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1.
Stud Health Technol Inform ; 272: 17-20, 2020 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-32604589

RESUMO

The increased prevalence and frequency of infectious diseases are alarming with respect to the disproportionate fatalities across different regions, socio-economic conditions, and demographic groups. Combining pathological data, socio-environmental data, and extracted knowledge from white papers, we proposed a Globally Localized Epidemic Knowledge base (GLEK) that can be utilized for efficient and optimal epidemic surveillance. GLEK merges social, environmental, pathological, and governmental intervention data to provide efficient advice for epidemic control and intervention. Heuristically utilizing multi-locus data sources, GLEK can identify the best tailored intervention.


Assuntos
Doenças Transmissíveis , Epidemias , Humanos , Inteligência , Bases de Conhecimento
2.
Stud Health Technol Inform ; 272: 159-162, 2020 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-32604625

RESUMO

The successful introduction of ICTs into medical practice is a key factor in improving the performance of any health system for both patients and healthcare professionals. In Burkina Faso, many hospital information systems (HIS) have been developed and are already widely used in large health centers with proven efficiency. To improve the quality of patient care, these hospital information systems should exchange information. Interoperability is one of the privileged ways to improve the integration of different systems because nowadays a HIS is no longer just a single monolithic software system, which is run on a single machine. This paper presents a semantic interoperability architecture, which is based on a mediation approach. The mediator implements local domain ontologies for each HIS, a knowledge base, and a referential ontology which is used as a semantic repository and web services.


Assuntos
Sistemas de Informação Hospitalar , Burkina Faso , Humanos , Bases de Conhecimento , Semântica , Software
3.
Stud Health Technol Inform ; 272: 461-464, 2020 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-32604702

RESUMO

The heterogeneous localized concepts of various hospitals reduce interoperability among localized data models of Hospital Information Systems (HIS) and the knowledge bases of clinical decision support systems (CDSS). The leading solution to overcome the interoperability barrier is the reconciliation of standard medical terminologies with localized data models. In this paper, we extend the semantic reconciliation model (SRM) to provide mappings among diverse concepts of localized domain clinical models (DCM) and concepts of standard medical terminologies such as Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT). In the extended SRM, we insert the explicit semantics only into the word vector of the localized DCM concepts instead of the implicit semantics, which enhances the system's accuracy with a lower computational cost. The extended SRM performed well on the datasets of localized DCM and SNOMED CT with a precision of 0.95, a recall of 0.92, and an F-measure of 0.93.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Semântica , Bases de Conhecimento , Systematized Nomenclature of Medicine
4.
Work ; 66(3): 479-489, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32623414

RESUMO

BACKGROUND: The COVID-19 outbreak pandemic is a situation without a tested action plan. Rehabilitation team members have been called for duty with new responsibilities in addition to their conventional roles in the healthcare system. The infectious disease specialists are updating the knowledge base in limited time in clinical settings. The number of articles in PubMed grows at an increasing rate. OBJECTIVE: The purpose of this study is to identify core COVID-19 articles by citation and co-citation network analysis in the PMC subset of PubMed. METHODS: Citation and co-citation network analysis methods were used to identify core articles and knowledge base. RESULTS: COVID-19 terms query retrieved 15,387 articles in PubMed. These articles formed a citation network with 6,778 articles and 25,163 PMC-PubMed citations. The main article cluster in the co-citation network consists of 2,811 articles and 78,844 co-citations. CONCLUSIONS: The number of COVID-19 articles in PubMed is increasing at a very high rate. Citation and co-citation network analysis are advantageous techniques to identify knowledge base in a scientific discipline. These techniques may help rehabilitation specialists to identify core articles efficiently.


Assuntos
Betacoronavirus , Bibliometria , Infecções por Coronavirus/epidemiologia , Bases de Conhecimento , Pneumonia Viral/epidemiologia , Reabilitação/organização & administração , Humanos , Pandemias
5.
BMC Bioinformatics ; 21(1): 312, 2020 Jul 16.
Artigo em Inglês | MEDLINE | ID: mdl-32677883

