Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 10.170
Filtrar
1.
Eur Respir Rev ; 29(157)2020 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-33004526

RESUMO

Artificial intelligence (AI) is transforming healthcare delivery. The digital revolution in medicine and healthcare information is prompting a staggering growth of data intertwined with elements from many digital sources such as genomics, medical imaging and electronic health records. Such massive growth has sparked the development of an increasing number of AI-based applications that can be deployed in clinical practice. Pulmonary specialists who are familiar with the principles of AI and its applications will be empowered and prepared to seize future practice and research opportunities. The goal of this review is to provide pulmonary specialists and other readers with information pertinent to the use of AI in pulmonary medicine. First, we describe the concept of AI and some of the requisites of machine learning and deep learning. Next, we review some of the literature relevant to the use of computer vision in medical imaging, predictive modelling with machine learning, and the use of AI for battling the novel severe acute respiratory syndrome-coronavirus-2 pandemic. We close our review with a discussion of limitations and challenges pertaining to the further incorporation of AI into clinical pulmonary practice.


Assuntos
Algoritmos , Inteligência Artificial , Betacoronavirus , Infecções por Coronavirus/diagnóstico , Assistência à Saúde/métodos , Aprendizado de Máquina , Pneumonia Viral/diagnóstico , Pneumologia/métodos , Humanos , Pandemias
2.
Stud Health Technol Inform ; 273: 104-108, 2020 Sep 04.
Artigo em Inglês | MEDLINE | ID: mdl-33087598

RESUMO

Prediction of a labor due date is important especially for the pregnancies with high risk of complications where a special treatment is needed. This is especially valid in the countries with multilevel health care institutions like Russia. In Russia medical organizations are distributed into national, regional and municipal levels. Organizations of each level can provide treatment of different types and quality. For example, pregnancies with low risk of complications are routed to the municipal hospitals, moderate risk pregnancies are routed to the reginal and high risk of complications are routed to the hospitals of the national level. In the situation of resource deficiency especially on the national level it is necessary to plan admission date and a treatment team in advance to provide the best possible care. When pregnancy data is not standardized and semantically interoperable, data driven models. We have retrospectively analyzed electronic health records from the perinatal Center of the Almazov perinatal medical center in Saint-Petersburg, Russia. The dataset was exported from the medical information system. It consisted of structured and semi structured data with the total of 73115 lines for 12989 female patients. The proposed due date prediction data-driven model allows a high accuracy prediction to allow proper resource planning. The models are based on the real-world evidence and can be applied with limited amount of predictors.


Assuntos
Aprendizado de Máquina , História Reprodutiva , Registros Eletrônicos de Saúde , Feminino , Humanos , Gravidez , Estudos Retrospectivos , Federação Russa
3.
Stud Health Technol Inform ; 273: 109-114, 2020 Sep 04.
Artigo em Inglês | MEDLINE | ID: mdl-33087599

RESUMO

Timely identification of risk factors in the early stages of pregnancy, risk management and mitigation, prevention, adherence management can reduce the number of adverse perinatal outcomes and complications for both mother and a child. We have retrospectively analyzed electronic health records from the perinatal Center of the Almazov specialized medical center in Saint-Petersburg, Russia. Correlation analysis was performed using Pearson correlation coefficient to select the most relevant predictors. We used APGAR score as a metrics for the childbirth outcomes. Score of 5 and less was considered as a negative outcome. To analyze the influence of the unstructured anamnesis data on the prediction accuracy we have run two prediction experiments for every classification task: 1. Without unstructured data and 2. With unstructured data. This study presents implementation of predictive models for adverse childbirth events that provides higher precision than state of the art models. This is due to the use of unstructured medical data in addition to the structured dataset that allowed to reach 0.92 precision. Identification of main risk factors using the results of the features importance analysis can support clinicians in early identification of possible complications and planning and execution preventive measures.


Assuntos
Parto Obstétrico , Parto , Criança , Feminino , Humanos , Aprendizado de Máquina , Gravidez , Estudos Retrospectivos , Federação Russa
4.
Stud Health Technol Inform ; 273: 123-128, 2020 Sep 04.
Artigo em Inglês | MEDLINE | ID: mdl-33087601

RESUMO

Type 2 diabetes is one of the most common chronic diseases in the world. World Diabetes Federation experts predict that the diabetes patients' number by 2035 will increase by 205 million to reach 592 million. For health care, this diabetes type is one of the highest priority problems. This disease is associated with many concomitant diseases leading to early disability and high cardiovascular risk. A severity disease indicator is the degree of carbohydrate metabolism compensation. Decompensated and subcompensated carbohydrate metabolism patients have increased cardiovascular risks. Therefore, it is important to be able to select the right therapy to control carbohydrate metabolism. In this study, we propose a new method for selecting the optimal therapy automatically. The method includes creating personal optimal therapies. This kind of therapy has the highest probability of compensating carbohydrate metabolism for a patient within a six-month. The method includes models for predicting the results of different therapies. It is based on data from the previous medical history and current medical indicators of patients. This method provides high-quality predictions and medical recommendations. Therefore, medical professionals can use this method as part of the Support and Decision-Making Systems for working with T2DM patients.


