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1.
Artigo em Inglês | MEDLINE | ID: mdl-31963654

RESUMO

Ischemic stroke is the most common type of stroke, and early interventional treatment is associated with favorable outcomes. In the guidelines, thrombolytic therapy using recombinant tissue-type plasminogen activator (rt-PA) is recommended for eligible patients with acute ischemic stroke. However, the risk of hemorrhagic complications limits the use of rt-PA, and the risk factors for poor treatment outcomes need to be identified. To identify the risk factors associated with in-hospital poor outcomes in patients treated with rt-PA, we analyzed the electronic medical records of patients who were diagnosed with acute ischemic stroke and treated for rt-PA at Chang Gung Memorial Hospitals from 2006 to 2016. In-hospital death, intensive care unit (ICU) stay, or prolonged hospitalization were defined as unfavorable treatment outcomes. Medical history variables and laboratory test results were considered variables of interest to determine risk factors. Among 643 eligible patients, 537 (83.5%) and 106 (16.5%) patients had favorable and poor outcomes, respectively. In the multivariable analysis, risk factors associated with poor outcomes were female gender, higher stroke severity index (SSI), higher serum glucose levels, lower mean corpuscular hemoglobin concentration (MCHC), lower platelet counts, and anemia. The risk factors found in this research could help us study the treatment strategy for ischemic stroke.

2.
Front Microbiol ; 10: 2120, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31572327

RESUMO

Staphylococcus haemolyticus is one of the most significant coagulase-negative staphylococci, and it often causes severe infections. Rapid strain typing of pathogenic S. haemolyticus is indispensable in modern public health infectious disease control, facilitating the identification of the origin of infections to prevent further infectious outbreak. Rapid identification enables the effective control of pathogenic infections, which is tremendously beneficial to critically ill patients. However, the existing strain typing methods, such as multi-locus sequencing, are of relatively high cost and comparatively time-consuming. A practical method for the rapid strain typing of pathogens, suitable for routine use in clinics and hospitals, is still not available. Matrix-assisted laser desorption ionization-time of flight mass spectrometry combined with machine learning approaches is a promising method to carry out rapid strain typing. In this study, we developed a statistical test-based method to determine the reference spectrum when dealing with alignment of mass spectra datasets, and constructed machine learning-based classifiers for categorizing different strains of S. haemolyticus. The area under the receiver operating characteristic curve and accuracy of multi-class predictions were 0.848 and 0.866, respectively. Additionally, we employed a variety of statistical tests and feature-selection strategies to identify the discriminative peaks that can substantially contribute to strain typing. This study not only incorporates statistical test-based methods to manage the alignment of mass spectra datasets but also provides a practical means to accomplish rapid strain typing of S. haemolyticus.

3.
Sci Rep ; 9(1): 11074, 2019 Aug 19.
Artigo em Inglês | MEDLINE | ID: mdl-31423009

RESUMO

Trichomonas vaginalis (T. vaginalis) detection remains an unsolved problem in using of automated instruments for urinalysis. The study proposes a machine learning (ML)-based strategy to increase the detection rate of T. vaginalis in urine. On the basis of urinalysis data from a teaching hospital during 2009-2013, individuals underwent at least one urinalysis test were included. Logistic regression, support vector machine, and random forest, were used to select specimens with a high risk of T. vaginalis infection for confirmation through microscopic examinations. A total of 410,952 and 428,203 specimens from men and women were tested, of which 91 (0.02%) and 517 (0.12%) T. vaginalis-positive specimens were reported, respectively. The prediction models of T. vaginalis infection attained an area under the receiver operating characteristic curve of more than 0.87 for women and 0.83 for men. The Lift values of the top 5% risky specimens were above eight. While the most risky vigintile was picked out by the models and confirmed by microscopic examination, the incremental cost-effectiveness ratios for T. vaginalis detection in men and women were USD$170.1 and USD$29.7, respectively. On the basis of urinalysis, the proposed strategy can significantly increase the detection rate of T. vaginalis in a cost-effective manner.

