Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 11.567
Filtrar
1.
Artículo en Inglés | MEDLINE | ID: mdl-33808263

RESUMEN

The number of breast reconstructions following mastectomy has increased significantly during the last decades, but women are experiencing a number of conflicts with breast reconstruction decisions. The aim of this study was to develop a decision tree model of breast reconstruction and to examine its predictability. Mixed method design using ethnographic decision tree modeling was used. In the qualitative stage, data were collected using individual and focus group interviews and analyzed to construct a decision tree model. In the quantitative stage, the questionnaire was developed questions based on the criteria identified in the qualitative stage. A total of 61 women with breast cancer participated in 2017. Five major criteria: recovery of body image; impact on recurrence; recommendations from others; financial resources; and confirmation by physicians. The model also included nine predictive pathways. It turns out that the model predicted 90% of decisions concerning whether or not to have breast reconstruction. The findings indicate that the five criteria play a key role in decision-making about whether or not to have breast reconstruction. Thus, more comprehensive issues, including these five criteria, need to be integrated into an intervention for women with breast cancer to make their best decision on breast reconstruction.


Asunto(s)
Neoplasias de la Mama , Mamoplastia , Neoplasias de la Mama/cirugía , Toma de Decisiones , Árboles de Decisión , Femenino , Humanos , Mastectomía , Recurrencia Local de Neoplasia
2.
Ecotoxicol Environ Saf ; 214: 112114, 2021 May.
Artículo en Inglés | MEDLINE | ID: mdl-33711575

RESUMEN

Endocrine disrupting chemicals can mimic, block, or interfere with hormones in organisms and subsequently affect their development and reproduction, which has raised significant public concern over the past several decades. To investigate (quantitative) structure-activity relationship, 8280 compounds were compiled from the Tox21 10K compound library. The results show that 50% activity concentrations of agonists are poorly related to that of antagonists because many compounds have considerably different activity concentrations between the agonists and antagonists. Analysis on the chemical classes based on mode of action (MOA) reveals that estrogen receptor (ER) is not the main target site in the acute toxicity to aquatic organisms. Binomial analysis of active and inactive ER agonists/antagonists reveals that ER activity of compounds is dominated by octanol/water partition coefficient and excess molar refraction. The binomial equation developed from the two descriptors can classify well active and inactive ER chemicals with an overall prediction accuracy of 73%. The classification equation developed from the molecular descriptors indicates that estrogens react with the receptor through hydrophobic and π-n electron interactions. At the same time, molecular ionization, polarity, and hydrogen bonding ability can also affect the chemical ER activity. A decision tree developed from chemical structures and their applications reveals that many hormones, proton pump inhibitors, PAHs, progestin, insecticides, fungicides, steroid and chemotherapy medications are active ER agonists/antagonists. On the other hand, many monocyclic/nonaromatic chain compounds and herbicides are inactive ER compounds. The decision tree and binomial equation developed here are valuable tools to predict active and inactive ER compounds.


Asunto(s)
Disruptores Endocrinos/clasificación , Antagonistas de Estrógenos/clasificación , Estrógenos/clasificación , Receptores Estrogénicos/antagonistas & inhibidores , Árboles de Decisión , Disruptores Endocrinos/química , Disruptores Endocrinos/farmacología , Antagonistas de Estrógenos/química , Antagonistas de Estrógenos/farmacología , Estrógenos/química , Estrógenos/farmacología , Relación Estructura-Actividad Cuantitativa , Bibliotecas de Moléculas Pequeñas
3.
PLoS One ; 16(3): e0247995, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33657164

RESUMEN

BACKGROUND: Primary care is the major point of access in most health systems in developed countries and therefore for the detection of coronavirus disease 2019 (COVID-19) cases. The quality of its IT systems, together with access to the results of mass screening with Polymerase chain reaction (PCR) tests, makes it possible to analyse the impact of various concurrent factors on the likelihood of contracting the disease. METHODS AND FINDINGS: Through data mining techniques with the sociodemographic and clinical variables recorded in patient's medical histories, a decision tree-based logistic regression model has been proposed which analyses the significance of demographic and clinical variables in the probability of having a positive PCR in a sample of 7,314 individuals treated in the Primary Care service of the public health system of Catalonia. The statistical approach to decision tree modelling allows 66.2% of diagnoses of infection by COVID-19 to be classified with a sensitivity of 64.3% and a specificity of 62.5%, with prior contact with a positive case being the primary predictor variable. CONCLUSIONS: The use of a classification tree model may be useful in screening for COVID-19 infection. Contact detection is the most reliable variable for detecting Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases. The model would support that, beyond a symptomatic diagnosis, the best way to detect cases would be to engage in contact tracing.