RESUMO

BACKGROUND: Most biomedical information extraction focuses on binary relations within single sentences. However, extracting n-ary relations that span multiple sentences is in huge demand. At present, in the cross-sentence n-ary relation extraction task, the mainstream method not only relies heavily on syntactic parsing but also ignores prior knowledge. RESULTS: In this paper, we propose a novel cross-sentence n-ary relation extraction method that utilizes the multihead attention and knowledge representation that is learned from the knowledge graph. Our model is built on self-attention, which can directly capture the relations between two words regardless of their syntactic relation. In addition, our method makes use of entity and relation information from the knowledge base to impose assistance while predicting the relation. Experiments on n-ary relation extraction show that combining context and knowledge representations can significantly improve the n-ary relation extraction performance. Meanwhile, we achieve comparable results with state-of-the-art methods. CONCLUSIONS: We explored a novel method for cross-sentence n-ary relation extraction. Unlike previous approaches, our methods operate directly on the sequence and learn how to model the internal structures of sentences. In addition, we introduce the knowledge representations learned from the knowledge graph into the cross-sentence n-ary relation extraction. Experiments based on knowledge representation learning show that entities and relations can be extracted in the knowledge graph, and coding this knowledge can provide consistent benefits.


Assuntos
Algoritmos , Pesquisa Biomédica , Humanos , Bases de Conhecimento , Aprendizado de Máquina , Modelos Teóricos , Reprodutibilidade dos Testes
6.
PLoS One ; 15(6): e0233311, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32525872

RESUMO

Gene Ontology is used extensively in scientific knowledgebases and repositories to organize a wealth of biological information. However, interpreting annotations derived from differential gene lists is often difficult without manually sorting into higher-order categories. To address these issues, we present GOcats, a novel tool that organizes the Gene Ontology (GO) into subgraphs representing user-defined concepts, while ensuring that all appropriate relations are congruent with respect to scoping semantics. We tested GOcats performance using subcellular location categories to mine annotations from GO-utilizing knowledgebases and evaluated their accuracy against immunohistochemistry datasets in the Human Protein Atlas (HPA). In comparison to term categorizations generated from UniProt's controlled vocabulary and from GO slims via OWLTools' Map2Slim, GOcats outperformed these methods in its ability to mimic human-categorized GO term sets. Unlike the other methods, GOcats relies only on an input of basic keywords from the user (e.g. biologist), not a manually compiled or static set of top-level GO terms. Additionally, by identifying and properly defining relations with respect to semantic scope, GOcats can utilize the traditionally problematic relation, has_part, without encountering erroneous term mapping. We applied GOcats in the comparison of HPA-sourced knowledgebase annotations to experimentally-derived annotations provided by HPA directly. During the comparison, GOcats improved correspondence between the annotation sources by adjusting semantic granularity. GOcats enables the creation of custom, GO slim-like filters to map fine-grained gene annotations from gene annotation files to general subcellular compartments without needing to hand-select a set of GO terms for categorization. Moreover, GOcats can customize the level of semantic specificity for annotation categories. Furthermore, GOcats enables a safe and more comprehensive semantic scoping utilization of go-core, allowing for a more complete utilization of information available in GO. Together, these improvements can impact a variety of GO knowledgebase data mining use-cases as well as knowledgebase curation and quality control.


Assuntos
Biologia Computacional/métodos , Mineração de Dados/métodos , Anotação de Sequência Molecular/métodos , Algoritmos , Bases de Dados Genéticas , Ontologia Genética/estatística & dados numéricos , Humanos , Bases de Conhecimento , Software
7.
Medicine (Baltimore) ; 99(22): e20378, 2020 May 29.
Artigo em Inglês | MEDLINE | ID: mdl-32481423

RESUMO

BACKGROUND: Coronaviruses have drawn attention since the beginning of the 21st century. Over the past 17 years, coronaviruses have triggered several outbreaks of epidemic in people, which brought great threats to global public health security. We analyzed the publications on coronavirus with bibliometrics software and qualitatively and quantitatively evaluated the knowledge base and hot topics of coronavirus research from 2003 to 2020. METHODS: We explored the publications on coronavirus in the Web of Science core collection (WOSCC) from 2003 to 2020. Bibliometric analysis, evaluating knowledge base, and research hotspots were performed based on CiteSpace V (Drexel University, Chaomei Chen). RESULTS: There were a total of 8433 publications of coronavirus. The research on coronavirus boomed when a novel coronavirus triggered outbreaks in people. The leading country was the United States, and the leading institution was the University of Hong Kong. The most productive researchers were: Yuen KY, Drosten C, Baric RS. The keywords analysis showed that SARS-CoV, infection, acute respiratory syndrome, antibody, receptor, and spike protein were research hotspots. The research categories analysis showed that virology, microbiology, veterinary sciences, infectious diseases, and biochemistry and molecular biology were hot research categories. CONCLUSIONS: Bibliometric analysis of the literature shows the research on coronavirus boomed when a novel coronavirus triggered outbreaks in people. With the end of the epidemic, the research tended to be cooling. Virus identification, pathogenesis, and coronavirus-mediated diseases attracted much attention. We must continue studying the viruses after an outbreak ended.


