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1.
J Biomed Inform ; 65: 1-21, 2017 01.
Artigo em Inglês | MEDLINE | ID: mdl-27856379

RESUMO

Decision support systems are used as a method of promoting consistent guideline-based diagnosis supporting clinical reasoning at point of care. However, despite the availability of numerous commercial products, the wider acceptance of these systems has been hampered by concerns about diagnostic performance and a perceived lack of transparency in the process of generating clinical recommendations. This resonates with the Learning Health System paradigm that promotes data-driven medicine relying on routine data capture and transformation, which also stresses the need for trust in an evidence-based system. Data provenance is a way of automatically capturing the trace of a research task and its resulting data, thereby facilitating trust and the principles of reproducible research. While computational domains have started to embrace this technology through provenance-enabled execution middlewares, traditionally non-computational disciplines, such as medical research, that do not rely on a single software platform, are still struggling with its adoption. In order to address these issues, we introduce provenance templates - abstract provenance fragments representing meaningful domain actions. Templates can be used to generate a model-driven service interface for domain software tools to routinely capture the provenance of their data and tasks. This paper specifies the requirements for a Decision Support tool based on the Learning Health System, introduces the theoretical model for provenance templates and demonstrates the resulting architecture. Our methods were tested and validated on the provenance infrastructure for a Diagnostic Decision Support System that was developed as part of the EU FP7 TRANSFoRm project.


Assuntos
Pesquisa Biomédica/tendências , Coleta de Dados/normas , Sistemas de Apoio a Decisões Clínicas , Software , Sistemas Computacionais , Humanos , Modelos Teóricos
2.
BMC Fam Pract ; 16: 63, 2015 May 16.
Artigo em Inglês | MEDLINE | ID: mdl-25980623

RESUMO

BACKGROUND: Analysis of encounter data relevant to the diagnostic process sourced from routine electronic medical record (EMR) databases represents a classic example of the concept of a learning healthcare system (LHS). By collecting International Classification of Primary Care (ICPC) coded EMR data as part of the Transition Project from Dutch and Maltese databases (using the EMR TransHIS), data mining algorithms can empirically quantify the relationships of all presenting reasons for encounter (RfEs) and recorded diagnostic outcomes. We have specifically looked at new episodes of care (EoC) for two urinary system infections: simple urinary tract infection (UTI, ICPC code: U71) and pyelonephritis (ICPC code: U70). METHODS: Participating family doctors (FDs) recorded details of all their patient contacts in an EoC structure using the ICPC, including RfEs presented by the patient, and the FDs' diagnostic labels. The relationships between RfEs and episode titles were studied using probabilistic and data mining methods as part of the TRANSFoRm project. RESULTS: The Dutch data indicated that the presence of RfE's "Cystitis/Urinary Tract Infection", "Dysuria", "Fear of UTI", "Urinary frequency/urgency", "Haematuria", "Urine symptom/complaint, other" are all strong, reliable, predictors for the diagnosis "Cystitis/Urinary Tract Infection" . The Maltese data indicated that the presence of RfE's "Dysuria", "Urinary frequency/urgency", "Haematuria" are all strong, reliable, predictors for the diagnosis "Cystitis/Urinary Tract Infection". The Dutch data indicated that the presence of RfE's "Flank/axilla symptom/complaint", "Dysuria", "Fever", "Cystitis/Urinary Tract Infection", "Abdominal pain/cramps general" are all strong, reliable, predictors for the diagnosis "Pyelonephritis" . The Maltese data set did not present any clinically and statistically significant predictors for pyelonephritis. CONCLUSIONS: We describe clinically and statistically significant diagnostic associations observed between UTIs and pyelonephritis presenting as a new problem in family practice, and all associated RfEs, and demonstrate that the significant diagnostic cues obtained are consistent with the literature. We conclude that it is possible to generate clinically meaningful diagnostic evidence from electronic sources of patient data.


