Keyword search (4,163 papers available)

"Classification" Keyword-tagged Publications:

Title Authors PubMed ID
1 Attention-Fusion-Based Two-Stream Vision Transformer for Heart Sound Classification Ranipa K; Zhu WP; Swamy MNS; 41155032
ENCS
2 Lung Nodule Malignancy Classification Integrating Deep and Radiomic Features in a Three-Way Attention-Based Fusion Module Khademi S; Heidarian S; Afshar P; Mohammadi A; Sidiqi A; Nguyen ET; Ganeshan B; Oikonomou A; 41150036
ENCS
3 An Effective and Fast Model for Characterization of Cardiac Arrhythmia and Congestive Heart Failure Lahmiri S; Bekiros S; 40218199
JMSB
4 CACTUS: An open dataset and framework for automated Cardiac Assessment and Classification of Ultrasound images using deep transfer learning Elmekki H; Alagha A; Sami H; Spilkin A; Zanuttini AM; Zakeri E; Bentahar J; Kadem L; Xie WF; Pibarot P; Mizouni R; Otrok H; Singh S; Mourad A; 40107020
ENCS
5 Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology Hrtonova V; Nejedly P; Travnicek V; Cimbalnik J; Matouskova B; Pail M; Peter-Derex L; Grova C; Gotman J; Halamek J; Jurak P; Brazdil M; Klimes P; Frauscher B; 39608298
SOH
6 CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets Islam M; Zunair H; Mohammed N; 38492455
ENCS
7 Fractals in Neuroimaging Lahmiri S; Boukadoum M; Di Ieva A; 38468046
JMSB
8 Bayesian workflow for the investigation of hierarchical classification models from tau-PET and structural MRI data across the Alzheimer's disease spectrum Belasso CJ; Cai Z; Bezgin G; Pascoal T; Stevenson J; Rahmouni N; Tissot C; Lussier F; Rosa-Neto P; Soucy JP; Rivaz H; Benali H; 37920382
PERFORM
9 Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data Thölke P; Mantilla-Ramos YJ; Abdelhedi H; Maschke C; Dehgan A; Harel Y; Kemtur A; Mekki Berrada L; Sahraoui M; Young T; Bellemare Pépin A; El Khantour C; Landry M; Pascarella A; Hadid V; Combrisson E; O' Byrne J; Jerbi K; 37385392
IMAGING
10 Compatible-domain Transfer Learning for Breast Cancer Classification with Limited Annotated Data Shamshiri MA; Krzyzak A; Kowal M; Korbicz J; 36758326
ENCS
11 Cross-collection latent Beta-Liouville allocation model training with privacy protection and applications Luo Z; Amayri M; Fan W; Bouguila N; 36685642
ENCS
12 Quantifying imbalanced classification methods for leukemia detection Depto DS; Rizvee MM; Rahman A; Zunair H; Rahman MS; Mahdy MRC; 36516574
ENCS
13 Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging Shaikh MS; Jaferzadeh K; Thörnberg B; 35270968
ENCS
14 Voice characteristics from isolated rapid eye movement sleep behavior disorder to early Parkinson's disease Laetitia Jeancolas 35063866
PERFORM
15 Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images Bourouis S; Alharbi A; Bouguila N; 34460578
ENCS
16 Coding Public Health Interventions for Health Technology Assessments: A Pilot Experience With WHO's International Classification of Health Interventions (ICHI) Wübbeler M; Geis S; Stojanovic J; Elliott L; Gutierrez-Ibarluzea I; Lenoir-Wijnkoop I; 34222165
HKAP
17 COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans Heidarian S; Afshar P; Enshaei N; Naderkhani F; Rafiee MJ; Babaki Fard F; Samimi K; Atashzar SF; Oikonomou A; Plataniotis KN; Mohammadi A; 34113843
ENCS
18 A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects. Khannouz M; Glatard T; 33202905
ENCS
19 Probability of Major Depression Classification Based on the SCID, CIDI, and MINI Diagnostic Interviews: A Synthesis of Three Individual Participant Data Meta-Analyses Wu Y; Levis B; Ioannidis JPA; Benedetti A; Thombs BD; 32814337
LIBRARY
20 Diversity, evolution, and classification of virophages uncovered through global metagenomics. Paez-Espino D, Zhou J, Roux S, Nayfach S, Pavlopoulos GA, Schulz F, McMahon KD, Walsh D, Woyke T, Ivanova NN, Eloe-Fadrosh EA, Tringe SG, Kyrpides NC 31823797
BIOLOGY
21 A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors. Dehghani A, Sarbishei O, Glatard T, Shihab E 31752158
ENCS
22 Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis. Lee K, Khoo HM, Fourcade C, Gotman J, Grova C 30695721
PERFORM

 

Title:Coding Public Health Interventions for Health Technology Assessments: A Pilot Experience With WHO's International Classification of Health Interventions (ICHI)
Authors:Wübbeler MGeis SStojanovic JElliott LGutierrez-Ibarluzea ILenoir-Wijnkoop I
Link:https://pubmed.ncbi.nlm.nih.gov/34222165/
DOI:10.3389/fpubh.2021.620637
Publication:Frontiers in public health
Keywords:Health Technology AssessmentInternational Classification of Health Interventionscodingfeasibility evaluationpublic health intervention
PMID:34222165 Category: Date Added:2021-07-05
Dept Affiliation: HKAP
1 Department of Nursing Science, University of Applied Sciences, Bochum, Germany.
2 Department of Health, Kinesiology, and Applied Physiology, Faculty of Arts and Science, Concordia University, Montreal, QC, Canada.
3 Montreal Behavioural Medicine Centre, CIUSSS du Nord-de-l'Ile-de-Montréal, Montréal, QC, Canada.
4 Centre for Guidelines, National Institute for Health and Care Excellence, Manchester, United Kingdom.
5 Basque Foundation for Health Innovation and Research (BIOEF), Barakaldo, Spain.
6 Basque Office for HTA (Osteba), Barakaldo, Spain.
7 Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands.

Description:

Introduction: An important requirement for successful public health interventions is a standardized classification in order to make these health technologies comparable in all contexts and recognized by all parties. The WHO International Classification of Health Interventions (ICHI), including an integrated public health component, has been developed to propose such an international standard. Methods: To test (a) the translation of public health interventions to ICHI codes and (b) the technical handling and general coding in public health, we used a set of public health interventions from a recent cross-sectional survey among Health Technology Assessment professionals. Results: Our study showed that handling of the ICHI interface is stable, that there is a need for specificity and adequate detail of intervention descriptions and desired outcomes to code adequately with ICHI and that the professional background of the coder, as well as his/her sex might influence the selection of codes. Conclusion: International Classification of Health Interventions provides a good coverage of public health interventions. However, the broader character of system wide interventions, often involving a variety of institutions and stakeholders, may present a challenge to the application of ICHI coding. Based on this experience, we would tailor future surveys more specifically to the needs of the classification and we advise training for health professionals before coding with ICHI. Standards of reporting will likely strengthen insights about the efficiency of primary prevention interventions and thus benefit long-term health of populations and structured HTA reporting process.





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