Assuntos
Bibliometria , Pesquisa Biomédica/estatística & dados numéricos , Infecções por Coronavirus , Coronavirus , Bases de Conhecimento , Humanos , Vírus da SARS , Síndrome Respiratória Aguda Grave/virologia
8.
Stud Health Technol Inform ; 270: 297-301, 2020 Jun 16.
Artigo em Inglês | MEDLINE | ID: mdl-32570394

RESUMO

Iris is a system for coding multiple causes of death in ICD-10 and for the selection of the underlying cause of death, based on a knowledge base composed by a large number of rules. With the adoption of ICD-11, those rules need translation to ICD-11. A pre-project has been carried out to evaluate feasibility of transition to ICD-11, which included the analysis of the logical meta-rules needed for rule translation and development of a prototype support system for the expert that will translate the coding rules.


Assuntos
Codificação Clínica , Classificação Internacional de Doenças , Mortalidade , Tradução , Causas de Morte , Humanos , Bases de Conhecimento
9.
Stud Health Technol Inform ; 270: 417-421, 2020 Jun 16.
Artigo em Inglês | MEDLINE | ID: mdl-32570418

RESUMO

This paper focusses on the question of knowledge-based modeling of discharge summaries for semantic requesting in the context of hospital reporting process. An ontology was designed and implemented to capture patient data and required expert knowledge. The modeling process was carried out manually using Protégé after an initial reverse engineering of the official format of discharge summaries. An OWL 2 model was built integrating discharge summaries ontology and ICD 10 hierarchy. The framework is operationalized using the OpenLink Virtuoso database system (RDF store), enabling SPARQL queries and basic reasoning features. The evaluation was performed by implementing a use case based on patient comorbidities.


Assuntos
Classificação Internacional de Doenças , Semântica , Bases de Dados Factuais , Bases de Conhecimento , Alta do Paciente
10.
Stud Health Technol Inform ; 270: 607-612, 2020 Jun 16.
Artigo em Inglês | MEDLINE | ID: mdl-32570455

RESUMO

The access to data in healthcare is an enabler for the implementation of clinical decision support systems (CDSS) in practice. The usage of CDSS aims to be of efficient assistance to healthcare providers. The aim of the BMBF project "PosiThera", is to support the involved professions in the treatment process of chronic wounds. In this study we implemented the formalized knowledge of chronic wound diagnosis into two different knowledge base approaches, the HL7 Arden Syntax and a Petri net approach. The motivating factor behind our study was to use both approaches for the implementation of the projects knowledge base and to compare the results. We implemented the formalized knowledge successfully in both approaches. The results of our comparison showed similarities and differences of the Arden Syntax and the Petri net approach, which might support the evolution of both approaches in the future.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Doença Crônica , Humanos , Bases de Conhecimento , Linguagens de Programação
11.
BMC Bioinformatics ; 21(1): 213, 2020 May 24.
Artigo em Inglês | MEDLINE | ID: mdl-32448122

RESUMO

BACKGROUND: Semantic resources such as knowledge bases contains high-quality-structured knowledge and therefore require significant effort from domain experts. Using the resources to reinforce the information retrieval from the unstructured text may further exploit the potentials of such unstructured text resources and their curated knowledge. RESULTS: The paper proposes a novel method that uses a deep neural network model adopting the prior knowledge to improve performance in the automated extraction of biological semantic relations from the scientific literature. The model is based on a recurrent neural network combining the attention mechanism with the semantic resources, i.e., UniProt and BioModels. Our method is evaluated on the BioNLP and BioCreative corpus, a set of manually annotated biological text. The experiments demonstrate that the method outperforms the current state-of-the-art models, and the structured semantic information could improve the result of bio-text-mining. CONCLUSION: The experiment results show that our approach can effectively make use of the external prior knowledge information and improve the performance in the protein-protein interaction extraction task. The method should be able to be generalized for other types of data, although it is validated on biomedical texts.


