Automatic variables extraction
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Thanks to technical progress and the availability of virtual data, sex estimation methods as part of a biological profile are undergoing an inevitable evolution. Further reductions in subjectivity, but potentially also in measurement errors, can be brought by approaches that automate the extraction of variables. Such automatization also significantly accelerates and facilitates the specialist's work. The aim of this study is (1) to apply a previously proposed algorithm (Kuchař et al. 2021) to automatically extract 10 variables used for the DSP2 sex estimation method, and (2) to test the robustness of the new automatic approach in a current heterogeneous population. For the first aim, we used a sample of 240 3D scans of pelvic bones from the same individuals, which were measured manually for the DSP database. For the second aim a sample of 108 pelvic bones from the New Mexico Decedent Image Database was used. The results showed high agreement between automatic and manual measurements with rTEM below 5% for all dimensions except two. The accuracy of final sex estimates based on all 10 variables was excellent (error rate 0.3%). However, we observed a higher number of undetermined individuals in the Portuguese sample (25% of males) and the New Mexican sample (36.5% of females). In conclusion, the procedure for automatic dimension extraction was successfully applied both to a different type of data and to a heterogeneous population.
- MeSH
- algoritmy * MeSH
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- pánevní kosti * diagnostické zobrazování MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- soudní antropologie * metody MeSH
- určení pohlaví podle kostry * metody MeSH
- zobrazování trojrozměrné * MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Portugalsko MeSH
... Some Basics 23 -- 2.1 Printing Something 23 -- 2.2 Setting Variables 25 -- 2.3 Listing Variables 26 - ... ... - 2.4 Deleting Variables 27 -- 2.5 Creating a Vector 28 -- 2.6 Computing Basic Statistics 30 -- 2.7 Creating ... ... 3.12 Running a Script 62 -- 3.13 Running a Batch Script 63 -- 3.14 Getting and Setting Environment Variables ... ... and Dates 161 -- 7.1 Getting the Length of a String 163 -- 7.2 Concatenating Strings 163 -- 7.3 Extracting ... ... Against All Other Variables 233 -- 10.8 Creating One Scatter Plot for Each Factor Level 233 -- 10.9 ...
1st ed. xviii, 413 s. : il. ; 24 cm
- Klíčová slova
- systém R, R software,
- MeSH
- automatizované zpracování dat MeSH
- software MeSH
- statistika jako téma MeSH
- Publikační typ
- monografie MeSH
- příručky MeSH
The main drive force in apnea current diagnostic is to reduce overwhelming number of sleep disorders candidates by means of very simple-to-use, comfortable and cheap methodology. The proposed framework is based only on automatic analysis of electrocardiogram signal. The feature extraction stage was performed using methods of Heart Rate Variability and Detrended Fluctuation analysis. Feature-spaces formed using these two methods were used as input to a Long Short-Term Memory Artificial Neural Network chosen for its capability to find temporally dependencies in the data. The framework was evaluated on Challenge 2000 Physionet database yielding successful rate 82.1%, sensitivity 85.5% and specificity 80.1%.
- MeSH
- algoritmy MeSH
- diagnóza počítačová metody MeSH
- financování organizované MeSH
- lidé MeSH
- neuronové sítě MeSH
- paměť MeSH
- počítačové zpracování signálu MeSH
- polysomnografie metody MeSH
- reprodukovatelnost výsledků MeSH
- rozpoznávání automatizované MeSH
- senzitivita a specificita MeSH
- srdeční frekvence MeSH
- syndromy spánkové apnoe diagnóza patofyziologie MeSH
- Check Tag
- lidé MeSH
... Designated versus Active Window 5 -- Menus 6 -- Toolbars 6 -- Status Bar 8 -- Dialog Boxes 8 -- Variable ... ... Names and Variable Labels in Dialog Box Lists 9 -- Dialog Box Pushbuttons 10 -- Subdialog Boxes 11 - ... ... 49 -- 4 Data Editor 51 -- Define Variable 52 -- Variable Names 53 -- Define Variable: Measurement 54 ... ... 71 -- Compute Variable: If Cases 73 -- Compute Variable: Type and Label 74 -- Functions 74 -- Missing ... ... Information 601 -- Variable Sets 602 -- Define Variable Sets 603 -- Use Sets 604 -- Reordering Target ...
1st ed. xxix, 701 s.
... 45 -- DATA LIST Command Additional Features -- Define Freefield Variables 50 -- File Information 52 ... ... -- Saving Data Files 53 -- Saving File Options 54 -- Data Editor 55 -- Define Variable 56 -- Variable ... ... Names 57 -- Define Variable Type 57 -- Define Labels 58 -- Define Missing Values 59 -- Define Column ... ... 73 -- Compute Variable: If Cases 75 -- Compute Variable: Type and Label 75 -- Functions 76 -- Missing ... ... Information 541 -- Variable Sets 542 -- Define Variable Sets 543 -- Use Sets 544 -- Reordering Target ...
[1st ed.] xxvi, 628 s.
OBJECTIVE: Age-at-death estimation is usually done manually by experts. As such, manual estimation is subjective and greatly depends on the past experience and proficiency of the expert. This becomes even more critical if experts need to evaluate individuals with unknown population affinity or with affinity that they are not familiar with. The purpose of this study is to design a novel age-at-death estimation method allowing for automatic evaluation on computers, thus eliminating the human factor. METHODS: We used a traditional machine-learning approach with explicit feature extraction. First, we identified and described the features that are relevant for age-at-death estimation. Then, we created a multi-linear regression model combining these features. Finally, we analysed the model performance in terms of Mean Absolute Error (MAE), Mean Bias Error (MBE), Slope of Residuals (SoR) and Root Mean Squared Error (RMSE). RESULTS: The main result of this study is a population-independent method of estimating an individual's age-at-death using the acetabulum of the pelvis. Apart from data acquisition, the whole procedure of pre-processing, feature extraction and age estimation is fully automated and implemented as a computer program. This program is a part of a freely available web-based software tool called CoxAGE3D, which is available at https://coxage3d.fit.cvut.cz/. Based on our dataset, the MAE of the presented method is about 10.7 years. In addition, five population-specific models for Thai, Lithuanian, Portuguese, Greek and Swiss populations are also given. The MAEs for these populations are 9.6, 9.8, 10.8, 10.5 and 9.2 years, respectively. Our age-at-death estimation method is suitable for individuals with unknown population affinity and provides acceptable accuracy. The age estimation error cannot be completely eliminated, because it is a consequence of the variability of the ageing process of different individuals not only across different populations but also within a certain population.
- MeSH
- acetabulum * diagnostické zobrazování MeSH
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- lineární modely MeSH
- mladý dospělý MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- software * MeSH
- soudní antropologie * metody MeSH
- strojové učení * MeSH
- určení kostního věku * metody MeSH
- zobrazování trojrozměrné * MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
... coverage of the target population in the key datasets of Denmark, Finland, Sweden and Iceland 36 -- Automatic ... ... extraction of electronic data is prevalent in 13 countries 37 -- Twelve countries reported consistently ... ... Number of countries reporting sources of variables within national datasets.37 -- Table 2.5. ... ... Variables considered as being among the most sensitive within national health datasets 74 -- Table 3.4 ...
OECD health policy studies, ISSN 2074-3181
197 stran : ilustrace ; 28 cm
- MeSH
- dokumentace MeSH
- dostupnost zdravotnických služeb MeSH
- důvěrnost informací MeSH
- ekonomika a organizace zdravotní péče MeSH
- integrované poskytování zdravotní péče MeSH
- osobní údaje MeSH
- veřejné zdravotnictví - informatika MeSH
- zdravotní politika MeSH
- zdravotnické informační systémy MeSH
- Konspekt
- Veřejné zdraví a hygiena
- NLK Obory
- veřejné zdravotnictví
- ekonomie, ekonomika, ekonomika zdravotnictví
- NLK Publikační typ
- studie
... Acquisition for Defining Guideline-Compliant Pathways Katja Heiden and Britta Böckmann *¦ Tempolenses with Variable ... ... Läcrämioara Stoicu-Tivadar, Bernd Biobel and Elena Bemad -- Interest Propagation for Knowledge Extraction ... ... and Representation 182 -- Francesca Mulas, Elisa Del Fabbro, Blaž Zupan and Riccardo Beilazzi -- Automatic ...
Studies in health technology and informatics, ISSN 0926-9630 volume 186
xiv, 208 stran : ilustrace, tabulky ; 25 cm
- MeSH
- lékařská informatika MeSH
- telemedicína MeSH
- Publikační typ
- kongresy MeSH
- sborníky MeSH
- Konspekt
- Veřejné zdraví a hygiena
- NLK Obory
- lékařská informatika
Extracellular vesicles (EVs) function as important conveyers of information between cells and thus can be exploited as drug delivery systems or disease biomarkers. Transmission electron microscopy (TEM) remains the gold standard method for visualisation of EVs, however the analysis of individual EVs in TEM images is time-consuming if performed manually. Therefore, we present here a software tool for computer-assisted evaluation of EVs in TEM images. TEM ExosomeAnalyzer detects EVs based on their shape and edge contrast criteria and subsequently analyses their size and roundness. The software tool is compatible with common negative staining protocols and isolation methods used in the field of EV research; even with challenging TEM images (EVs both lighter and darker than the background, images containing artefacts or precipitated stain, etc.). If the fully-automatic analysis fails to produce correct results, users can promptly adjust the detected seeds of EVs as well as their boundaries manually. The performance of our tool was evaluated for three different modes with variable levels of human interaction, using two datasets with various heterogeneity. The semi-automatic mode analyses EVs with high success rate in the homogenous dataset (F1 score 0.9094, Jaccard coefficient 0.8218) as well as in the highly heterogeneous dataset containing EVs isolated from cell culture medium and patient samples (F1 score 0.7619, Jaccard coefficient 0.7553). Moreover, the extracted size distribution profiles of EVs isolated from malignant ascites of ovarian cancer patients overlap with those derived by cryo-EM and are comparable to NTA- and TRPS-derived data. In summary, TEM ExosomeAnalyzer is an easy-to-use software tool for evaluation of many types of vesicular microparticles and is available at http://cbia.fi.muni.cz/exosome-analyzer free of charge for non-commercial and research purposes. The web page contains also detailed description how to use the software tool including a video tutorial.
- Publikační typ
- časopisecké články MeSH
... Laboratory to Automatize the Follow Up of AIDS Patients, M Giacomini, C. Ruggiero, I. Martini, J.L. ... ... Baev sky 352 -- An Efficient Algorithm for Automatic Decoding of ECG Signals, F.A. ... ... Palamarchouk ~ 357 -- Frequency Domain Methods for Measurement of Heart Rate Variability, M. ... ... Lungeanu 617 -- Automatization of Physicians’ Phone-In Hours, J. Turunen, P. ... ... McAllister 631 -- Classification of Metabolic Patients Using Dynamic Variables, S. ...
Studies in health technology and informatics, ISSN 0926-9630 volume 68
xvii, 1009 stran : ilustrace, tabulky ; 25 cm