4.
Int J Med Inform ; 128: 79-86, 2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-31103449

RESUMO

BACKGROUND: Approximately 10%-15% of patients with breast cancer die of cancer metastasis or recurrence, and early diagnosis of it can improve prognosis. Breast cancer outcomes may be prognosticated on the basis of surface markers of tumor cells and serum tests. However, evaluation of a combination of clinicopathological features may offer a more comprehensive overview for breast cancer prognosis. MATERIALS AND METHODS: We evaluated serum human epidermal growth factor receptor 2 (sHER2) as part of a combination of clinicopathological features used to predict breast cancer metastasis using machine learning algorithms, namely random forest, support vector machine, logistic regression, and Bayesian classification algorithms. The sample cohort comprised 302 patients who were diagnosed with and treated for breast cancer and received at least one sHER2 test at Chang Gung Memorial Hospital at Linkou between 2003 and 2016. RESULTS: The random-forest-based model was determined to be the optimal model to predict breast cancer metastasis at least 3 months in advance; the correspondingarea under the receiver operating characteristic curve value was 0. 75 (p < 0. 001). CONCLUSION: The random-forest-based model presented in this study may be helpful as part of a follow-up intervention decision support system and may lead to early detection of recurrence, early treatment, and more favorable outcomes.


Assuntos
Algoritmos , Biomarcadores/análise , Neoplasias da Mama/secundário , Aprendizado de Máquina , Teorema de Bayes , Neoplasias da Mama/sangue , Estudos de Coortes , Feminino , Humanos , Pessoa de Meia-Idade , Prognóstico , Curva ROC
5.
Front Microbiol ; 9: 2393, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30364336

RESUMO

Heterogeneous vancomycin-intermediate Staphylococcus aureus (hVISA) is an emerging superbug with implicit drug resistance to vancomycin. Detecting hVISA can guide the correct administration of antibiotics. However, hVISA cannot be detected in most clinical microbiology laboratories because the required diagnostic tools are either expensive, time consuming, or labor intensive. By contrast, matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) is a cost-effective and rapid tool that has potential for providing antibiotics resistance information. To analyze complex MALDI-TOF mass spectra, machine learning (ML) algorithms can be used to generate robust hVISA detection models. In this study, MALDI-TOF mass spectra were obtained from 35 hVISA/vancomycin-intermediate S. aureus (VISA) and 90 vancomycin-susceptible S. aureus isolates. The vancomycin susceptibility of the isolates was determined using an Etest and modified population analysis profile-area under the curve. ML algorithms, namely a decision tree, k-nearest neighbors, random forest, and a support vector machine (SVM), were trained and validated using nested cross-validation to provide unbiased validation results. The area under the curve of the models ranged from 0.67 to 0.79, and the SVM-derived model outperformed those of the other algorithms. The peaks at m/z 1132, 2895, 3176, and 6591 were noted as informative peaks for detecting hVISA/VISA. We demonstrated that hVISA/VISA could be detected by analyzing MALDI-TOF mass spectra using ML. Moreover, the results are particularly robust due to a strict validation method. The ML models in this study can provide rapid and accurate reports regarding hVISA/VISA and thus guide the correct administration of antibiotics in treatment of S. aureus infection.

6.
PLoS One ; 13(3): e0194289, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29534106

RESUMO

Methicillin-resistant Staphylococcus aureus (MRSA), one of the most important clinical pathogens, conducts an increasing number of morbidity and mortality in the world. Rapid and accurate strain typing of bacteria would facilitate epidemiological investigation and infection control in near real time. Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry is a rapid and cost-effective tool for presumptive strain typing. To develop robust method for strain typing based on MALDI-TOF spectrum, machine learning (ML) is a promising algorithm for the construction of predictive model. In this study, a strategy of building templates of specific types was used to facilitate generating predictive models of methicillin-resistant Staphylococcus aureus (MRSA) strain typing through various ML methods. The strain types of the isolates were determined through multilocus sequence typing (MLST). The area under the receiver operating characteristic curve (AUC) and the predictive accuracy of the models were compared. ST5, ST59, and ST239 were the major MLST types, and ST45 was the minor type. For binary classification, the AUC values of various ML methods ranged from 0.76 to 0.99 for ST5, ST59, and ST239 types. In multiclass classification, the predictive accuracy of all generated models was more than 0.83. This study has demonstrated that ML methods can serve as a cost-effective and promising tool that provides preliminary strain typing information about major MRSA lineages on the basis of MALDI-TOF spectra.