Assuntos
Algoritmos , Atenção/fisiologia , Bases de Conhecimento , Semântica , Bases de Dados Genéticas , Humanos , Redes Neurais de Computação , Publicações
12.
J Cancer Res Clin Oncol ; 146(7): 1813-1818, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32270287

RESUMO

PURPOSE: To identify key factors for the best practice of knowledge transfer from high-income settings to low- and middle-income settings. RESULTS: Interactive sessions led to the identification of European learnings that can and should be shared beyond Europe. Furthermore, methods were characterised which may lead to successful knowledge transfer with subsequent quality improvement. CONCLUSION: To ensure successful implementation of knowledge and new methods, political support is extremely important. A strong focus should be an improvement of collaboration and network development. Rehabilitation, early and late pallative care, cost effectiveness and long-term follow-up are priorities. Limitations are budget constraints which limit the execution of NCCPs.


Assuntos
Assistência à Saúde , Bases de Conhecimento , Neoplasias/epidemiologia , Melhoria de Qualidade , Efeitos Psicossociais da Doença , Assistência à Saúde/métodos , Assistência à Saúde/normas , Países Desenvolvidos , Países em Desenvolvimento , Saúde Global , Humanos , Neoplasias/diagnóstico , Vigilância da População , Pesquisa
13.
Nat Genet ; 52(4): 448-457, 2020 04.
Artigo em Inglês | MEDLINE | ID: mdl-32246132

RESUMO

Precision oncology relies on accurate discovery and interpretation of genomic variants, enabling individualized diagnosis, prognosis and therapy selection. We found that six prominent somatic cancer variant knowledgebases were highly disparate in content, structure and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. We developed a framework for harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations. We demonstrated large gains in overlap between resources across variants, diseases and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 57% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide a freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases.


Assuntos
Variação Genética/genética , Neoplasias/genética , Bases de Dados Genéticas , Diploide , Genômica/métodos , Humanos , Bases de Conhecimento , Medicina de Precisão/métodos
15.
BMC Bioinformatics ; 21(1): 35, 2020 Jan 30.
Artigo em Inglês | MEDLINE | ID: mdl-32000677

RESUMO

BACKGROUND: Automated biomedical named entity recognition and normalization serves as the basis for many downstream applications in information management. However, this task is challenging due to name variations and entity ambiguity. A biomedical entity may have multiple variants and a variant could denote several different entity identifiers. RESULTS: To remedy the above issues, we present a novel knowledge-enhanced system for protein/gene named entity recognition (PNER) and normalization (PNEN). On one hand, a large amount of entity name knowledge extracted from biomedical knowledge bases is used to recognize more entity variants. On the other hand, structural knowledge of entities is extracted and encoded as identifier (ID) embeddings, which are then used for better entity normalization. Moreover, deep contextualized word representations generated by pre-trained language models are also incorporated into our knowledge-enhanced system for modeling multi-sense information of entities. Experimental results on the BioCreative VI Bio-ID corpus show that our proposed knowledge-enhanced system achieves 0.871 F1-score for PNER and 0.445 F1-score for PNEN, respectively, leading to a new state-of-the-art performance. CONCLUSIONS: We propose a knowledge-enhanced system that combines both entity knowledge and deep contextualized word representations. Comparison results show that entity knowledge is beneficial to the PNER and PNEN task and can be well combined with contextualized information in our system for further improvement.


Assuntos
Proteínas/genética , Animais , Biologia Computacional , Humanos , Bases de Conhecimento , Proteínas/química
16.
BMC Bioinformatics ; 21(1): 6, 2020 Jan 03.
Artigo em Inglês | MEDLINE | ID: mdl-31900127

RESUMO

BACKGROUND: In recent years, biomedical ontologies have become important for describing existing biological knowledge in the form of knowledge graphs. Data mining approaches that work with knowledge graphs have been proposed, but they are based on vector representations that do not capture the full underlying semantics. An alternative is to use machine learning approaches that explore semantic similarity. However, since ontologies can model multiple perspectives, semantic similarity computations for a given learning task need to be fine-tuned to account for this. Obtaining the best combination of semantic similarity aspects for each learning task is not trivial and typically depends on expert knowledge. RESULTS: We have developed a novel approach, evoKGsim, that applies Genetic Programming over a set of semantic similarity features, each based on a semantic aspect of the data, to obtain the best combination for a given supervised learning task. The approach was evaluated on several benchmark datasets for protein-protein interaction prediction using the Gene Ontology as the knowledge graph to support semantic similarity, and it outperformed competing strategies, including manually selected combinations of semantic aspects emulating expert knowledge. evoKGsim was also able to learn species-agnostic models with different combinations of species for training and testing, effectively addressing the limitations of predicting protein-protein interactions for species with fewer known interactions. CONCLUSIONS: evoKGsim can overcome one of the limitations in knowledge graph-based semantic similarity applications: the need to expertly select which aspects should be taken into account for a given application. Applying this methodology to protein-protein interaction prediction proved successful, paving the way to broader applications.


Assuntos
Ontologias Biológicas , Aprendizado de Máquina Supervisionado , Algoritmos , Mineração de Dados , Ontologia Genética , Humanos , Bases de Conhecimento , Semântica
17.
J Comput Assist Tomogr ; 44(1): 13-19, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-31939876

RESUMO

OBJECTIVE: To evaluate image quality and radiation dose exposure of low-kV setting and low-volume contrast medium (CM) computed tomography angiography (CTA) protocol for transcatheter aortic valve implantation (TAVI) planning in comparison with standard CTA protocol. METHODS: Sixty-patients were examined with 256-row MDCT for TAVI planning: 32 patients (study group) were evaluated using 80-kV electrocardiogram-gated protocol with 60 mL of CM and IMR reconstruction; 28 patients underwent a standard electrocardiogram-gated CTA study (100 kV; 80 mL of CM; iDose4 reconstruction). Subjective and objective image quality was evaluated in each patient at different aortic levels. Finally, we collected radiation dose exposure data (CT dose index and dose-length product) of both groups. RESULTS: In study protocol, significant higher mean attenuation values were achieved in all measurements compared with the standard protocol. There were no significant differences in the subjective image quality evaluation in both groups. Mean dose-length product of study group was 56% lower than in the control one (P < 0.0001). CONCLUSION: Low-kV and low-CM volume CTA, combined with IMR, allows to correctly performing TAVI planning with high-quality images and significant radiation dose reduction compared with standard CTA protocol.


Assuntos
Estenose da Valva Aórtica/diagnóstico por imagem , Angiografia por Tomografia Computadorizada/métodos , Meios de Contraste/administração & dosagem , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Adulto , Idoso , Idoso de 80 Anos ou mais , Algoritmos , Estenose da Valva Aórtica/cirurgia , Feminino , Humanos , Bases de Conhecimento , Masculino , Pessoa de Meia-Idade , Tomografia Computadorizada Multidetectores , Período Pré-Operatório , Doses de Radiação , Substituição da Valva Aórtica Transcateter
18.
Int J Radiat Oncol Biol Phys ; 106(5): 1095-1103, 2020 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-31982497

RESUMO

PURPOSE: To develop a tradeoff hyperplane model to facilitate tradeoff decision-making before inverse planning. METHODS AND MATERIALS: We propose a model-based approach to determine the tradeoff hyperplanes that allow physicians to navigate the clinically viable space of plans with best achievable dose-volume parameters before planning. For a given case, a case reference set (CRS) is selected using a novel anatomic similarity metric from a large reference plan pool. Then, a regression model is built on the CRS to estimate the expected dose-volume histograms (DVHs) for the current case. This model also predicts the DVHs for all CRS cases and captures the variation from the corresponding DVHs in the clinical plans. Finally, these DVH variations are analyzed using the principal component analysis to determine the tradeoff hyperplane for the current case. To evaluate the effectiveness of the proposed approach, 244 head and neck cases were randomly partitioned into reference (214) and validation (30) sets. A tradeoff hyperplane was built for each validation case and evenly sampled for 12 tradeoff predictions. Each prediction yielded a tradeoff plan. The root-mean-square errors of the predicted and the realized plan DVHs were computed for prediction achievability evaluation. RESULTS: The tradeoff hyperplane with 3 principal directions accounts for 57.8% ± 3.6% of variations in the validation cases, suggesting the hyperplanes capture a significant portion of the clinical tradeoff space. The average root-mean-square errors in 3 tradeoff directions are 5.23 ± 2.46, 5.20 ± 2.52, and 5.19 ± 2.49, compared with 4.96 ± 2.48 of the knowledge-based planning predictions, indicating that the tradeoff predictions are comparably achievable. CONCLUSIONS: Clinically relevant tradeoffs can be effectively extracted from existing plans and characterized by a tradeoff hyperplane model. The hyperplane allows physicians and planners to explore the best clinically achievable plans with different organ-at-risk sparing goals before inverse planning and is a natural extension of the current knowledge-based planning framework.


Assuntos
Neoplasias de Cabeça e Pescoço/radioterapia , Bases de Conhecimento , Planejamento da Radioterapia Assistida por Computador/métodos , Humanos , Dosagem Radioterapêutica , Radioterapia de Intensidade Modulada
20.
Int J Radiat Oncol Biol Phys ; 106(2): 430-439, 2020 02 01.
Artigo em Inglês | MEDLINE | ID: mdl-31678227

RESUMO

PURPOSE: To evaluate whether automated knowledge-based planning (KBP) (a) is noninferior to human-driven planning across multiple disease sites and (b) systematically affects dosimetric plan quality and variability. METHODS AND MATERIALS: Clinical KBP automated planning routines were developed for prostate, prostatic fossa, hypofractionated lung, and head and neck. Clinical implementation consisted of independent generation of human-generated and KBP plans (145 cases across all sites), followed by blinded plan selection. Reviewing physicians were prompted to select a single plan; when plan equivalence was volunteered, this scored as KBP selection. Plan selection analysis used a noninferiority framework testing the hypothesis that KBP is not worse than human-driven planning (threshold: lower 95% confidence interval [CI] > 0.45 = noninferiority; > 0.5 = superiority). Target and organ-at-risk metrics were compared by dose differencing: ΔDx = Dx, human-Dx, KBP (2-tailed paired t test, Bonferroni-corrected P < .05 significance threshold). To evaluate the aggregated effect of KBP on planning performance, we examined post-KBP dosimetric parameters against 183 plans generated just before KBP implementation (2-tailed unpaired t test, Bonferroni-corrected P < .05). RESULTS: Across all disease sites, the KBP success rate (physician preferred + equivalent) was noninferior compared with human-driven planning (83 of 145 = 57.2%; range, 49.2%-65.3%) but did not cross the threshold for superiority. The KBP success rate in respective disease sites was superior with head and neck ([22 + 2]/36 = 66.7%; 95% CI, 51%-82%) and noninferior for lung stereotactic body radiation therapy ([21 + 2]/36 = 63.9%; 95% CI, 48%-80%) but did not meet noninferiority criteria with prostate ([16 + 3]/41 = 46.3%; 95% CI, 31%-62%) or prostatic fossa ([17 + 0]/32 = 53.1%; 95% CI, 36%-70%). Prostate, prostatic fossa, and head and neck showed significant differences in KBP-selected plans versus human-selected plans, with KBP generally exhibiting greater organ-at-risk sparing and human plans exhibiting better target homogeneity. Analysis of plan quality pre- and post-KBP showed some reductions in organ doses and quality metric variability in prostate and head and neck. CONCLUSIONS: Fully automated KBP was noninferior to human-driven plan optimization across multiple disease sites. Dosimetric analysis of treatment plans before and after KBP implementation showed a systematic shift to higher plan quality and lower variability with the introduction of KBP.


Assuntos
Protocolos Clínicos , Neoplasias de Cabeça e Pescoço/radioterapia , Gestão do Conhecimento , Neoplasias Pulmonares/radioterapia , Neoplasias da Próstata/radioterapia , Planejamento da Radioterapia Assistida por Computador/métodos , Neoplasias de Cabeça e Pescoço/patologia , Humanos , Bases de Conhecimento , Neoplasias Pulmonares/patologia , Masculino , Tratamentos com Preservação do Órgão/métodos , Órgãos em Risco , Neoplasias da Próstata/patologia , Garantia da Qualidade dos Cuidados de Saúde , Radiometria , Dosagem Radioterapêutica , Planejamento da Radioterapia Assistida por Computador/normas , Equipolência Terapêutica
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