1,100 research outputs found

    Multimodal Machine Learning for Automated ICD Coding

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    This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD-10 codes. Key evidence was also extracted to make our prediction more convincing and explainable. We used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.Comment: Machine Learning for Healthcare 201

    Domain-specific word embeddings for ICD-9-CM classification

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    In this work we evaluate domain-speci�c embedding models induced from textual resources in the medical domain. The International Classi�cation of Diseases (ICD) is a standard, broadly used classi�cation system, that codes a large number of speci�c diseases, symptoms, injuries and medical procedures into numerical classes. Assigning a code to a clinical case means classifying that case into one or more particular discrete class, hence allowing further statistics studies and automated calculations. The possibility to have a discrete code instead of a text in natural language is intuitively a great advantage for data processing systems. The use of such classi�cation is becoming increasingly important for, but not limited to, economic and policy-making purposes. Experiments show that domain-speci�c word embeddings, instead of a general one, improves classi�ers in terms of frequency similarities between words

    Classification of user queries according to a hierarchical medical procedure encoding system using an ensemble classifier

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    The Swiss classification of surgical interventions (CHOP) has to be used in daily practice by physicians to classify clinical procedures. Its purpose is to encode the delivered healthcare services for the sake of quality assurance and billing. For encoding a procedure, a code of a maximal of 6-digits has to be selected from the classification system, which is currently realized by a rule-based system composed of encoding experts and a manual search in the CHOP catalog. In this paper, we will investigate the possibility of automatic CHOP code generation based on a short query to enable automatic support of manual classification. The wide and deep hierarchy of CHOP and the differences between text used in queries and catalog descriptions are two apparent obstacles for training and deploying a learning-based algorithm. Because of these challenges, there is a need for an appropriate classification approach. We evaluate different strategies (multi-class non-terminal and per-node classifications) with different configurations so that a flexible modular solution with high accuracy and efficiency can be provided. The results clearly show that the per-node binary classification outperforms the non-terminal multi-class classification with an F1-micro measure between 92.6 and 94%. The hierarchical prediction based on per-node binary classifiers achieved a high exact match by the single code assignment on the 5-fold cross-validation. In conclusion, the hierarchical context from the CHOP encoding can be employed by both classifier training and representation learning. The hierarchical features have all shown improvement in the classification performances under different configurations, respectively: the stacked autoencoder and training examples aggregation using true path rules as well as the unified vocabulary space have largely increased the utility of hierarchical features. Additionally, the threshold adaption through Bayesian aggregation has largely increased the vertical reachability of the per node classification. All the trainable nodes can be triggered after the threshold adaption, while the F1 measures at code levels 3–6 have been increased from 6 to 89% after the threshold adaption

    Machine learning for medical coding in health care surveys

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    This exploratory study evaluates the use of machine learning classifiers to perform automated medical coding for large statistical healthcare surveys

    Extreme multi-label deep neural classification of Spanish health records according to the International Classification of Diseases

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    111 p.Este trabajo trata sobre la minería de textos clínicos, un campo del Procesamiento del Lenguaje Natural aplicado al dominio biomédico. El objetivo es automatizar la tarea de codificación médica. Los registros electrónicos de salud (EHR) son documentos que contienen información clínica sobre la salud de unpaciente. Los diagnósticos y procedimientos médicos plasmados en la Historia Clínica Electrónica están codificados con respecto a la Clasificación Internacional de Enfermedades (CIE). De hecho, la CIE es la base para identificar estadísticas de salud internacionales y el estándar para informar enfermedades y condiciones de salud. Desde la perspectiva del aprendizaje automático, el objetivo es resolver un problema extremo de clasificación de texto de múltiples etiquetas, ya que a cada registro de salud se le asignan múltiples códigos ICD de un conjunto de más de 70 000 términos de diagnóstico. Una cantidad importante de recursos se dedican a la codificación médica, una laboriosa tarea que actualmente se realiza de forma manual. Los EHR son narraciones extensas, y los codificadores médicos revisan los registros escritos por los médicos y asignan los códigos ICD correspondientes. Los textos son técnicos ya que los médicos emplean una jerga médica especializada, aunque rica en abreviaturas, acrónimos y errores ortográficos, ya que los médicos documentan los registros mientras realizan la práctica clínica real. Paraabordar la clasificación automática de registros de salud, investigamos y desarrollamos un conjunto de técnicas de clasificación de texto de aprendizaje profundo