Assuntos
Técnicas de Tipagem Bacteriana/métodos , Aprendizado de Máquina , Staphylococcus aureus Resistente à Meticilina/classificação , Tipagem de Sequências Multilocus/métodos , Infecções Estafilocócicas/diagnóstico , Técnicas de Tipagem Bacteriana/economia , Análise por Conglomerados , Análise Custo-Benefício , Humanos , Staphylococcus aureus Resistente à Meticilina/genética , Tipagem de Sequências Multilocus/economia , Valor Preditivo dos Testes , Espectrometria de Massas por Ionização e Dessorção a Laser Assistida por Matriz/economia , Espectrometria de Massas por Ionização e Dessorção a Laser Assistida por Matriz/métodos , Infecções Estafilocócicas/microbiologia
7.
Int J Med Inform ; 111: 159-164, 2018 03.
Artigo em Inglês | MEDLINE | ID: mdl-29425627

RESUMO

OBJECTIVES: Prediction of activities of daily living (ADL) is crucial for optimized care of post-stroke patients. However, no suitably-validated and practical models are currently available in clinical practice. METHODS: Participants of a Post-acute Care-Cerebrovascular Diseases (PAC-CVD) program from a reference hospital in Taiwan between 2014 and 2016 were enrolled in this study. Based on 15 rehabilitation assessments, machine learning (ML) methods, namely logistic regression (LR), support vector machine (SVM), and random forest (RF), were used to predict the Barthel index (BI) status at discharge. Furthermore, SVM and linear regression were used to predict the actual BI scores at discharge. RESULTS: A total of 313 individuals (men: 208; women: 105) were enrolled in the study. All the classification models outperformed single assessments in predicting the BI statuses of the patients at discharge. The performance of the LR and RF algorithms was higher (area under ROC curve (AUC): 0.79) than that of SVM algorithm (AUC: 0.77). In addition, the mean absolute errors of both SVM and linear regression models in predicting the actual BI score at discharge were 9.86 and 9.95, respectively. CONCLUSIONS: The proposed ML-based method provides a promising and practical computer-assisted decision making tool for predicting ADL in clinical practice.


Assuntos
Atividades Cotidianas , Tomada de Decisões Assistida por Computador , Aprendizado de Máquina , Reabilitação do Acidente Vascular Cerebral/métodos , Acidente Vascular Cerebral/terapia , Idoso , Algoritmos , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Alta do Paciente , Estudos Retrospectivos , Acidente Vascular Cerebral/psicologia , Reabilitação do Acidente Vascular Cerebral/psicologia , Taiwan
8.
Diabetes Care ; 40(11): 1500-1505, 2017 11.
Artigo em Inglês | MEDLINE | ID: mdl-28903978

RESUMO

OBJECTIVE: The American Diabetes Association recommends metformin as first-line therapy for type 2 diabetes. However, nonadherence to antihyperglycemic medication is common, and a clinician could confuse nonadherence with pharmacologic failure, potentially leading to premature prescribing of second-line therapies. We measured metformin use prior to second-line therapy initialization. RESEARCH DESIGN AND METHODS: This retrospective cross-sectional study used unidentifiable member claims data from individuals covered from 2010 to 2015 by Aetna, a U.S. health benefits company. Beneficiaries with two physician claims or one hospitalization with a type 2 diabetes diagnosis were included. Recommended use of metformin was measured by the proportion of days covered over 60 days. Through sensitivity analysis, we varied estimates of the percentage of beneficiaries who used low-cost generic prescription medication programs. RESULTS: A total of 52,544 individuals with type 2 diabetes were eligible. Of 22,956 patients given second-line treatment, only 1,875 (8.2%) had evidence of recommended use of metformin in the prior 60 days, and 6,441 (28.0%) had no prior claims evidence of having taken metformin. At the top range of sensitivity, only 49.5% patients could have had recommended use. Patients were more likely to be given an additional second-line antihyperglycemic medication or insulin if they were given their initial second-line medication without evidence of recommended use of metformin (P < 0.001). CONCLUSIONS: Despite published guidelines, second-line therapy often is initiated without evidence of recommended use of first-line therapy. Apparent treatment failures, which may in fact be attributable to nonadherence to guidelines, are common. Point-of-care and population-level processes are needed to monitor and improve guideline adherence.


