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1.
Sensors (Basel) ; 23(4)2023 Feb 13.
Artigo em Inglês | MEDLINE | ID: mdl-36850680

RESUMO

Diarization is an important task when work with audiodata is executed, as it provides a solution to the problem related to the need of dividing one analyzed call recording into several speech recordings, each of which belongs to one speaker. Diarization systems segment audio recordings by defining the time boundaries of utterances, and typically use unsupervised methods to group utterances belonging to individual speakers, but do not answer the question "who is speaking?" On the other hand, there are biometric systems that identify individuals on the basis of their voices, but such systems are designed with the prerequisite that only one speaker is present in the analyzed audio recording. However, some applications involve the need to identify multiple speakers that interact freely in an audio recording. This paper proposes two architectures of speaker identification systems based on a combination of diarization and identification methods, which operate on the basis of segment-level or group-level classification. The open-source PyAnnote framework was used to develop the system. The performance of the speaker identification system was verified through the application of the AMI Corpus open-source audio database, which contains 100 h of annotated and transcribed audio and video data. The research method consisted of four experiments to select the best-performing supervised diarization algorithms on the basis of PyAnnote. The first experiment was designed to investigate how the selection of the distance function between vector embedding affects the reliability of identification of a speaker's utterance in a segment-level classification architecture. The second experiment examines the architecture of cluster-centroid (group-level) classification, i.e., the selection of the best clustering and classification methods. The third experiment investigates the impact of different segmentation algorithms on the accuracy of identifying speaker utterances, and the fourth examines embedding window sizes. Experimental results demonstrated that the group-level approach offered better identification results were compared to the segment-level approach, and the latter had the advantage of real-time processing.


Assuntos
Algoritmos , Biometria , Humanos , Reprodutibilidade dos Testes , Análise por Conglomerados , Bases de Dados Factuais
2.
Sensors (Basel) ; 19(10)2019 May 22.
Artigo em Inglês | MEDLINE | ID: mdl-31121807

RESUMO

In this paper, the possibility of using the ECG signal as an unequivocal biometric marker for authentication and identification purposes has been presented. Furthermore, since the ECG signal was acquired from 4 sources using different measurement equipment, electrodes positioning and number of patients as well as the duration of the ECG record acquisition, we have additionally provided an estimation of the extent of information available in the ECG record. To provide a more objective assessment of the credibility of the identification method, some selected machine learning algorithms were used in two combinations: with and without compression. The results that we have obtained confirm that the ECG signal can be acclaimed as a valid biometric marker that is very robust to hardware variations, noise and artifacts presence, that is stable over time and that is scalable across quite a solid (~100) number of users. Our experiments indicate that the most promising algorithms for ECG identification are LDA, KNN and MLP algorithms. Moreover, our results show that PCA compression, used as part of data preprocessing, does not only bring any noticeable benefits but in some cases might even reduce accuracy.


Assuntos
Algoritmos , Eletrocardiografia , Biomarcadores/análise , Análise Discriminante , Humanos , Modelos Logísticos , Análise de Componente Principal , Processamento de Sinais Assistido por Computador
3.
Cancer Inform ; 18: 1176935119835538, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30906191

RESUMO

In diffuse large B-cell lymphoma (DLBCL), predictive modeling may contribute to targeted drug development by enrichment of the study populations enrolled in clinical trials of DLBCL investigational drugs to include patients with lower likelihood of responding to standard of care. In clinical practice, predictive modeling has the potential to optimize therapy choices in DLBCL. The objectives of this study were to create a model for predicting health outcomes in patients with DLBCL treated with standard of care and determine informative predictors of health outcomes for patients with DLBCL. This was a retrospective observational study using data extracted from the IMS Health Database between September 2007 and April 2015. Patients were ⩾18 years of age with a DLBCL diagnosis. The index date was the date of the first DLBCL diagnosis. Patients were followed until outcome occurrence, defined as progression to a later line of therapy after ⩾60 days from the end of a previous therapy or stem cell transplantation. Patients were categorized into three cohorts depending on the post-index observation period: ⩽1 year, ⩽3 years, or ⩽5 years. Lasso logistic regression (LASSO), Naive Bayes, gradient-boosting machine (GBM), random forest (RF), and neural network models were performed for each cohort. The best-performing algorithms were predictive models based on GBM and observation periods ⩽1 and ⩽3 years after index date. Informative predictors included myocardial imaging, DLBCL stage IV, bronchiolar and renal disease, a chemotherapy regimen, and exposure to diphenhydramine and vasoprotectives on or before the first DLBCL diagnosis. These predictive models may be applied to targeted drug development and have the potential to optimize therapy choices in DLBCL. They were generated efficiently using a large number of independent variables readily available in standard insurance claims or electronic health record data systems.

4.
Epilepsia ; 58(8): e101-e106, 2017 08.
Artigo em Inglês | MEDLINE | ID: mdl-28681416

RESUMO

Recent adverse event reports have raised the question of increased angioedema risk associated with exposure to levetiracetam. To help address this question, the Observational Health Data Sciences and Informatics research network conducted a retrospective observational new-user cohort study of seizure patients exposed to levetiracetam (n = 276,665) across 10 databases. With phenytoin users (n = 74,682) as a comparator group, propensity score-matching was conducted and hazard ratios computed for angioedema events by per-protocol and intent-to-treat analyses. Angioedema events were rare in both the levetiracetam and phenytoin groups (54 vs. 71 in per-protocol and 248 vs. 435 in intent-to-treat). No significant increase in angioedema risk with levetiracetam was seen in any individual database (hazard ratios ranging from 0.43 to 1.31). Meta-analysis showed a summary hazard ratio of 0.72 (95% confidence interval [CI] 0.39-1.31) and 0.64 (95% CI 0.52-0.79) for the per-protocol and intent-to-treat analyses, respectively. The results suggest that levetiracetam has the same or lower risk for angioedema than phenytoin, which does not currently carry a labeled warning for angioedema. Further studies are warranted to evaluate angioedema risk across all antiepileptic drugs.


Assuntos
Angioedema/induzido quimicamente , Angioedema/epidemiologia , Epilepsia/tratamento farmacológico , Fenitoína/efeitos adversos , Piracetam/análogos & derivados , Redes Comunitárias/estatística & dados numéricos , Bases de Dados Factuais/estatística & dados numéricos , Feminino , Humanos , Levetiracetam , Masculino , Piracetam/efeitos adversos
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