Asunto(s)
/diagnóstico , Transmisión de Enfermedad Infecciosa/estadística & datos numéricos , Adulto , Anciano , Estudios de Cohortes , Trazado de Contacto , Minería de Datos/métodos , Árboles de Decisión , Femenino , Humanos , Masculino , Tamizaje Masivo/métodos , Persona de Mediana Edad , Probabilidad , Estudios Retrospectivos , Sensibilidad y Especificidad
4.
PLoS One ; 16(3): e0248438, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33690722

RESUMEN

OBJECTIVES: Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. METHODS: Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. RESULTS: Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation<95%, Black race, Hispanic or Latino ethnicity, household contact with known or suspected COVID-19, patient reported history of dry cough, anosmia/dysgeusia, myalgias or fever; and -1 point each for White race, no direct contact with infected person, or smoking. In the validation sample (n = 9,975), the probability from logistic regression score produced an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.79-0.81), and this level of accuracy was retained across patients enrolled from the early spring to summer of 2020. In the simplified score, a score of zero produced a sensitivity of 95.6% (94.8-96.3%), specificity of 20.0% (19.0-21.0%), negative likelihood ratio of 0.22 (0.19-0.26). Increasing points on the simplified score predicted higher probability of infection (e.g., >75% probability with +5 or more points). CONCLUSION: Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.


Asunto(s)
/diagnóstico , Servicio de Urgencia en Hospital/tendencias , Adulto , Anciano , Reglas de Decisión Clínica , Infecciones por Coronavirus/diagnóstico , Tos , Bases de Datos Factuales , Árboles de Decisión , Servicio de Urgencia en Hospital/estadística & datos numéricos , Femenino , Fiebre , Humanos , Masculino , Tamizaje Masivo , Persona de Mediana Edad , Sistema de Registros , Estados Unidos/epidemiología
5.
J Korean Acad Nurs ; 51(1): 40-53, 2021 Feb.
Artículo en Coreano | MEDLINE | ID: mdl-33706330

RESUMEN

PURPOSE: The purpose of this study was to develop and compare the prediction model for suicide attempts by Korean adolescents using logistic regression and decision tree analysis. METHODS: This study utilized secondary data drawn from the 2019 Youth Health Risk Behavior web-based survey. A total of 20 items were selected as the explanatory variables (5 of sociodemographic characteristics, 10 of health-related behaviors, and 5 of psychosocial characteristics). For data analysis, descriptive statistics and logistic regression with complex samples and decision tree analysis were performed using IBM SPSS ver. 25.0 and Stata ver. 16.0. RESULTS: A total of 1,731 participants (3.0%) out of 57,303 responded that they had attempted suicide. The most significant predictors of suicide attempts as determined using the logistic regression model were experience of sadness and hopelessness, substance abuse, and violent victimization. Girls who have experience of sadness and hopelessness, and experience of substance abuse have been identified as the most vulnerable group in suicide attempts in the decision tree model. CONCLUSION: Experiences of sadness and hopelessness, experiences of substance abuse, and experiences of violent victimization are the common major predictors of suicide attempts in both logistic regression and decision tree models, and the predict rates of both models were similar. We suggest to provide programs considering combination of high-risk predictors for adolescents to prevent suicide attempt.


Asunto(s)
Conducta del Adolescente , Árboles de Decisión , Intento de Suicidio/psicología , Adolescente , Depresión/patología , Femenino , Humanos , Internet , Modelos Logísticos , Masculino , Factores de Riesgo , Aislamiento Social , Estrés Psicológico , Trastornos Relacionados con Sustancias/patología , Ideación Suicida , Encuestas y Cuestionarios
6.
Environ Monit Assess ; 193(4): 183, 2021 Mar 12.
Artículo en Inglés | MEDLINE | ID: mdl-33712944

RESUMEN

In a world where pristine water is becoming scarcer, the need to reuse water becomes imperative. In this context explaining the water quality, purpose fitness and the parameters or conditions of the water body to adjust so as to improve its quality, are of great relevance. The goal of the present study was the use of water, riverine, and biodiversity quality indices to assess the condition of the studied urban wetland, since no single index can provide a complete health assessment of a water body. Decision trees were also used to elucidate the best water parameters to mend in order to recover the overall health of the urban wetland. The decision trees identified relevant physicochemical parameters as well as their approximate concentration at which a healthy water environment can be sustained for zooplankton and proved to be a powerful and simple alternative to customary approaches. Suspended particles and phosphates proved to be important parameters with concentrations approximately lower than 88 mg L-1 and 11 mg L-1, respectively, for a good biodiversity index of zooplankton. Ammonia, total coliforms, BOD, nitrates, and sodium were the main parameters that affected the water quality index. The vegetation coverage and its structure were the driving factors in the riverine quality index of the wetland.


Asunto(s)
Monitoreo del Ambiente , Humedales , Animales , Biodiversidad , Árboles de Decisión , Calidad del Agua
7.
JAMA Netw Open ; 4(2): e210037, 2021 02 01.
Artículo en Inglés | MEDLINE | ID: mdl-33625508

RESUMEN

Importance: Treatment with atezolizumab plus bevacizumab may prolong overall survival among patients with unresectable hepatocellular carcinoma. However, to our knowledge, the cost-effectiveness of using this high-priced therapy for this indication is currently unknown. Objective: To evaluate the cost-effectiveness of atezolizumab plus bevacizumab to treat unresectable hepatocellular carcinoma from the US payer perspective. Design, Setting, and Participants: This economic evaluation used a partitioned survival model consisting of 3 discrete health states to assess the cost-effectiveness of treatment of hepatocellular carcinoma with atezolizumab plus bevacizumab vs sorafenib. The characteristics of patients in the model were similar to patients in a phase 3, open-label randomized clinical trial (IMbrave150) who had unresectable hepatocellular carcinoma and had not previously received systemic treatment. Key clinical data were generated from the IMbrave150 trial conducted between March 15, 2018, and January 30, 2019, and cost and health preference data were collected from the literature. Main Outcomes and Measures: Costs, quality-adjusted life-years (QALYs), incremental cost-utility ratios, incremental net health benefits, and incremental net monetary benefits were calculated for the 2 treatment strategies. Subgroup, 1-way sensitivity, and probabilistic sensitivity analyses were performed. Results: Treatment of hepatocellular carcinoma with atezolizumab plus bevacizumab added 0.530 QALYs and resulted in an incremental cost of $89 807 compared with sorafenib therapy, which had an incremental cost-utility ratio of $169 223 per QALY gained. The incremental net health benefit was -0.068 QALYs, and the incremental net monetary benefit was -$10 202 at a willingness-to-pay threshold of $150 000/QALY. The probabilistic sensitivity analysis indicated that treatment with atezolizumab plus bevacizumab achieved a 35% probability of cost-effectiveness at a threshold of $150 000/QALY. One-way sensitivity analysis revealed that the results were most sensitive to the hazard ratio of overall survival. The subgroup analysis found that treatment with atezolizumab plus bevacizumab was associated with preferred incremental net health benefits in several subgroups, including patients with hepatitis B and C. Conclusions and Relevance: Atezolizumab plus bevacizumab treatment is unlikely to be a cost-effective option compared with sorafenib for patients with unresectable hepatocellular carcinoma. Reducing the prices of atezolizumab and bevacizumab may improve cost-effectiveness. The economic outcomes also may be improved by tailoring treatments based on individual patient factors.


Asunto(s)
Antineoplásicos/uso terapéutico , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapéutico , Carcinoma Hepatocelular/tratamiento farmacológico , Neoplasias Hepáticas/tratamiento farmacológico , Sorafenib/uso terapéutico , Anciano , Anticuerpos Monoclonales Humanizados/administración & dosificación , Antineoplásicos/economía , Protocolos de Quimioterapia Combinada Antineoplásica/economía , Bevacizumab/administración & dosificación , Carcinoma Hepatocelular/patología , Análisis Costo-Beneficio , Árboles de Decisión , Progresión de la Enfermedad , Costos de los Medicamentos , Femenino , Humanos , Neoplasias Hepáticas/patología , Masculino , Persona de Mediana Edad , Mortalidad , Supervivencia sin Progresión , Modelos de Riesgos Proporcionales , Años de Vida Ajustados por Calidad de Vida , Sorafenib/economía , Resultado del Tratamiento
8.
J Stroke Cerebrovasc Dis ; 30(4): 105636, 2021 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-33545520

RESUMEN

BACKGROUND AND PURPOSE: The importance of environmental factors for stroke patients to achieve home discharge was not scientifically proven. There are limited studies on the application of the decision tree algorithm with various functional and environmental variables to identify stroke patients with a high possibility of home discharge. The present study aimed to identify the factors, including functional and environmental factors, affecting home discharge after stroke inpatient rehabilitation using the machine learning method. METHOD: This was a cohort study on data from the maintained database of all patients with stroke who were admitted to the convalescence rehabilitation ward of our facility. In total, 1125 stroke patients were investigated. We developed three classification and regression tree (CART) models to identify the possibility of home discharge after inpatient rehabilitation. RESULTS: Among three models, CART model incorporating basic information, functional factor, and environmental factor variables achieved the highest accuracy for identification of home discharge. This model identified FIM dressing of the upper body (score of ≤2 or >2) as the first single discriminator for home discharge. Performing house renovation was associated with a high possibility of home discharge even in patients with stroke who had a poor FIM score in the ability to dress the upper body (≤2) at admission into the convalescence rehabilitation ward. Interestingly, many patients who performed house renovation have achieved home discharge regardless of the degree of lower limb paralysis. CONCLUSION: We identified the influential factors for realizing home discharge using the decision tree algorithm, including environmental factors, in patients with convalescent stroke.


Asunto(s)
Técnicas de Apoyo para la Decisión , Árboles de Decisión , Aprendizaje Automático , Alta del Paciente , Rehabilitación de Accidente Cerebrovascular , Accidente Cerebrovascular/terapia , Anciano , Anciano de 80 o más Años , Bases de Datos Factuales , Evaluación de la Discapacidad , Ambiente , Femenino , Humanos , Masculino , Persona de Mediana Edad , Actividad Motora , Valor Predictivo de las Pruebas , Recuperación de la Función , Accidente Cerebrovascular/diagnóstico , Accidente Cerebrovascular/fisiopatología , Factores de Tiempo , Resultado del Tratamiento
9.
J Stroke Cerebrovasc Dis ; 30(4): 105641, 2021 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-33549861

RESUMEN

OBJECTIVES: The purpose of this study was to clarify the interaction among cognitive and physical functions associated with toilet independence in stroke patients. MATERIALS AND METHODS: We retrospectively examined 125 stroke patients. We performed decision tree analysis to detect the interaction associated with toilet independent using assessment of motor function on the affected side, muscle strength on unaffected side, trunk function, neglect, motivation, and cognitive function. The interactions detected via decision tree confirmed the existence and influence using logistic regression. RESULTS: The verticality test of the Stroke Impairment Assessment Set (3 or ≤2 points) was selected at the first level, and the Revised Hasegawa's dementia scale (≥19 or ≤18 points) and age (≥70 or ≤69 y) were selected at the second level of decision tree. Interaction terms created by these factors were significantly associated with toilet independence after adjusting for the independent influence of each factor using logistic regression. CONCLUSIONS: Our results show an interaction of trunk and cognitive functions or trunk function and age associated with toilet independence. The probability of toilet independence dramatically changes if two factors of each interaction were satisfied in stroke patients.


Asunto(s)
Actividades Cotidianas , Cognición , Defecación , Actividad Motora , Autocuidado , Rehabilitación de Accidente Cerebrovascular , Accidente Cerebrovascular/terapia , Micción , Factores de Edad , Anciano , Anciano de 80 o más Años , Árboles de Decisión , Evaluación de la Discapacidad , Femenino , Humanos , Masculino , Persona de Mediana Edad , Motivación , Fuerza Muscular , Recuperación de la Función , Estudios Retrospectivos , Accidente Cerebrovascular/diagnóstico , Accidente Cerebrovascular/fisiopatología , Accidente Cerebrovascular/psicología , Torso/fisiopatología , Resultado del Tratamiento
10.
Comput Intell Neurosci ; 2021: 6656770, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33628217

RESUMEN

Cardiotocography data uncertainty is a critical task for the classification in biomedical field. Constructing good and efficient classifier via machine learning algorithms is necessary to help doctors in diagnosing the state of fetus heart rate. The proposed neutrosophic diagnostic system is an Interval Neutrosophic Rough Neural Network framework based on the backpropagation algorithm. It benefits from the advantages of neutrosophic set theory not only to improve the performance of rough neural networks but also to achieve a better performance than the other algorithms. The experimental results visualize the data using the boxplot for better understanding of attribute distribution. The performance measurement of the confusion matrix for the proposed framework is 95.1, 94.95, 95.2, and 95.1 concerning accuracy rate, precision, recall, and F1-score, respectively. WEKA application is used to analyse cardiotocography data performance measurement of different algorithms, e.g., neural network, decision table, the nearest neighbor, and rough neural network. The comparison with other algorithms shows that the proposed framework is both feasible and efficient classifier. Additionally, the receiver operation characteristic curve displays the proposed framework classifications of the pathologic, normal, and suspicious states by 0.93, 0.90, and 0.85 areas that are considered high and acceptable under the curve, respectively. Improving the performance measurements of the proposed framework by removing ineffective attributes via feature selection would be suitable advancement in the future. Moreover, the proposed framework can also be used in various real-life problems such as classification of coronavirus, social media, and satellite image.


Asunto(s)
Inteligencia Artificial , Cardiotocografía/métodos , Aprendizaje Automático , Algoritmos , Árboles de Decisión , Humanos , Redes Neurales de la Computación , Valor Predictivo de las Pruebas , Reproducibilidad de los Resultados , Máquina de Vectores de Soporte
11.
Artículo en Inglés | MEDLINE | ID: mdl-33528445

RESUMEN

Vertical bone augmentation (VBA) procedures for dental implant placement are biologically and technically challenging. Systematic reviews and meta-analyses of studies on VBA have failed to identify clinical procedures that provide superior results for treatment of the vertical ridge deficiencies. A decision tree was developed to guide clinicians on selecting treatment options based on reported vertical bone gains (< 5 mm, 5 to 8 mm, > 8 mm). The choice of a particular augmentation technique will also depend on other factors, including the size and morphology of the defect, location, and clinician or patient preferences. Surgeons should consider the advantages and disadvantages of each option for the clinical situation and select an approach with low complications, low cost, and the highest likelihood of success.


Asunto(s)
Pérdida de Hueso Alveolar , Aumento de la Cresta Alveolar , Implantes Dentales , Pérdida de Hueso Alveolar/cirugía , Trasplante Óseo , Árboles de Decisión , Implantación Dental Endoósea , Humanos
12.
J Environ Radioact ; 229-230: 106542, 2021 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-33581483

RESUMEN

We present a novel application of machine learning techniques to optimize the design of a radiation detection system. A decision tree-based algorithm is described which greedily optimizes partitioning of energy depositions based on a minimum detectable concentration metric - appropriate for radiation measurement. We apply this method to the task of optimizing sensitivity to radioxenon decays in the presence of a high rate of radon-progeny backgrounds (i.e., assuming no physical radon removal by traditional gas separation techniques). Assuming other backgrounds are negligible, and considering sensitivity to each xenon isotope separately (neglecting interference between isotopes), we find that, in general, high resolution readout and high spatial segmentation yield little additional capability to discriminate against radon backgrounds compared to simpler detector designs.


Asunto(s)
Contaminantes Radiactivos del Aire , Monitoreo de Radiación , Radón , Contaminantes Radiactivos del Aire/análisis , Árboles de Decisión , Radón/análisis , Radioisótopos de Xenón/análisis
13.
Am Heart J ; 234: 31-41, 2021 04.
Artículo en Inglés | MEDLINE | ID: mdl-33387469

RESUMEN

IMPORTANCE: The arrest and the post-arrest period are an incredibly emotionally traumatic time for family and friends of the affected individual. There is a need to assess prognosis early in the patient pathway to offer objective, realistic and non-emotive information to the next-of-kin regarding the likelihood of survival. OBJECTIVE: To present a systematic review of the clinical risk scores available to assess patients on admission following out-of-hospital cardiac arrest (OHCA) which can predict in-hospital mortality. EVIDENCE REVIEW: A systematic search of online databases Embase, MEDLINE and Cochrane Central Register of Controlled Trials was conducted up until 20th November 2020. FINDINGS: Out of 1,817 initial articles, we identified a total of 28 scoring systems, with 11 of the scores predicting mortality following OHCA included in this review. The majority of the scores included arrest characteristics (initial rhythm and time to return of spontaneous circulation) as prognostic indicators. Out of these, the 3 most clinically-useful scores, namely those which are easy-to-use, comprise of commonly available parameters and measurements, and which have high predictive value are the OHCA, NULL-PLEASE, and rCAST scores, which appear to perform similarly. Of these, the NULL-PLEASE score is the easiest to calculate and has also been externally validated. CONCLUSIONS: Clinicians should be aware of these risk scores, which can be used to provide objective, nonemotive and reproducible information to the next-of-kin on the likely prognosis following OHCA. However, in isolation, these scores should not form the basis for clinical decision-making.


Asunto(s)
Mortalidad Hospitalaria , Paro Cardíaco Extrahospitalario/mortalidad , Apoyo Vital Cardíaco Avanzado , Área Bajo la Curva , Árboles de Decisión , Frecuencia Cardíaca , Humanos , Hipotermia/mortalidad , Hipotermia Inducida , Paro Cardíaco Extrahospitalario/terapia , Pronóstico , Calidad de Vida , Factores de Riesgo , Sensibilidad y Especificidad , Índice de Severidad de la Enfermedad
14.
Arch Gynecol Obstet ; 303(3): 811-820, 2021 03.
Artículo en Inglés | MEDLINE | ID: mdl-33394142

RESUMEN

PURPOSE: Our objective was to establish a random forest model and to evaluate its predictive capability of the treatment effect of neoadjuvant chemotherapy-radiation therapy. METHODS: This retrospective study included 82 patients with locally advanced cervical cancer who underwent scanning from March 2013 to May 2018. The random forest model was established and optimised based on the open source toolkit scikit-learn. Byoptimising of the number of decision trees in the random forest, the criteria for selecting the final partition index and the minimum number of samples partitioned by each node, the performance of random forest in the prediction of the treatment effect of neoadjuvant chemotherapy-radiation therapy on advanced cervical cancer (> IIb) was evaluated. RESULTS: The number of decision trees in the random forests influenced the model performance. When the number of decision trees was set to 10, 25, 40, 55, 70, 85 and 100, the performance of random forest model exhibited an increasing trend first and then a decreasing one. The criteria for the selection of final partition index showed significant effects on the generation of decision trees. The Gini index demonstrated a better effect compared with information gain index. The area under the receiver operating curve for Gini index attained a value of 0.917. CONCLUSION: The random forest model showed potential in predicting the treatment effect of neoadjuvant chemotherapy-radiation therapy based on high-resolution T2WIs for advanced cervical cancer (> IIb).


Asunto(s)
Cuello del Útero/efectos de los fármacos , Cuello del Útero/efectos de la radiación , Quimioradioterapia/métodos , Terapia Neoadyuvante/métodos , Neoplasias del Cuello Uterino/terapia , Adulto , Cuello del Útero/diagnóstico por imagen , Árboles de Decisión , Femenino , Humanos , Imagen por Resonancia Magnética/métodos , Persona de Mediana Edad , Estadificación de Neoplasias , Evaluación de Resultado en la Atención de Salud , Estudios Retrospectivos , Neoplasias del Cuello Uterino/patología
15.
Sensors (Basel) ; 21(2)2021 Jan 13.
Artículo en Inglés | MEDLINE | ID: mdl-33450967

RESUMEN

Smart wearable robotic system, such as exoskeleton assist device and powered lower limb prostheses can rapidly and accurately realize man-machine interaction through locomotion mode recognition system. However, previous locomotion mode recognition studies usually adopted more sensors for higher accuracy and effective intelligent algorithms to recognize multiple locomotion modes simultaneously. To reduce the burden of sensors on users and recognize more locomotion modes, we design a novel decision tree structure (DTS) based on using an improved backpropagation neural network (IBPNN) as judgment nodes named IBPNN-DTS, after analyzing the experimental locomotion mode data using the original values with a 200-ms time window for a single inertial measurement unit to hierarchically identify nine common locomotion modes (level walking at three kinds of speeds, ramp ascent/descent, stair ascent/descent, Sit, and Stand). In addition, we reduce the number of parameters in the IBPNN for structure optimization and adopted the artificial bee colony (ABC) algorithm to perform global search for initial weight and threshold value to eliminate system uncertainty because randomly generated initial values tend to result in a failure to converge or falling into local optima. Experimental results demonstrate that recognition accuracy of the IBPNN-DTS with ABC optimization (ABC-IBPNN-DTS) was up to 96.71% (97.29% for the IBPNN-DTS). Compared to IBPNN-DTS without optimization, the number of parameters in ABC-IBPNN-DTS shrank by 66% with only a 0.58% reduction in accuracy while the classification model kept high robustness.


Asunto(s)
Locomoción , Algoritmos , Amputados , Miembros Artificiales , Árboles de Decisión , Humanos , Caminata
16.
Chemosphere ; 270: 129449, 2021 May.
Artículo en Inglés | MEDLINE | ID: mdl-33418218

RESUMEN

Pesticides are chemical compounds used to kill pests and weeds. Due to their nature, pesticides are potentially toxic to many organisms, including humans. Among the various methods used to decontaminate pesticides from the environment, the heterogeneous photocatalytic process is one of the most effective approaches. This study focuses on artificial intelligence (AI) techniques used to generate optimum predictive models for pesticide decontamination processes using heterogeneous photocatalytic processes. In the present study, 537 valid cases from 45 articles from January 2000 to April 2020 were filtered based on their content collected and analyzed. Based on cross-industry standard process (CRISP) methodology, a set of four classifiers were applied: Decision Trees (DT), Bayesian Network (BN), Support Vector Machines (SVM), and Feed Forward Multilayer Perceptron Neural Networks (MLP). To compare the accuracy of the selected algorithms, accuracy, and sensitivity criteria were applied. After the final analysis, the DT classification algorithm with seven factors of prediction, the accuracy of 91.06%, and sensitivity of 80.32% was selected as the optimal predictor model.


Asunto(s)
Inteligencia Artificial , Plaguicidas , Algoritmos , Teorema de Bayes , Minería de Datos , Árboles de Decisión , Descontaminación , Humanos
18.
Urol Clin North Am ; 48(1): 91-101, 2021 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-33218597

RESUMEN

Robotically assisted laparoscopic techniques may be used for proximal and distal ureteral strictures. Distal strictures may be approached with ureteroneocystotomy, psoas hitch, and Boari flap. Ureteroureterostomy, buccal mucosa graft ureteroplasty, and appendiceal flap ureteroplasty are viable techniques for strictures anywhere along the ureter. Ileal ureteral substitution is reserved for more extensive disease, and autotransplantation is reserved for salvage situations.


Asunto(s)
Constricción Patológica/cirugía , Procedimientos Quirúrgicos Reconstructivos/métodos , Procedimientos Quirúrgicos Robotizados/métodos , Uréter/cirugía , Obstrucción Ureteral/cirugía , Procedimientos Quirúrgicos Urológicos/métodos , Algoritmos , Constricción Patológica/diagnóstico , Constricción Patológica/etiología , Árboles de Decisión , Humanos , Íleon/trasplante , Mucosa Bucal/trasplante , Atención Perioperativa , Procedimientos Quirúrgicos Reconstructivos/instrumentación , Reimplantación , Colgajos Quirúrgicos , Uréter/anatomía & histología , Uréter/irrigación sanguínea , Obstrucción Ureteral/diagnóstico , Obstrucción Ureteral/etiología , Procedimientos Quirúrgicos Urológicos/instrumentación
19.
J Surg Res ; 257: 118-127, 2021 01.
Artículo en Inglés | MEDLINE | ID: mdl-32823009

RESUMEN

BACKGROUND: As the population ages, the incidence of traumatic falls has been increasing. We hypothesize that a machine learning algorithm can more accurately predict mortality after a fall compared with a standard logistic regression (LR) model based on immediately available admission data. Secondary objectives were to predict who would be discharged home and determine which variables had the largest effect on prediction. METHODS: All patients who were admitted for fall between 2012 and 2017 at our level 1 trauma center were reviewed. Fourteen variables describing patient demographics, injury characteristics, and physiology were collected at the time of admission and were used for prediction modeling. Algorithms assessed included LR, decision tree classifier (DTC), and random forest classifier (RFC). Area under the receiver operating characteristic curve (AUC) values were calculated for each algorithm for mortality and discharge to home. RESULTS: About 4725 patients met inclusion criteria. The mean age was 61 ± 20.5 y, Injury Severity Score 8 ± 7, length of stay 5.8 ± 7.6 d, intensive care unit length of stay 1.8± 5.2 d, and ventilator days 0.7 ± 4.2 d. The mortality rate was 3% and three times greater for elderly (aged 65 y and older) patients (5.0% versus 1.6%, P < 0.001). The AUC for predicting mortality for LR, DTC, and RFC was 0.78, 0.64, and 0.86, respectively. The AUC for predicting discharge to home for LR, DTC, and RFC was 0.72, 0.61, and 0.74, respectively. The top five variables that contribute to the prediction of mortality in descending order of importance are the Glasgow Coma Score (GCS) motor, GCS verbal, respiratory rate, GCS eye, and temperature. CONCLUSIONS: RFC can accurately predict mortality and discharge home after a fall. This predictive model can be implemented at the time of patient arrival and may help identify candidates for targeted intervention as well as improve prognostication and resource utilization.


Asunto(s)
Accidentes por Caídas/mortalidad , Accidentes por Caídas/estadística & datos numéricos , Aprendizaje Automático , Adulto , Anciano , Anciano de 80 o más Años , Algoritmos , Árboles de Decisión , Femenino , Escala de Coma de Glasgow , Humanos , Puntaje de Gravedad del Traumatismo , Unidades de Cuidados Intensivos , Tiempo de Internación , Masculino , Persona de Mediana Edad , Alta del Paciente/estadística & datos numéricos , Curva ROC , Estudios Retrospectivos , Centros Traumatológicos
20.
Sports Health ; 13(1): 78-84, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-32822265

RESUMEN

CONTEXT: There is a renewed interest in diagnosing and treating subscapularis tears, but there is a paucity of clinical guidance to optimize diagnostic decision-making. OBJECTIVE: To perform a literature review to evaluate advanced maneuvers and special tests in the diagnosis of subscapularis tears and create a diagnostic algorithm for subscapularis pathology. DATA SOURCES: PubMed, MEDLINE, Ovid, and Cochrane Reviews databases. STUDY SELECTION: Inclusion criteria consisted of level 1 and 2 studies published in peer-reviewed scientific journals that focused on physical examination. STUDY DESIGN: Systematic review. LEVEL OF EVIDENCE: Level 2. DATA EXTRACTION: Individual test characteristics (bear hug, belly press, lift-off, Napoleon, and internal rotation lag sign) were combined in series and in parallel to maximize clinical sensitivity and specificity for any special test evaluated in at least 2 studies. A secondary analysis utilized subjective pretest probabilities to create a clinical decision tree algorithm and provide posttest probabilities. RESULTS: A total of 3174 studies were identified, and 5 studies met inclusion criteria. The special test combination of the bear hug and belly press demonstrated the highest positive likelihood ratio (18.29). Overall, 3 special test combinations in series demonstrated a significant impact on posttest probabilities. With parallel testing, the combination of bear hug and belly press had the highest sensitivity (84%) and lowest calculated negative likelihood ratio (0.21). CONCLUSION: The combined application of the bear hug and belly press physical examination maneuvers is an optimal combination for evaluating subscapularis pathology. Positive findings using this test combination in series with a likely pretest probability yield a 96% posttest probability; whereas, negative findings tested in parallel with an unlikely pretest probability yield a 12% posttest probability.


Asunto(s)
Examen Físico , Lesiones del Manguito de los Rotadores/diagnóstico , Algoritmos , Toma de Decisiones Clínicas , Árboles de Decisión , Medicina Basada en la Evidencia , Humanos , Sensibilidad y Especificidad
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA
...