Assuntos
Diabetes Mellitus Tipo 2/tratamento farmacológico , Hipoglicemiantes/uso terapêutico , Adolescente , Adulto , Idoso , Estudos Transversais , Feminino , Seguimentos , Hospitalização , Humanos , Insulina/uso terapêutico , Masculino , Metformina/uso terapêutico , Pessoa de Meia-Idade , Medicamentos sob Prescrição , Estudos Retrospectivos , Sensibilidade e Especificidade , Adulto Jovem
10.
Vector Borne Zoonotic Dis ; 17(2): 116-122, 2017 02.
Artigo em Inglês | MEDLINE | ID: mdl-27855040

RESUMO

BACKGROUND: A Lyme disease (LD) diagnosis can be far from straightforward, particularly if erythema migrans does not develop or is not noticed. Extended courses of antibiotics for LD are not recommended, but their use is increasing. We sought to elucidate the patient patterns toward a diagnosis of LD, hypothesizing that a subset of patients ultimately receiving extended courses antibiotics may be symptomatic for an extended period before the first LD diagnosis. METHODS: Claims submitted to a nationwide U.S. health insurance plan in 14 high-prevalence states were grouped into standardized diagnostic categories. The patterns of diagnostic categories over time were compared between patients evaluated for LD and given standard antibiotic therapy (PLDSA) and patients evaluated for LD and given extended antibiotic therapy (PLDEA) in 2011-2012. RESULTS: The incidence of PLDSA was 40.45 (N = 3207) and that of PLDEA was 7.57 (N = 600) per 100,000 insured over 2011-2012. 50.3% of PLDEA were diagnosed in the nonsummer months. Seven diagnostic categories were associated with PLDEA. From 180 days before the first LD diagnosis, the risks of having claims associated with back problems (odds ratio [OR], 2.1; confidence interval [95% CI], 1.4-2.9; p < 0.001) and connective tissue disease (OR, 1.6; 95% CI, 1.1-2.3; p < 0.01) complaints were higher among PLDEA. From 90 days before the diagnosis, malaise and fatigue (OR, 1.7; 95% CI, 1.1-2.6; p < 0.05), other nervous system disorders (OR, 2.0; 95% CI, 1.3-3.1; p < 0.01), and nontraumatic joint disorder (OR, 1.4; 95% CI, 1.0-2.0; p < 0.05) were more likely found among PLDEA than PLDSA. From 30 days before the diagnosis, the risk for mental health (OR 1.6; 95% CI, 1.1-2.0; p < 0.01) and headache (OR 1.5; 95% CI, 1.1-2.0; p < 0.05) among PLDEA was elevated. CONCLUSIONS: Among patients evaluated for LD and ultimately receiving an extended course of antibiotics for LD, 15.8% of them were symptomatic and seeking care for several months before their first LD diagnosis.


Assuntos
Antibacterianos/uso terapêutico , Formulário de Reclamação de Seguro , Doença de Lyme/diagnóstico , Doença de Lyme/tratamento farmacológico , Adulto , Dor nas Costas/diagnóstico , Doenças do Tecido Conjuntivo/diagnóstico , Fadiga/diagnóstico , Feminino , Humanos , Artropatias/diagnóstico , Doença de Lyme/epidemiologia , Masculino , Pessoa de Meia-Idade , Doenças do Sistema Nervoso/diagnóstico , Razão de Chances , Fatores de Risco , Estados Unidos/epidemiologia
11.
Vector Borne Zoonotic Dis ; 15(10): 591-6, 2015 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-26393537