Assuntos
Diabetes Mellitus Tipo 2 , Metabolismo dos Carboidratos , Análise Fatorial , Humanos , Aprendizado de Máquina
5.
Stud Health Technol Inform ; 273: 136-141, 2020 Sep 04.
Artigo em Inglês | MEDLINE | ID: mdl-33087603

RESUMO

Specific predictive models for diabetes polyneuropathy based on screening methods, for example Nerve conduction studies (NCS, can reach up to AUC 65.8 - 84.7 % for the conditional diagnosis of DPN in primary care. Prediction methods that utilize data from personal health records deal with large non-specific datasets with different prediction methods. Li et al. utilized 30 independent variables, which allowed to implement a model with AUC = 0.8863 for a Multilayer perceptron (MLP). Linear regression (LR) based methods produced up to AUC = 0.8 %. This way, modern data mining and computational methods can be effectively adopted in clinical medicine to derive models that use patient-specific information to predict the development of diabetic polyneuropathy, however, there still is a space to improve the efficiency of the predictive models. The goal of this study is the implementation of machine learning methods for early risk identification of diabetes polyneuropathy based on structured electronic medical records. It was demonstrated that the machine learning methods allow to achieve up to 0.7982 precision, 0.8152 recall, 0.8064 f1-score, 0.8261 accuracy, and 0.8988 AUC using the neural network classifier.


Assuntos
Diabetes Mellitus , Aprendizado de Máquina , Humanos , Redes Neurais de Computação , Medição de Risco , Fatores de Risco
6.
Stud Health Technol Inform ; 273: 155-160, 2020 Sep 04.
Artigo em Inglês | MEDLINE | ID: mdl-33087606

RESUMO

Human Activity Recognition (HAR) is becoming a significant issue in modern times and directly impact the field of mobile health. Therefore, it is essential the designing of systems which are capable of recognizing properly the activities conducted by the individuals. In this work, we developed a system using the Internet of Things (IoT) and machine learning technologies in order to monitor and assist individuals in their daily life. We compared the data collected using a mobile application and a wearable device with built-in sensors (accelerometer and gyroscope) with the data of a publicly available dataset. By this way, we were able to validate our results and also investigate the functionality and applicability of the wearable device that we choose for the Human Activity Recognition problem. The classification results for the different types of activities presented using our dataset (99%) outperforms the results from the publicly database (97%).


Assuntos
Aplicativos Móveis , Dispositivos Eletrônicos Vestíveis , Atividades Humanas , Humanos , Aprendizado de Máquina , Reconhecimento Psicológico
7.
Stud Health Technol Inform ; 273: 223-227, 2020 Sep 04.
Artigo em Inglês | MEDLINE | ID: mdl-33087616

RESUMO

The current pandemic can likely have several waves and will require a major effort to save lives and provide optimal treatment. The efficient clinical resource planning and efficient treatment require identification of risk groups and specific clinical features of the patients. In this study we develop analyze mortality for COVID19 patients in Russia. We identify comorbidities and risk factors for different groups of patients including cardiovascular diseases and therapy. In the study we used a Russian national COVID registry, that provides sophisticated information about all the COVID-19 patients in Russia. To analyze Features importance for the mortality we have calculated Shapley values for the "mortality" class and ANN hidden layer coefficients for patient lifetime. We calculated the distribution of days spent in hospital before death to show how many days a patient occupies a bed depending on the age and the severity of the disease to allow optimal resource planning and enable age-based risk assessment. Predictors of the days spent in hospital were calculated using Pearson correlation coefficient. Decisions trees were developed to classify the patients into the groups and reveal the lethality factors.


