutes of Health

Grant: 3OT3TR002026-01S2

Biomedical Data Translator Technical Feasibility Assessment and Architecture Design

National Institutes of Health

Grant: 1OT2TR002515-01

Biomedical Data Translator Technical Feasibility Assessment of Reasoning Tool: Stanford University

National Institutes of Health

Grant: 3OT3TR002027-01S1

Biomedical Data Translator Technical Feasibility Assessment and Architecture Design

FWCI

3.34

Citation Percentile

0.9%

Citation Trend

2021

2022

2023

2024

2025

2026

goldLicense: cc-by

journalhttps://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0231300&type=printable

publisherhttps://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0231300&type=printable

publisherhttps://dx.plos.org/10.1371/journal.pone.0231300

journalhttps://doi.org/10.1371/journal.pone.0231300

repositoryhttps://pubmed.ncbi.nlm.nih.gov/32324754

Fields of Study

Machine Learning in HealthcareTime Series Analysis and ForecastingArtificial Intelligence in Healthcare0301 basic medicine02 engineering and technology03 medical and health sciences0202 electrical engineering, electronic engineering, information engineeringAsthmaHealth Knowledge, Attitudes, PracticeHumansMachine LearningPrognosis

MeSH Terms

Machine LearningAsthmaHumansHealth Knowledge, Attitudes, PracticePrognosis

Keywords

Discriminative modelFeature engineeringMachine learningArtificial intelligenceBaseline (sea)Computer scienceFeature (linguistics)F1 scoreTask (project management)Predictive modellingDeep learningEngineeringHealth Knowledge, Attitudes, PracticeScienceQRPrognosisAsthmaMedicineHumansResearch Article

Sustainable Development Goals

SDG 3: 3. Good healthSDG 0: Reduced inequalities