RESUMO

OBJECTIVE: Lyme disease (LD) is the most commonly reported tick-borne illness in North America. To improve LD surveillance, we explored claims data as an adjunct data source for monitoring trends in Lyme disease incidence. METHODS: We retrospectively analyzed claims from a nationwide US health insurance plan, identifying patients with newly diagnosed LD in 13 high-prevalence states over two time periods, 2004-2006 and 2010-2012. RESULTS: The average LD case incidence as estimated by using claims data in 2010-2012 (75.67 per 100,000 person-years, n = 3474) was 1.50 times higher than 2004-2006 (50.25 per 100,000 person-years, n = 1965) (p < 0.001) and higher than incidence reported by the states to the Centers for Disease Control and Prevention. Among the 13 highest-prevalence states, there were 11 states with increased LD incidence over time. CONCLUSIONS: Surveillance systems should explore a fusion of data sources, including payer claims that appear to be highly sensitive with limitations, with electronic laboratory data that afford high specificity, but appear to miss cases.


Assuntos
Revisão da Utilização de Seguros/estatística & dados numéricos , Doença de Lyme/epidemiologia , Vigilância da População/métodos , Doenças Transmitidas por Carrapatos/epidemiologia , Adolescente , Adulto , Criança , Pré-Escolar , Feminino , Humanos , Incidência , Lactente , Recém-Nascido , Masculino , Pessoa de Meia-Idade , Prevalência , Estudos Retrospectivos , Estados Unidos/epidemiologia , Adulto Jovem
12.
JMIR Med Inform ; 3(3): e31, 2015 Sep 21.
Artigo em Inglês | MEDLINE | ID: mdl-26392229

RESUMO

BACKGROUND: Surveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent. OBJECTIVE: To develop an automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), and to evaluate its performance by comparing it with a conventional infection control personnel (ICP)-based surveillance system. METHODS: We developed a Web-based system that was integrated into the medical information system of a 2200-bed teaching hospital in Taiwan. The system automatically detects and classifies HABSIs. RESULTS: In this study, the number of computer-detected HABSIs correlated closely with the number of HABSIs detected by ICP by department (n=20; r=.999 P<.001) and by time (n=14; r=.941; P<.001). Compared with reference standards, this system performed excellently with regard to sensitivity (98.16%), specificity (99.96%), positive predictive value (95.81%), and negative predictive value (99.98%). The system enabled decreasing the delay in confirmation of HABSI cases, on average, by 29 days. CONCLUSIONS: This system provides reliable and objective HABSI data for quality indicators, improving the delay caused by a conventional surveillance system.

13.
Clin Infect Dis ; 61(10): 1536-42, 2015 Nov 15.
Artigo em Inglês | MEDLINE | ID: mdl-26223992

RESUMO

BACKGROUND: Most patients with Lyme disease (LD) can be treated effectively with 2-4 weeks of antibiotics. The Infectious Disease Society of America guidelines do not currently recommend extended treatment even in patients with persistent symptoms. METHODS: To estimate the incidence of extended use of antibiotics in patients evaluated for LD, we retrospectively analyzed claims from a nationwide US health insurance plan in 14 high-prevalence states over 2 periods: 2004-2006 and 2010-2012. RESULTS: As measured by payer claims, the incidence of extended antibiotic therapy among patients evaluated for LD was higher in 2010-2012 (14.72 per 100 000 person-years; n = 684) than in 2004-2006 (9.94 per 100 000 person-years; n = 394) (P < .001). Among these patients, 48.8% were treated with ≥2 antibiotics in 2010-2012 and 29.9% in 2004-2006 (P < .001). In each study period, a distinct small group of providers (roughly 3%-4%) made the diagnosis in >20% of the patients who were evaluated for LD and prescribed extended antibiotic treatment. CONCLUSIONS: Insurance claims data suggest that the use of extended courses of antibiotics and multiple antibiotics in the treatment of LD has increased in recent years.