Assuntos
Infecções por Coronavirus , Aprendizado de Máquina , Pandemias , Pneumonia Viral , Betacoronavirus , Humanos , Federação Russa , Análise de Sobrevida
8.
Stud Health Technol Inform ; 273: 266-271, 2020 Sep 04.
Artigo em Inglês | MEDLINE | ID: mdl-33087625

RESUMO

Human Activity Recognition (HAR) is an arisen research topic because of its usage of self-care and prevention issues. In our days, the advances of technology (smart-phones, smart-watches, tablets, wristbands) and achievements of Machine Learning provide great opportunities for in-depth research on HAR. Technological gadgets include many sensors that gather various, which in turn are input to machine learning techniques to derive useful information and results about human activities and health conditions. Activity Recognition is mainly based physical sensors attached to the human body, with wearable devices coming with built-in sensors such as the accelerometer, gyroscope. This work presents a system based on the Internet of Things (IoT), that monitoring essential vital signals. A mobile application has designed and developed to collect data from a wearable device with built-in sensors (accelerometer and gyroscope) for different human activities and store them for use in a database. The purpose of this work is to present the module of the system that is responsible for the data acquisition, processing and storage of signals that will feed then the Machine Learning module to identify the human health status.


Assuntos
Aplicativos Móveis , Dispositivos Eletrônicos Vestíveis , Assistência à Saúde , Corpo Humano , Humanos , Aprendizado de Máquina
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3917-3920, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018857

RESUMO

Frailty in old age is defined as the individual intrinsic susceptibility of having bad outcomes following a health problem. It relies on sarcopenia, mobility and activity. Recognizing and monitoring a range of physical activities is a necessary step which precedes the analysis of this syndrome. This paper investigates the optimal tools for this recognition in terms of type and placement of wearable sensors. Two machine learning procedures are proposed and compared on a public dataset. The first one is based on deep learning, where feature extraction is done manually, by constructing activity images from raw signals and applying convolutional neural networks to learn optimal features from these images. The second one is based on shallow learning, where hundreds of handcrafted features are extracted manually, followed by a novel feature selection approach to retain the most discriminant subset.Clinical relevance- This analysis is an indispensable prerequisite to develop efficacious way in order to identify people with frailty using sensors and moreover, to take on the challenge of frailty prevention, an actual world health organization priority.


Assuntos
Fragilidade , Algoritmos , Exercício Físico , Fragilidade/diagnóstico , Humanos , Aprendizado de Máquina , Redes Neurais de Computação
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4252-4255, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018935

RESUMO

Medication adherence is a critical component and implicit assumption of the patient life cycle that is often violated, incurring financial and medical costs to both patients and the medical system at large. As obstacles to medication adherence are complex and varied, approaches to overcome them must themselves be multifaceted.This paper demonstrates one such approach using sensor data recorded by an Apple Watch to detect low counts of pill medication in standard prescription bottles. We use distributed computing on a cloud-based platform to efficiently process large volumes of high-frequency data and train a Gradient Boosted Tree machine learning model. Our final model yielded average cross-validated accuracy and F1 scores of 80.27% and 80.22%, respectively.We conclude this paper with two use cases in which wearable devices such as the Apple Watch can contribute to efforts to improve patient medication adherence.


Assuntos
Aprendizado de Máquina , Dispositivos Eletrônicos Vestíveis , Humanos , Adesão à Medicação
11.
Hum Genomics ; 14(1): 36, 2020 10 09.
Artigo em Inglês | MEDLINE | ID: mdl-33036646

RESUMO

INTRODUCTION: The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection. METHODS: We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age-matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification. RESULTS: We found that the XGBoost classifier could differentiate between the two classes at a significant level (p = 2 · 10-11) as measured against a randomized control and (p = 3 · 10-14) as measured against the expected value of a random guessing algorithm (AUC = 0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test. CONCLUSION: Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity.


Assuntos
Algoritmos , Betacoronavirus/isolamento & purificação , Aberrações Cromossômicas , Cromossomos Humanos/genética , Infecções por Coronavirus/diagnóstico , Aprendizado de Máquina , Pneumonia Viral/diagnóstico , Índice de Gravidade de Doença , Betacoronavirus/genética , Estudos de Casos e Controles , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/genética , Infecções por Coronavirus/virologia , Conjuntos de Dados como Assunto , Humanos , Pandemias , Pneumonia Viral/epidemiologia , Pneumonia Viral/genética , Pneumonia Viral/virologia , Fatores de Risco
12.
BMC Bioinformatics ; 21(Suppl 14): 364, 2020 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-32998700

RESUMO

BACKGROUND: Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients' primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine. RESULTS: We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study's limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis. CONCLUSIONS: Primary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.


