Bar charts of false positive misclassifications for tested clinicopathologic findings across all testers.

Bar charts of false positive misclassifications for tested clinicopathologic findings across all testers.

<p>Number of false positive accessions extracted (y-axis) by each anonymised tester (x-axis) stacked by reason for misclassification (key), for each tested clinicopathologic finding (as labelled) during pilot testing of the application, DEE, for extraction of necropsy data from the Wildbase Pa...

Spremljeno u:

Bibliografski detalji
Glavni autor:	Stefan Saverimuttu (20882325) (author)
Daljnji autori:	Kate McInnes (7520158) (author), Kristin Warren (3339657) (author), Lian Yeap (21221659) (author), Stuart Hunter (2498485) (author), Brett Gartrell (6325817) (author), An Pas (12001965) (author), James Chatterton (20347204) (author), Bethany Jackson (3339660) (author)
Izdano:	2025
Teme:	Ecology Information Systems not elsewhere classified wildlife necropsy data wildbase pathology register study evaluates part score &# 8212 pilot study highlights one health objectives limited terminological variance examine ), designed driven misclassification patterns consuming process poses analysed alongside tester 63 &# 8211 terminological variance may efficiently derive insights text clinical data search term selection efficient data retrieval four clinicopathologic findings highest performance scores scores across knowledge retrieval extracting insights efficient utilisation clinicopathologic finding tested findings findings reveal findings characterized xlink "> valuable resources results highlight relatively simple rapid analysis principals underlying mining approaches mining application may describe manual review external oiling diphtheritic stomatitis broader implementation bespoke text based text automatically capture application testers advancing conservation
Oznake:	Dodaj oznaku Bez oznaka, Budi prvi tko označuje ovaj zapis!

Slični predmeti

Bar charts of false negative misclassifications for tested clinicopathologic findings across all testers.
od: Stefan Saverimuttu (20882325)
Izdano: (2025)

Quantification of misclassification categorisation. Misclassification categories quantified across all testers and clinicopathologic findings for false positives and false negatives in a pilot test of the text-mining application; DEE, designed for the rapid retrieval of necropsy data from free-text reports within the Wildbase Pathology Register of Aotearoa New Zealand.
od: Stefan Saverimuttu (20882325)
Izdano: (2025)

Table of misclassification coding structure. Codes in the left-hand column were given to individual records misclassified by the application according to the false positive and false negative code definitions and examples described.
od: Stefan Saverimuttu (20882325)
Izdano: (2025)

All tester data.
od: Stefan Saverimuttu (20882325)
Izdano: (2025)

Violin plots of performance metrics across all testers.
od: Stefan Saverimuttu (20882325)
Izdano: (2025)

Metrics of application performance. Mean, standard deviation, and range of recall, precision, and calculated F1-score of all four tested clinicopathologic findings across all testers of the application for extraction of necropsy data from the Wildbase Pathology Register, DEE.
od: Stefan Saverimuttu (20882325)
Izdano: (2025)

Statistics of search terms and stems. Mean, standard deviation, and range of the number of selected search terms and number of 3 letter word stems derived from these search terms, across testers and clinicopathologic findings in the pilot test of an application for extraction of necropsy data from the Wildbase Pathology Register, DEE.
od: Stefan Saverimuttu (20882325)
Izdano: (2025)

Clinicopathologic finding descriptions given for each dataset, and reference name of each finding in this study.
od: Stefan Saverimuttu (20882325)
Izdano: (2025)

Line plots of performance metrics across TFIDF conditions.
od: Stefan Saverimuttu (20882325)
Izdano: (2025)

Summarised workflow of the ‘Examine’ section of DEE.
od: Stefan Saverimuttu (20882325)
Izdano: (2025)

Violin plot of all application test performance metrics.
od: Stefan Saverimuttu (20882325)
Izdano: (2025)

Image 1_The relationship between social emotions and intuitive eating behaviors: an exploration based on text analysis.pdf
od: Jing Wu (54032)
Izdano: (2025)

اقتضاء النص عند الأصوليين وتطبيقاته في القانون المدني الأردني
od: Al-Saleh, Abdullah
Izdano: (2021)