Assuntos
Antibacterianos/administração & dosagem , Doença de Lyme/tratamento farmacológico , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Criança , Pré-Escolar , Feminino , Humanos , Incidência , Lactente , Doença de Lyme/epidemiologia , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Fatores de Tempo , Estados Unidos/epidemiologia , Adulto Jovem
14.
IEEE J Biomed Health Inform ; 19(3): 1036-43, 2015 May.
Artigo em Inglês | MEDLINE | ID: mdl-25222960

RESUMO

A description of patient conditions should consist of the changes in and combination of clinical measures. Traditional data-processing method and classification algorithms might cause clinical information to disappear and reduce prediction performance. To improve the accuracy of clinical-outcome prediction by using multiple measurements, a new multiple-time-series data-processing algorithm with period merging is proposed. Clinical data from 83 hepatocellular carcinoma (HCC) patients were used in this research. Their clinical reports from a defined period were merged using the proposed merging algorithm, and statistical measures were also calculated. After data processing, multiple measurements support vector machine (MMSVM) with radial basis function (RBF) kernels was used as a classification method to predict HCC recurrence. A multiple measurements random forest regression (MMRF) was also used as an additional evaluation/classification method. To evaluate the data-merging algorithm, the performance of prediction using processed multiple measurements was compared to prediction using single measurements. The results of recurrence prediction by MMSVM with RBF using multiple measurements and a period of 120 days (accuracy 0.771, balanced accuracy 0.603) were optimal, and their superiority to the results obtained using single measurements was statistically significant (accuracy 0.626, balanced accuracy 0.459, P < 0.01). In the cases of MMRF, the prediction results obtained after applying the proposed merging algorithm were also better than single-measurement results (P < 0.05). The results show that the performance of HCC-recurrence prediction was significantly improved when the proposed data-processing algorithm was used, and that multiple measurements could be of greater value than single.


Assuntos
Algoritmos , Mineração de Dados/métodos , Modelos Estatísticos , Estudos de Tempo e Movimento , Bases de Dados Factuais , Humanos , Informática Médica , Reprodutibilidade dos Testes , Máquina de Vetores de Suporte
15.
Comput Methods Programs Biomed ; 117(3): 425-34, 2014 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-25278224

RESUMO

BACKGROUND AND OBJECTIVE: Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment. METHODS: From January 2007 to December 2009, 83 newly diagnosed HCC patients receiving RFA as their first treatment were enrolled. Five feature selection methods including genetic algorithm (GA), simulated annealing (SA) algorithm, random forests (RF) and hybrid methods (GA+RF and SA+RF) were utilized for selecting an important subset of features from a total of 16 clinical features. These feature selection methods were combined with support vector machine (SVM) for developing predictive models with better performance. Five-fold cross-validation was used to train and test SVM models. RESULTS: The developed SVM-based predictive models with hybrid feature selection methods and 5-fold cross-validation had averages of the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the ROC curve as 67%, 86%, 82%, 69%, 90%, and 0.69, respectively. CONCLUSIONS: The SVM derived predictive model can provide suggestive high-risk recurrent patients, who should be closely followed up after complete RFA treatment.


Assuntos
Carcinoma Hepatocelular/diagnóstico , Carcinoma Hepatocelular/radioterapia , Ablação por Cateter/métodos , Neoplasias Hepáticas/diagnóstico , Neoplasias Hepáticas/radioterapia , Máquina de Vetores de Suporte , Idoso , Feminino , Humanos , Masculino , Informática Médica/métodos , Pessoa de Meia-Idade , Modelos Teóricos , Valor Preditivo dos Testes , Curva ROC , Ondas de Rádio , Recidiva , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
16.
Telemed J E Health ; 19(9): 704-10, 2013 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-23869395