Assuntos
Técnicas de Apoio para a Decisão , Registros Eletrônicos de Saúde/normas , Cuidado Periódico , Medicina de Família e Comunidade , Pielonefrite/diagnóstico , Infecções Urinárias/diagnóstico , Mineração de Dados , Medicina de Família e Comunidade/métodos , Medicina de Família e Comunidade/normas , Humanos , Classificação Internacional de Doenças , Malta , Modelos Estatísticos , Países Baixos , Avaliação de Processos e Resultados em Cuidados de Saúde , Atenção Primária à Saúde/métodos , Atenção Primária à Saúde/normas , Reprodutibilidade dos Testes
3.
J Biomed Inform ; 43(6): 902-13, 2010 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-20688192

RESUMO

Important progress in treating diseases has been possible thanks to the identification of drug targets. Drug targets are the molecular structures whose abnormal activity, associated to a disease, can be modified by drugs, improving the health of patients. Pharmaceutical industry needs to give priority to their identification and validation in order to reduce the long and costly drug development times. In the last two decades, our knowledge about drugs, their mechanisms of action and drug targets has rapidly increased. Nevertheless, most of this knowledge is hidden in millions of medical articles and textbooks. Extracting knowledge from this large amount of unstructured information is a laborious job, even for human experts. Drug target articles identification, a crucial first step toward the automatic extraction of information from texts, constitutes the aim of this paper. A comparison of several machine learning techniques has been performed in order to obtain a satisfactory classifier for detecting drug target articles using semantic information from biomedical resources such as the Unified Medical Language System. The best result has been achieved by a Fuzzy Lattice Reasoning classifier, which reaches 98% of ROC area measure.


Assuntos
Inteligência Artificial , Mineração de Dados/métodos , Descoberta de Drogas , Algoritmos , Preparações Farmacêuticas/química , Unified Medical Language System
4.
Stud Health Technol Inform ; 210: 85-9, 2015.
Artigo em Inglês | MEDLINE | ID: mdl-25991107

RESUMO

Data mining of electronic health records (eHRs) allows us to identify patterns of patient data that characterize diseases and their progress and learn best practices for treatment and diagnosis. Clinical Prediction Rules (CPRs) are a form of clinical evidence that quantifies the contribution of different clinical data to a particular clinical outcome and help clinicians to decide the diagnosis, prognosis or therapeutic conduct for any given patient. The TRANSFoRm diagnostic support system (DSS) is based on the construction of an ontological repository of CPRs for diagnosis prediction in which clinical evidence is expressed using a unified vocabulary. This paper explains the proposed methodology for constructing this CPR repository, addressing algorithms and quality measures for filtering relevant rules. Some preliminary application results are also presented.


Assuntos
Algoritmos , Mineração de Dados/métodos , Registros Eletrônicos de Saúde/organização & administração , Aprendizado de Máquina , Processamento de Linguagem Natural , Sistemas de Apoio a Decisões Clínicas/organização & administração
5.
Stud Health Technol Inform ; 192: 1223, 2013.
Artigo em Inglês | MEDLINE | ID: mdl-23920997

RESUMO

Detailed insight into the recruitment parameters of a clinical trial is crucial to interpretation of its results, and reasons for its success or failure. Such recruitment is increasingly done through specialized software tools, sometimes linked to Electronic Health Record (EHR) systems, enabling automated capture of audit logs. However, in the absence of shared semantic models underpinning these logs, gathered data remains insular and opaque. We propose a standardized syntactical representation to capture the provenance of the recruitment task, and ground it in CRIM, a variant of the established PCROM information model for research in primary care. The method has been successfully prototyped in the EU FP7 TRANSFoRm project, where the recruitment eligibility query module has been integrated with a provenance capture infrastructure, resulting in the full reproducibility of the study design process.


Assuntos
Algoritmos , Registros Eletrônicos de Saúde/classificação , Registros Eletrônicos de Saúde/normas , Guias como Assunto/normas , Auditoria Médica/normas , Seleção de Pacientes , Vocabulário Controlado , Mineração de Dados/métodos , Mineração de Dados/normas , Auditoria Médica/métodos , Processamento de Linguagem Natural , Terminologia como Assunto
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