    Mining and Integration of Structured and Unstructured Electronic Clinical Data for Dementia Detection

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    Dementia is an increasing problem for the aging population that incurs high medical costs, in part due to the lack of available treatment options. Accordingly, early detection is critical to potentially postpone symptoms and to prepare both healthcare providers and families for a patient\u27s management needs. Current detection methods are typically costly or unreliable, and could greatly benefit from improved recognition of early dementia markers. Identification of such markers may be possible through computational analysis of patients\u27 electronic clinical records. Prior work on has focused on structured data (e.g. test results), but these records often also contain natural language (text) data in the form of patient histories, visit summaries, or other notes, which may be valuable for disease prediction. This thesis has three main goals: to incorporate analysis of the aforementioned electronic medical texts into predictive models of dementia development, to explore the use of topic modeling as a form of interpretable dimensionality reduction to improve prediction and to characterize the texts, and to integrate these models with ones using structured data. This kind of computational modeling could be used in an automated screening system to identify and flag potentially problematic patients for assessment by clinicians. Results support the potential for unstructured clinical text data both as standalone predictors of dementia status when structured data are missing, and as complements to structured data

    Codificação médica ICD-9-CM automatizada de relatórios clínicos de pacientes diabéticos

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    The assignment of ICD-9-CM codes to patient’s clinical reports is a costly and wearing process manually done by medical personnel, estimated to cost about $25 billion per year in the United States. To develop a system that automates this process has been an ambition of researchers but is still an unsolved problem due to the inherent difficulties in processing unstructured clinical text. This problem is here formulated as a multi-label supervised learning one where the independent variable is the report’s text and the dependent the several assigned ICD-9-CM labels. Different variations of two neural network based models, the Bag-of-Tricks and the Convolutional Neural Network (CNN) are investigated. The models are trained on the diabetic patient subset of the freely available MIMIC-III dataset. The results show that a CNN with three parallel convolutional layers achieves F1 scores of 44.51% for five digit codes and 51.73% for three digit, rolled up, codes. Additionally, it is shown that joining several binary classifiers, with the binary relevance method, produces an improvement of almost 7% over its multi-labeling equivalent in a restricted classification task of only the eleven most common labels in the dataset.A atribuição de códigos ICD-9-CM a relatórios clínicos de pacientes é um processo dispendioso e cansativo, realizado por pessoal médico especializado e com um custo estimado de 25 mil milhões de dólares por ano nos Estados Unidos. É uma constante ambição de investigadores desenvolver um sistema que automatize esta atribuição. No entanto, o problema mantém se irresoluto dadas as dificuldades inerentes em processar texto clínico não estruturado. Este problema é aqui formulado como um de aprendizagem supervisionada multi-label em que a variável independente é o texto do relatório e a dependente os vários códigos ICD-9-CM atribuídos. São investigadas diferentes variações de dois modelos baseados em redes neurais, o Bag-of-Tricks e a Rede Neural Convolucional (RNC). Os modelos são treinados no subconjunto de pacientes diabéticos dos dados MIMIC-III. Os resultados mostram que uma RNC com três níveis convolucionais em paralelo obtém avaliações F1 de 44.51% para códigos de cinco dígitos e 51.73% para códigos abreviados de três dígitos. Além disto, é mostrado que a combinação de vários classificadores binários num só, com o método de relevância binária, produz uma melhoria de 7% em relação ao seu equivalente multi-label, num problema de classificação limitado aos onze códigos mais comuns nos dados.Mestrado em Engenharia de Computadores e Telemátic