RESUMO

OBJECTIVE: To provide an efficient way for tracking patients' condition over long periods of time and to facilitate the collection of clinical data from different types of narrative reports, it is critical to develop an efficient method for smoothly analyzing the clinical data accumulated in narrative reports. MATERIALS AND METHODS: To facilitate liver cancer clinical research, a method was developed for extracting clinical factors from various types of narrative clinical reports, including ultrasound reports, radiology reports, pathology reports, operation notes, admission notes, and discharge summaries. An information extraction (IE) module was developed for tracking disease progression in liver cancer patients over time, and a rule-based classifier was developed for answering whether patients met the clinical research eligibility criteria. The classifier provided the answers and direct/indirect evidence (evidence sentences) for the clinical questions. To evaluate the implemented IE module and the classifier, the gold-standard annotations and answers were developed manually, and the results of the implemented system were compared with the gold standard. RESULTS: The IE model achieved an F-score from 92.40% to 99.59%, and the classifier achieved accuracy from 96.15% to 100%. CONCLUSIONS: The application was successfully applied to the various types of narrative clinical reports. It might be applied to the key extraction for other types of cancer patients.


Assuntos
Mineração de Dados/métodos , Registros Eletrônicos de Saúde , Nível de Saúde , Neoplasias Hepáticas , Progressão da Doença , Feminino , Humanos , Masculino , Modelos Teóricos , Processamento de Linguagem Natural , Taiwan
17.
J Med Internet Res ; 15(5): e98, 2013 May 23.
Artigo em Inglês | MEDLINE | ID: mdl-23702487

RESUMO

BACKGROUND: A hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification. OBJECTIVE: The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system was designed and deployed according to a service-oriented architecture (SOA) framework under the Web services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment, and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. METHODS: The framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as predicting cases. RESULTS: The feature selection strategies were implemented and the optimal markers for PKU, hypermethioninemia, and 3-MCC deficiency were obtained. The results of the machine learning approach were compared with the cutoff scheme. The number of the false positive cases were reduced from 21 to 2 for PKU, from 30 to 10 for hypermethioninemia, and 209 to 46 for 3-MCC deficiency. CONCLUSIONS: This SOA Web service-based newborn screening system can accelerate screening procedures effectively and efficiently. An SVM learning methodology for PKU, hypermethioninemia, and 3-MCC deficiency metabolic diseases classification, including optimal feature selection strategies, is presented. By adopting the results of this study, the number of suspected cases could be reduced dramatically.


Assuntos
Inteligência Artificial , Internet/estatística & dados numéricos , Doenças Metabólicas/diagnóstico , Triagem Neonatal , Padrões de Prática Médica , Humanos , Recém-Nascido , Máquina de Vetores de Suporte
18.
Stud Health Technol Inform ; 186: 145-9, 2013.
Artigo em Inglês | MEDLINE | ID: mdl-23542986

RESUMO

Healthcare-associated infections (HAIs) are a major patient safety issue. These adverse events add to the burden of resource use, promote resistance to antibiotics, and contribute to patient deaths and disability. A rule-based HAI classification and surveillance system was developed for automatic integration, analysis, and interpretation of HAIs and related pathogens. Rule-based classification system was design and implement to facilitate healthcare-associated bloodstream infection (HABSI) surveillance. Electronic medical records from a 2200-bed teaching hospital in Taiwan were classified according to predefined criteria of HABSI. The detailed information in each HABSI was presented systematically to support infection control personnel decision. The accuracy of HABSI classification was 0.94, and the square of the sample correlation coefficient was 0.99.


Assuntos
Algoritmos , Bacteriemia/diagnóstico , Bacteriemia/epidemiologia , Infecção Hospitalar/diagnóstico , Sistemas de Apoio a Decisões Clínicas , Vigilância da População/métodos , Infecção Hospitalar/epidemiologia , Feminino , Humanos , Masculino , Prevalência , Medição de Risco/métodos , Taiwan/epidemiologia
19.
JMIR Med Inform ; 1(1): e2, 2013 Oct 08.
Artigo em Inglês | MEDLINE | ID: mdl-25600078

RESUMO

BACKGROUND: Because of the increased adoption rate of electronic medical record (EMR) systems, more health care records have been increasingly accumulating in clinical data repositories. Therefore, querying the data stored in these repositories is crucial for retrieving the knowledge from such large volumes of clinical data. OBJECTIVE: The aim of this study is to develop a Web-based approach for enriching the capabilities of the data-querying system along the three following considerations: (1) the interface design used for query formulation, (2) the representation of query results, and (3) the models used for formulating query criteria. METHODS: The Guideline Interchange Format version 3.5 (GLIF3.5), an ontology-driven clinical guideline representation language, was used for formulating the query tasks based on the GLIF3.5 flowchart in the Protégé environment. The flowchart-based data-querying model (FBDQM) query execution engine was developed and implemented for executing queries and presenting the results through a visual and graphical interface. To examine a broad variety of patient data, the clinical data generator was implemented to automatically generate the clinical data in the repository, and the generated data, thereby, were employed to evaluate the system. The accuracy and time performance of the system for three medical query tasks relevant to liver cancer were evaluated based on the clinical data generator in the experiments with varying numbers of patients. RESULTS: In this study, a prototype system was developed to test the feasibility of applying a methodology for building a query execution engine using FBDQMs by formulating query tasks using the existing GLIF. The FBDQM-based query execution engine was used to successfully retrieve the clinical data based on the query tasks formatted using the GLIF3.5 in the experiments with varying numbers of patients. The accuracy of the three queries (ie, "degree of liver damage," "degree of liver damage when applying a mutually exclusive setting," and "treatments for liver cancer") was 100% for all four experiments (10 patients, 100 patients, 1000 patients, and 10,000 patients). Among the three measured query phases, (1) structured query language operations, (2) criteria verification, and (3) other, the first two had the longest execution time. CONCLUSIONS: The ontology-driven FBDQM-based approach enriched the capabilities of the data-querying system. The adoption of the GLIF3.5 increased the potential for interoperability, shareability, and reusability of the query tasks.

20.
J Med Internet Res ; 14(5): e131, 2012 Oct 24.
Artigo em Inglês | MEDLINE | ID: mdl-23195868

RESUMO

BACKGROUND: The emergence and spread of multidrug-resistant organisms (MDROs) are causing a global crisis. Combating antimicrobial resistance requires prevention of transmission of resistant organisms and improved use of antimicrobials. OBJECTIVES: To develop a Web-based information system for automatic integration, analysis, and interpretation of the antimicrobial susceptibility of all clinical isolates that incorporates rule-based classification and cluster analysis of MDROs and implements control chart analysis to facilitate outbreak detection. METHODS: Electronic microbiological data from a 2200-bed teaching hospital in Taiwan were classified according to predefined criteria of MDROs. The numbers of organisms, patients, and incident patients in each MDRO pattern were presented graphically to describe spatial and time information in a Web-based user interface. Hierarchical clustering with 7 upper control limits (UCL) was used to detect suspicious outbreaks. The system's performance in outbreak detection was evaluated based on vancomycin-resistant enterococcal outbreaks determined by a hospital-wide prospective active surveillance database compiled by infection control personnel. RESULTS: The optimal UCL for MDRO outbreak detection was the upper 90% confidence interval (CI) using germ criterion with clustering (area under ROC curve (AUC) 0.93, 95% CI 0.91 to 0.95), upper 85% CI using patient criterion (AUC 0.87, 95% CI 0.80 to 0.93), and one standard deviation using incident patient criterion (AUC 0.84, 95% CI 0.75 to 0.92). The performance indicators of each UCL were statistically significantly higher with clustering than those without clustering in germ criterion (P < .001), patient criterion (P = .04), and incident patient criterion (P < .001). CONCLUSION: This system automatically identifies MDROs and accurately detects suspicious outbreaks of MDROs based on the antimicrobial susceptibility of all clinical isolates.


Assuntos
Surtos de Doenças/classificação , Resistência a Múltiplos Medicamentos , Monitoramento Epidemiológico , Internet , Análise por Conglomerados , Bases de Dados Factuais , Surtos de Doenças/prevenção & controle , Surtos de Doenças/estatística & dados numéricos , Hospitais de Ensino , Humanos , Controle de Infecções , /epidemiologia , Epidemiologia Molecular , Estudos Prospectivos , Software , Taiwan/epidemiologia
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