Assuntos
Antineoplásicos/farmacologia , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Aprendizado de Máquina , Antineoplásicos/uso terapêutico , Área Sob a Curva , Análise por Conglomerados , Bases de Dados Genéticas , Desoxicitidina/análogos & derivados , Desoxicitidina/farmacologia , Desoxicitidina/uso terapêutico , Fluoruracila/uso terapêutico , Humanos , Neoplasias/tratamento farmacológico , Curva ROC
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 576-579, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018054

RESUMO

The advancement in bioelectrical measurement technologies and the push towards a higher impact of the Brain Computer Interfaces and Affective Computing in the daily life have made non-invasive and low-priced devices available to the large population to record physiological states. The aim of this study is the assessment of the abilities of the MUSE headband, together with the Shimmer GSR+ device, to assess the emotional state of people during stimuli exposure. Twenty-four pictures from the IAPS database were showed to 54 subjects and were evaluated in their emotional values by means of the Self-Assessment Manikin (SAM). Using a Machine Learning approach, fifty-two scalar features were extracted from the signals and used to train 6 binary classifiers to predict the valence and arousal elicited by each stimulus. In all classifiers we obtained accuracies ranging from 53.6% to 69.9%, confirming that these devices are able to give information about the emotional state.


Assuntos
Interfaces Cérebro-Computador , Dispositivos Eletrônicos Vestíveis , Nível de Alerta , Emoções , Humanos , Aprendizado de Máquina
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 722-727, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018089

RESUMO

Electromyography offers a way to interface an amputee's resilient muscles to control a bionic prosthesis. While myoelectric prostheses are promising, user acceptance of these devices remain low due to a lack of intuitiveness and ease-of-use. Using a low-cost wearable flexible electrodes array, the proposed system leverages high-density surface electromyography (HD-EMG) and deep learning techniques to classify forearm muscle contractions. These techniques allow for increased intuitiveness and ease-of-use of a myoelectric control scheme with a single easy-to-install electrodes apparatus. This paper proposes a flexible electrodes array construction using standard printed circuit board manufacturing processes for low-cost and quick design-to-production cycles. HD-EMG dataset visualization with t-distributed Stochastic Neighbor Embedding (t-SNE) is introduced, and offline classification results of the wearable gesture recognition system for hand prosthesis control are validated on a group of 8 able-bodied subjects. Using a majority vote on 5 successive inferences, a median recognition accuracy of 98.61 % was obtained across the group for an 8 gestures set. For a 6 gestures set containing commonly used prosthesis positions, the median accuracy reached 99.57 % with the majority vote.


Assuntos
Visualização de Dados , Antebraço , Eletromiografia , Mãos , Humanos , Aprendizado de Máquina
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 910-913, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018132

RESUMO

Arterial pressure (AP) is a crucial biomarker for cardiovascular disease prevention and management. Photoplethysmography (PPG) could provide a novel, paradigm-shifting approach for continuous, non-obtrusive AP monitoring, comfortably integrated in wearable and mobile devices; yet, it still faces challenges in accuracy and robustness. In this work, we sought to integrate machine learning (ML) techniques into a previously established, clinically-validated classical approach (oBPM®) to develop new accurate AP estimation tools based on PPG, and at the same time improve our understanding of the underlying physiological parameters. In this novel approach, oBPM® was used to pre-process PPG signals and robustly extract physiological features, and ML models were trained on these features to estimate systolic AP (SAP). A feature relevance analysis showed that reference (calibration) information, followed by various morphological parameters of the PPG pulse wave, comprised the most important features for SAP estimation. A performance analysis then revealed that LASSO-regularized linear regression, Gaussian process regression and support vector regression are effective for SAP estimation, particularly when operating on reduced feature sets previously obtained with e.g. LASSO. These approaches yielded substantial reductions in error standard deviation of 9-15% relative to conventional oBPM®. Altogether, these results indicate that ML approaches are well-suited, and promising tools to help overcoming the challenges of ubiquitous AP monitoring.


Assuntos
Determinação da Pressão Arterial , Fotopletismografia , Pressão Arterial , Pressão Sanguínea , Humanos , Aprendizado de Máquina
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4269-4272, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018939

RESUMO

This paper proposes cuffless and continuous blood pressure estimation utilising Photoplethysmography (PPG) signals and state of the art recurrent network models, namely, Long Short Term Memory and Gated Recurrent Units. The models were validated on wide range of varying blood pressure and PPG signals acquired from the Multiparameter Intelligent Monitoring in Intensive Care database. Many features were extracted from the PPG waveform and several machine learning techniques were employed in an attempt to eliminate collinearity and reduce the size of input feature vector. Consequently, the most effective features for blood pressure estimation were selected. Experimental results show that the accuracy of the proposed methods outperform traditional models applied in the literature. The results satisfy the American National Standards of the Association for the Advancement of Medical Instrumentation.