التعارض بين النصوص: أسبابه ودفعه
od: Almujahed, Muhammad Said
Izdano: (2020)

Multilevel logistic regression models with each negative emotions as a predictor.
od: Chan-Young Ahn (22683343)
Izdano: (2025)

Confusion Matrix for Binary Classification.
od: Chan-Young Ahn (22683343)
Izdano: (2025)

Associations between pre-assessment self-report measures and EMA (14-day) ratings.
od: Chan-Young Ahn (22683343)
Izdano: (2025)

Descriptive statistics of demographic and clinical characteristics.
od: Chan-Young Ahn (22683343)
Izdano: (2025)

Feature importance scores of the predictors.
od: Chan-Young Ahn (22683343)
Izdano: (2025)

Supplementary material JImaging-3990735
od: Adrien Deliege (22675676)
Izdano: (2025)

Maximum clade credibility (MCC) trees of phylodynamic models for transmission in farm A.
od: Marina Meester (11765542)
Izdano: (2025)

Output of phylodynamic (BD) and SEIR model to estimate the effective reproduction number of between-pen transmission (between-pen R<sub>e</sub>) for farm B, compartment 2 during weeks 7 to 16 of batch B.
od: Marina Meester (11765542)
Izdano: (2025)

Infection dynamics and phylogenetics of HEV in two farms.
od: Marina Meester (11765542)
Izdano: (2025)

Schematic overview of a pig farm and the three hypothesized environmental transmission routes (indicated by pink arrows) of hepatitis E virus in farms.
od: Marina Meester (11765542)
Izdano: (2025)

تجديد الاصطلاح الفقهي عند الشيخ مصطفى الزرقا
od: Alajmi, Muhammad Mahdi Muhammad
Izdano: (2022)

Synthetic event log with control-flow deviations and known ground-truth causal factors from a loan application process
od: Deanna Coralage (20557091)
Izdano: (2025)

Data Sheet 1_Enhancing preoperative HER2 status classification of invasive breast cancers using machine learning models based on clinicopathological and MRI features: a multicenter study.docx
od: Suhong Zhao (10057478)
Izdano: (2025)

Result of cognitive feedback questionnaire bar chart.
od: Shuai Huang (60512)
Izdano: (2025)

Medication-inclusive multivariable logistic regression for MAFLD.
od: Zienab M. Saad (22676502)
Izdano: (2025)

Multivariable logistic regression models for MAFLD predictors.
od: Zienab M. Saad (22676502)
Izdano: (2025)

Liver Fibrosis Characteristics by CKD Stage in MAFLD Patients (n = 64).
od: Zienab M. Saad (22676502)
Izdano: (2025)

Liver Stiffness and Fibrosis Grading by MAFLD Status.
od: Zienab M. Saad (22676502)
Izdano: (2025)

Comparison of Non-invasive Liver Fibrosis Scores.
od: Zienab M. Saad (22676502)
Izdano: (2025)

Demographic and Clinical Characteristics of Study Participants.
od: Zienab M. Saad (22676502)
Izdano: (2025)

Proposed pathophysiological crosstalk between MAFLD and CKD.
od: Zienab M. Saad (22676502)
Izdano: (2025)

Receiver operating characteristic (ROC) curve analysis comparing the performance of non-invasive fibrosis scores in MAFLD patients.
od: Zienab M. Saad (22676502)
Izdano: (2025)

Stratification of Liver Stiffness according to eGFR in the MAFLD group.
od: Zienab M. Saad (22676502)
Izdano: (2025)

A. Violin plots showing eGFR distribution in MAFLD (red) vs. non-MAFLD (blue) groups.
od: Zienab M. Saad (22676502)
Izdano: (2025)

Grades of liver fibrosis in MAFLD group (grey) and non-MAFLD group (black).
od: Zienab M. Saad (22676502)
Izdano: (2025)

eGFR in the study participants.
od: Zienab M. Saad (22676502)
Izdano: (2025)

Cannot write session to /tmp/vufind_sessions/sess_u2jngupir8h289poqggg1324tb