    Deep Neural Networks for Multi-Label Text Classification: Application to Coding Electronic Medical Records

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    Coding Electronic Medical Records (EMRs) with diagnosis and procedure codes is an essential task for billing, secondary data analyses, and monitoring health trends. Both speed and accuracy of coding are critical. While coding errors could lead to more patient-side financial burden and misinterpretation of a patient’s well-being, timely coding is also needed to avoid backlogs and additional costs for the healthcare facility. Therefore, it is necessary to develop automated diagnosis and procedure code recommendation methods that can be used by professional medical coders. The main difficulty with developing automated EMR coding methods is the nature of the label space. The standardized vocabularies used for medical coding contain over 10 thousand codes. The label space is large, and the label distribution is extremely unbalanced - most codes occur very infrequently, with a few codes occurring several orders of magnitude more than others. A few codes never occur in training dataset at all. In this work, we present three methods to handle the large unbalanced label space. First, we study how to augment EMR training data with biomedical data (research articles indexed on PubMed) to improve the performance of standard neural networks for text classification. PubMed indexes more than 23 million citations. Many of the indexed articles contain relevant information about diagnosis and procedure codes. Therefore, we present a novel method of incorporating this unstructured data in PubMed using transfer learning. Second, we combine ideas from metric learning with recent advances in neural networks to form a novel neural architecture that better handles infrequent codes. And third, we present new methods to predict codes that have never appeared in the training dataset. Overall, our contributions constitute advances in neural multi-label text classification with potential consequences for improving EMR coding

    Use of statistical analysis, data mining, decision analysis and cost effectiveness analysis to analyze medical data : application to comparative effectiveness of lumpectomy and mastectomy for breast cancer.

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    Statistical models have been the first choice for comparative effectiveness in clinical research. Though effective, these models are limited when the data to be analyzed do not fit the assumed distributions; which is mostly the case when the study is not a clinical trial. In this project, data mining, decision analysis and cost effectiveness analysis methods were used to supplement statistical models in comparing lumpectomy to mastectomy for surgical treatment of breast cancer. Mastectomy has been the gold standard for breast cancer treatment for since the 1800s. In the 20th century, an equivalence of mastectomy and lumpectomy was established in terms of long-term survival and disease free survival. However, short term comparative effectiveness in post-operative outcomes has not been fully explored. Studies using administrative data are lacking and no study has used new technologies of self-expression, particularly the internet discussion board. In this study, data used were from the Nationwide Inpatient Sample (NIS) 2005, the Thomson Reuter\u27s MarketScan 2000 - 2001, the medical literature on clinical trials and online individuals\u27 posts in discussion boards on breastcancer.org. The NIS was used to compare lumpectomy to mastectomy in terms of hospital length of stay, total charges and in-hospital death at the time of surgery. MarketScan data was used to evaluate the comparative follow-up outcomes in terms of risk of repeat hospitalization, risk of repeat operation, number of outpatient services, number of prescribed medications, length of stay, and total charges per post-operative hospital admission on a period of eight months average. The MarketScan was also used to construct a simple post-operative hospital admission predictive model and to perform short-term cost-effectiveness analysis. The medical literature was used to analyze long term -10 years- mortality and recurrence for both treatments. The web postings were used to evaluate the comparative cost to improve quality of life in terms of patient satisfaction. In NIS and MarketScan data, International Classification of Disease, 9th revision, Clinical Modification (lCD-9-CM) diagnosis codes were used to extract cases of breast cancer; and ICD-9-CM procedure codes and Current Procedural Terminology, 4th edition procedure codes were used to form groups of treatment. Data were pre-processed and prepared for analysis using data mining techniques such as clustering, sampling and text mining. To clean the data for statistical models, some continuous variables were normalized using methods such as logarithmic transformation. Statistical models such as linear regression, generalized linear models, logistic and proportional hazard (Cox) regressions were used to compare post-operative outcomes of lumpectomy versus mastectomy. Neural networks, decision tree and logistic regression predictive modeling techniques were compared to create a simple predictive model predicting 90-day post-operative hospital re-admission. Cost and effectiveness were compared with the Incremental Cost Effectiveness Ratio (ICER). A simple method to process and analyze online po stings was created and used for patients\u27 input in the comparison of lumpectomy to mastectomy. All statistical analyses were performed in SAS 9.2. Data Mining was performed in SAS Enterprise Miner (EM) 6.1 and SAS Text Miner. Decision analysis and Cost Effectiveness Analysis were performed in TreeAge Pro 2011. A simple comparison of the two procedures using the NIS 2005, a discharge-level data, showed that in general, a lumpectomy surgery is associated with a significantly longer stay and more charges on average. From the MarketScan data, a person-level data where a patient can be followed longitudinally, it was found that for the initial hospitalization, patients who underwent mastectomy had a non-significant longer hospital stay and significantly lower charges. The post-operative number of outpatient services, prescribed medications as well as length of stay and charges for post-operative hospital admissions were not statistically significant. Using the MarketScan data, it was also found that the best model to predict 90-day post-operative hospital admission was logistic regression. A logistic regression revealed that the risk of a hospital re-admission within 90 days after surgery was 65% for a patient who underwent lumpectomy and 48% for a patient who underwent mastectomy. A cost effectiveness analysis using Markov models for up to 100 days after surgery showed that having lumpectomy saved hospital related costs every day with a minimum saving of 33onday10.Intermsoflong−termoutcomes,theuseofdecisionanalysismethodsontheliteraturereviewdatarevealedthat,10−yearsaftersurgery,739recurrencesand84deathswerepreventedamong10,000womenwhohadmastectomyinsteadoflumpectomy.Factoringpatients2˘7preferencesinthecomparisonofthetwoprocedures,itwasfoundthatpatientswhoundergolumpectomyarenon−significantlymoresatisfiedthantheirpeerswhoundergomastectomy.Intermsofcost,itwasfoundthatlumpectomysaves33 on day 10. In terms of long-term outcomes, the use of decision analysis methods on the literature review data revealed that, 10-years after surgery, 739 recurrences and 84 deaths were prevented among 10,000 women who had mastectomy instead of lumpectomy. Factoring patients\u27 preferences in the comparison of the two procedures, it was found that patients who undergo lumpectomy are non-significantly more satisfied than their peers who undergo mastectomy. In terms of cost, it was found that lumpectomy saves 517 for each satisfied individual in comparison to mastectomy. In conclusion, the current project showed how to use data mining, decision analysis and cost effectiveness methods to supplement statistical analysis when using real world nonclinical trial data for a more complete analysis. The application of this combination of methods on the comparative effectiveness of lumpectomy and mastectomy showed that in terms of cost and patients\u27 quality of life measured as satisfaction, lumpectomy was found to be the better choice

    Care episode retrieval: distributional semantic models for information retrieval in the clinical domain

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    Patients' health related information is stored in electronic health records (EHRs) by health service providers. These records include sequential documentation of care episodes in the form of clinical notes. EHRs are used throughout the health care sector by professionals, administrators and patients, primarily for clinical purposes, but also for secondary purposes such as decision support and research. The vast amounts of information in EHR systems complicate information management and increase the risk of information overload. Therefore, clinicians and researchers need new tools to manage the information stored in the EHRs. A common use case is, given a - possibly unfinished - care episode, to retrieve the most similar care episodes among the records. This paper presents several methods for information retrieval, focusing on care episode retrieval, based on textual similarity, where similarity is measured through domain-specific modelling of the distributional semantics of words. Models include variants of random indexing and the semantic neural network model word2vec. Two novel methods are introduced that utilize the ICD-10 codes attached to care episodes to better induce domain-specificity in the semantic model. We report on experimental evaluation of care episode retrieval that circumvents the lack of human judgements regarding episode relevance. Results suggest that several of the methods proposed outperform a state-of-the art search engine (Lucene) on the retrieval task
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