Assuntos
Determinação da Pressão Arterial , Fotopletismografia , Animais , Pressão Sanguínea , Aprendizado de Máquina , Redes Neurais de Computação
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4330-4336, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018954

RESUMO

After a stroke, individuals often exhibit upper extremity (UE) motor dysfunction, influencing the performance of everyday tasks. Characterizing UE movements is useful to track recovery and response to intervention. Yet, due to the complexity of the recovery process, UE movements may be extremely variable and person-specific. While this renders automatic recognition of these gestures challenging, machine learning methods could be used to classify UE movements in atypical populations. In the current study, we utilize data from 20 individuals post-stroke and 20 age-matched controls to identify an optimal set of sensor-extracted features for the classification of unimanual and bimanual gestures during task performance. We found that using fewer than 100 features along with a random forest classifier produced the best performance across both groups, with both user-dependent and user-independent models.


Assuntos
Reabilitação do Acidente Vascular Cerebral , Acidente Vascular Cerebral , Humanos , Aprendizado de Máquina , Movimento , Extremidade Superior
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4827-4830, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019071

RESUMO

Biomechanical movement data are highly correlated multivariate time-series for which a variety of machine learning and deep neural network classification techniques are possible. For image classification, convolutional neural networks have reshaped the field, but have been challenging to apply to 3D movement data with its intrinsic multidimensional nonlinear correlations. Deep neural networks afford the opportunity to reduce feature engineering effort, remove model-based approximations that can introduce systematic errors, and reduce the manual data processing burden which is often a bottleneck in biomechanical data acquisition. What classification techniques are most appropriate for biomechanical movement data? Baseline performance for 3D joint centre trajectory classification using a number of traditional machine learning techniques are presented. Our framework and dataset support a robust comparison between classifier architectures over 416 athletes (professional, college, and amateur) from five primary and six non-primary sports performing thirteen non-sport-specific movements. A variety of deep neural networks specifically intended for time-series data are currently being evaluated.


Assuntos
Redes Neurais de Computação , Esportes , Aprendizado de Máquina , Movimento (Física) , Movimento
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4986-4991, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019106

RESUMO

Sepsis is a life-threatening clinical syndrome and one of the most expensive conditions treated in hospitals. It is challenging to detect due to the nonspecific clinical signs and the absence of gold standard diagnostics. However, early recognition of sepsis and optimal treatments for sepsis are of paramount importance to improve the condition's management and patient outcomes. This paper aims to delineate key aspects of current sepsis detection systems, including their dependency on clinical expert and laboratory biometric features requiring ongoing critical care intervention, the efficacy of vital sign measures, and the effect of the study population with respect to the precision of sepsis prediction. The AUROC performances of XGBoost models trained on a heterogenous ICU patient group (n=3932) showed significant degradations (p<0.05) as the expert and laboratory biomarker features are removed systematically and vital sign features taken in ICU settings are left. The performance of XGBoost models trained only with vital sign features on a more homogeneous group of ICU patients (n=1927) had a significantly (P<0.05) improved AUPRC to moderate level. The presented results highlight the importance of making a practical machine learning system for sepsis prediction by considering the availability of dominant features as well as personalizing sepsis prediction by configuring it to the specific demographics of a targeted population.


Assuntos
Aprendizado de Máquina , Sepse , Cuidados Críticos , Humanos , Sepse/diagnóstico
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5292-5295, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019178

RESUMO

Clinical text classification is an indispensable and extensively studied problem in medical text processing. Existing research primarily employs machine learning and pattern based approaches to address the stated problem. In general, pattern based approaches perform better than other methods. However, these approaches commonly require human intervention for pattern identification, which diminish their benefits and restrain their applications. In this study, we present a novel pattern extraction algorithm, which identifies and extracts patterns from clinical textual resources, automatically. The algorithm identifies the candidate concepts in the clinical text, finds the context of the concepts by discovering their context windows, and finally transforms each context window to a pattern. We evaluate our proposed algorithm on Hypertension, Rhinosinusitis, and Asthma guidelines. 70% of the hypertension guideline was used for pattern extraction while the remaining 30% and the other two guidelines were used for evaluations. The algorithm extracts 21 patterns that classify Hypertension, Rhinosinusitis, and Asthma guidelines sentences to the recommendation and non-recommendation sentences with 84.53%, 80.03%, and 84.62% accuracy, respectively. The initial results reveal the benefits and applicability of the algorithm for clinical text classification.


Assuntos
Algoritmos , Aprendizado de Máquina , Humanos , Idioma
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA