In addition to the commonly used max-pooling for CNN, another pooling method is the k-max pooling CNN [6], in which for each feature map obtained from the convolution of a filter wwith the sequence matrix x, instead of selecting a single maximum value, it selects K consecutive maximum values. Geneva: World Health Organization. the patient had the condition(s) upon arrival. Introduced in the late 1970s, the ICD-9 code set was replaced by the more detailedICD-10code set on October 1, 2015. They are the precision, recall, and F1-score, and they are calculated in the following way: where tpdenotes true positives, fpdenotes false positives, and fndenotes false negatives. At the same time, it is very necessary for hospitals and doctors to provide high-quality medical healthcare data and the high-quality EHR data is equally important as the medical services provided to patients. Centers for Medicare & Medicaid Services. On the other hand, if the value is 0, the corresponding element in the old state cell C(t 1) is forbidden, and if the value is between 0 and 1, then only a portion of the corresponding element in the old state cell C(t 1) is kept. The optimized hyperparameter values of the CNN are: epoch size is 40, batch size is 20, dropout probability is 0.5, the L2-norm is used for regularization, and the dimension size of word embeddings is 200. If the reset gate is set to be all 1s, and update gate is set to be all 0s, the GRU is reduced to be the original RNN model. The clinic profile is very useful for all applications about the EHR analysis with machine learning. The International Classification of Functioning, Disability, and Health, commonly known as ICF, is a framework for measuring health and disability related to a health condition. These also change over time, as there was a revision in October 2017. When we use machine learning for document categorization, documents first need to be tokenized into individual words or tokens. Hospital services are categorized based on a diagnosis, type of treatment, and other criteria for billing purposes. They translate medical information found in the patient's record into medical codes. We are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including Nobel Prize winners and some of the worlds most-cited researchers. The CNN architecture contains the layers of automatic feature extraction, feature selection, and the pattern classification. For NLP, both syntactic and semantic information can be encoded by LSTM networks for prediction and classification purposes. In this paper, we develop natural language processing (NLP), deep learning, and machine learning algorithms to automatically categorize each patients individual diseases into the ICD-10 standard. This means that hospitals are paid a fixed rate for inpatient services corresponding to the DRG assigned to a given patient, regardless of what the real cost of the hospital stay was, or what the hospital bills the insurance company (or Medicare) for. For multi-class classification, the number of neurons of the output is equal to the number of classes to be predicted. [3]. Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision (DSM-IV-TR). This assumption is of course not 100% correct, but it greatly helps speed up the training process. Which of the following is a standardized vocabulary of clinical terminology used for clinical documentation and reporting? In the original softmax algorithm, as shown in formula (1), when updating one words vector during the training process, all words vectors should be involved in the calculation and the computational complexity is accordingly as high as O(N). The final step in the CNN architecture is a full connection layer including the dropout strategy and regularization, from the final feature vector to the output layer. The embeddings can be either from word2Vec, one-hot representation and/or other vector representations of words, forming different channels of the representation of text data. It does not reflect the updated codes for reporting diagnoses or in-patient hospital procedures. The automatic categorization is very desirable for the massive EHR data sets. Finally, the pooling operation continues to take the most important feature value or a few most important feature values from the feature map as the output of the filters convolution result with the sentence. We find that the RMSprop optimizer works best for our data set. In the first layer, it only classifies disease symptoms into 26 coarse disease categories, but, in the second layer, it can classify disease symptoms into more than 500 disease categories, and in the third layer, it can accomplish the categorization for about 21,000 diseases. This research consists of 4 components for the categorization of EHRs: problem definition and data preparation and collection from EHR, text data extraction from the prepared and collected data, the tokenization of the Chinese documents using NLP, and supervised deep learning algorithms with embedded vector representations for tokens/words as inputs to the neural network architectures for the semantic categorization of each patients disease symptom description into ICD-10 standard. The classification results of the 14-class SVM classifier are displayed in Tables 1 and 2 , with respect to the regularity of L1, and L2, respectively. There are two different models in the word2Vec algorithm and they are the continuous bag-of-words (CBOW) model and the skip-gram model as shown in Figures 1 and 2 , respectively. For text classification, the main idea of CNN using different sizes of filters is pretty much like that for computer vision. It is a chain of repeating modules, each of which is a modified version of that in RNNs. The typical NLP applications of using RNN are text classification, sentence generation, and language translation. For the 26 disease categories in the first-layer of ICD-10, our EHR system only has sufficient disease examples for 14 popular disease categories, but we do not have sufficient disease examples for the rest 12 unpopular disease categories. So, we first tokenize each Chinese document into a collection of terms. Then the result of the filtered cell state is multiplied with the result of the sigmoid layer in an elementwise way to get the output of the current module as well as the hidden state h(t) for the next module at time t+1. The diagnostic-related group (DRG) system categorizes different medical codes. The squeezed value is generated as a feature value, mathematically represented in the following formula: where activationdenotes the activation function such as Relu(), the dot operation between matrix wand xdenotes the element-wise multiplication operation, and the subscript index iis the position index of the filter. The experimental results are presented in Section 3, and the conclusions of the paper are made in Section 4 together with some discussions and the direction of future work. This is a system that is used to code ancillary services and procedures. International Classification of Functioning, Disability and Health (ICF). Experiments of comprehensive studies show that the CNN algorithm outperforms the other deep learning algorithms, and it generates much better results than the traditional machine learning algorithms for the same data set according to the quantitative metric of F1-score. Home > Books > Recent Trends in Computational Intelligence, Submitted: May 26th, 2019 Reviewed: January 21st, 2020 Published: February 28th, 2020, Edited by Ali Sadollah and Tilendra Shishir Sinha, Total Chapter Downloads on intechopen.com. The RNN structure with courtesy of Colah [7]. LSTMs are explicitly designed to avoid the vanishing gradient problem in learning the long-term dependencies in the data and they have achieved great success in sentiment analysis, text classification, and language translation. American Dental Association. By Ujwalla Gawande, Kamal Hajari and Yogesh Golhar. Which of the following is NOT considered a classification system? When all filters are applied for convolution with the sentences matrix, we obtain a feature vector for the input sentence. At each position, the filter covers a few (m) rows of the words vector matrix, and the element-wise multiplication of the filter with the covered matrix is taken, and the multiplication results are summed up. Furthermore, to improve the vector quality of low-frequency words, the high-frequency words are down-sampled and the low-frequency words are up-sampled by using a method called frequency lifting. The number of codes available in ICD-10 is much greater than in ICD-9. Healthcare Common Procedure Coding System (HCPCS) codes are used by Medicare and are based on CPT codes. Text classification using the CNN architecture with courtesy of Kim [4]. Sometimes, more than one sleeve is needed per record. Arlington, VA: American Psychiatric Association. They are published and maintained by the American Psychiatric Association. The architecture of gated recurrent units (GRU) [9] is a simplified version of the LSTM architecture with only two gates, a reset gate r and an update gate z. What was the main reason the disease classification system was originally developed? There are about 500 different DRGs. Patients who use Medicare, especially those who have needed ambulance services or other devices outside of the doctor's office, may want to learn more about HCPCS codes. In this work, we use the Han LP, an open source tokenization tool for tokenizing Chinese documents. Its based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. Firstly, by using domain knowledge of medical informatics and techniques of information fusion, we construct structured & meaningful patients clinic profiles from the scattered and heterogeneous medical records such as inpatient and outpatient records, lab tests, treatment plans, and doctors prescriptions for medications in the EHRs. How to leverage insights into big electronic health records (EHRs) becomes increasingly important for accomplishing precision medicine to improve the quality of human healthcare. How? Thank you, {{form.email}}, for signing up. ICD-10. We Now Live in a Post-Roe World. Medicare assignment: What it is and how it works, Phone and Online Service Codes on Your Medical Bill, Learn How to Look up Medicare HCPCS Codes for Free, Health Plans Often Require Pre-Approval for Various Medical Procedures, International Classification of Functioning, Disability and Health (ICF), Hospital acute inpatient services payment system. The results of CNN, LSTM and GRU algorithms are displayed in Table 3 , from which we can see that the CNN algorithm with max-pooling works best. If a value is 1, the corresponding information in the old state cell C(t 1) is completely kept. The clustering characteristics of embedded vector representations of semantically similar words in the dense vector space. It is for the purpose of extracting the semantic features (patterns) of different N-Grams characterized by the filters. to address the issue, the HIM director needs to focus her attention on which of the following reports? Learn About Insurance Codes to Avoid Billing Errors, Tips for Reading and Really Understanding Your Medical Bills, Reading Your Healthcare Provider's Medical Services Receipt, Asking for a Network Gap Exception When No In-Network Providers Nearby, HIPAA Provides Important Health and Private Information Protections, Tips for Deciphering Your Explanation of Benefits, You Pay More When Your Healthcare Provider Picks the Wrong Diagnosis Code. The similar sub-structures of the embedded vectors in the dense vector space between semantically meaningful country-capital word pairs with the courtesy of Mikolov etc. Verywell Health uses only high-quality sources, including peer-reviewed studies, to support the facts within our articles. For a larger layer number in the tree, it categorizes diseases into finer disease categories. Both traditional one-hot vector representation and BOW-based TF-IDF or binary feature representation for words have a lot of limitations for document classification. Clinical encoding software was adopted to: An inpatient Prospective Payment System requires the following as a foundation for determining the hospital payment: New Hope hospital has created a new coding position that requires the coder to start coding the patient's medical record while the patient is still receiving treatment. It is a set of procedural codes for oral health and related services. For deep learning algorithms, each document is the input of the deep learning architecture with individual tokens represented as word embeddings. It uses death certificates and hospital records to count deaths, as well as injuries and symptoms. We rely on the most current and reputable sources, which are cited in the text and listed at the bottom of each article. American Medical Association. This sum is then fed into the nonlinear rectified linear unit (Relu) activation function with an added biased term bR. Which of the following is NOT a responsible party for maintaining ICD-9-CM? Patients can use medical codes to learn more about their diagnosis, the services their practitioner has provided, figure out how much their providers were paid, or even to double-check their billing from either their providers or their insurance or payer. As shown in Figure 6 , the chain-link RNN architecture consists of a sequence of neural network modules sharing the same parameters. The constructed clinic profiles make it feasible for us to generate actionable intelligence from the unstructured EHR raw data sets using machine learning, NLP and artificial intelligence algorithms (AI). In EHR analysis, there are a lot of applications that need to categorize each patients disease into the corresponding category with respect to a medical coding standard. Individual images stored in 4 6 inch plastic sleeves, which contain multiple rows per page. For SVM, we use two kinds of vector representations for document representation: the BOW vectors and the embedded vectors. The tokenization of Chinese documents is very different from that of English, which can be accomplished through the delimiters between terms. RNNs are very efficient for modeling the time series data for prediction and forecasting tasks. HCPCS - General Information. The CNN model also outperforms the popular traditional machine learning model SVM for the same data set. By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers. The prediction results of 4 deep learning algorithms. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2022 Dotdash Media, Inc. All rights reserved. Apply relationship-building values and the principles of team dynamics to perform effectively in different team roles to plan, deliver, and evaluate patient/population-centered care and population health programs and policies that are safe, timely, efficient, effective, and equitable. The filtered cell state through a tanh activation function determines the polarity and proportion for each element in the current cell state C(t) for updating the result of the sigmoid layer. In this paper, we use the quantitative metrics to measure the performance of the models. In the Huffman tree, the high-frequency words have short paths from the root of the tree to the individual leaf nodes, and the low-frequency words have long paths, thus accomplishing optimal encoding performance from the viewpoint of bit-rate. This diagnostic classification system is the international standard for reporting diseases and health conditions. Since the training process for both LSTM and GRU is very time consuming for the use of 6000 training and validation examples for each disease category, for computational efficiency, we shorten each disease description to be 700 words. For text classification with RNN, a sentence is usually encoded into a single fixed-length feature vector for classification, while for sentence generation and language translation, the typical tasks are about predicting the next word given the context words seen so far together with what are generated or translated. She has written several books about patient advocacy and how to best navigate the healthcare system. With the embedded representation from Word2Vec, each word in the corpus can be represented by a unique low-dimensional dense vector. Which of the following best describes a clinical coder's responsibilities? By comparing a module at time tin Figures 6 and 7 , respectively, we can clearly observe that the LSTM architecture has some additional components in each module and these components are called gates with different functionalities to work together so that the long-term dependencies of words in the input sequence can be used for learning and the gradient vanishing problem can be solved. The CNN is one of the popular deep neural network algorithms for both NLP and computer vision applications. The BOW vectors can be further separated into TF-IDF weighted vectors and binary vectors. HeadquartersIntechOpen Limited5 Princes Gate Court,London, SW7 2QJ,UNITED KINGDOM, Recent Trends in Computational Intelligence. The sigmoid layer is used to determine how much information in the inputs can be added to the current cell state C(t), and the tanh layer creates a new vector of values to determine the polarity and proportion for the output of the sigmoid layer to be added to the current cell state C(t). James received a Master of Library Science degree from Dominican University. The ICD-10 coding system is essentially a tree-like hierarchical structure with 3 layers to encode patients diseases. This makes it possible for us to leverage the insights from the big data with artificial intelligence (AI) algorithms. In relation to EHR's, nomenclature is best described as? Mary is a medical coder for an outpatient bariatric center and typically codes gastrointestinal diseases and disorders, as well as procedures. They are submitted to insurance, Medicare, or other payers for reimbursement purposes. The word2Vec algorithm [3] is a kind of distributed representation learning algorithm for the language model to capture each words semantic information through the embedded dense vectors so that semantically similar words can be inferred from each other. 2020 The Author(s). As a result, by using the Huffman tree based hierarchical softmax algorithm, we can significantly reduce the computational complexity from O(N) to O(logN). Verywell Health content is rigorously reviewed by a team of qualified and experienced fact checkers. For the research of Chinese medical healthcare data analysis, we have obtained the Chinese EHRs from 10 Chinese hospitals in Shandong Province, China. Contact our London head office or media team here. Our website is not intended to be a substitute for professional medical advice, diagnosis, or treatment. The third segment identifies the package size and type. For computer vision applications, the R, G, B colors of an image are usually used as CNNs inputs. The embedded vectors can be separated into two forms: the averaged word embeddings of the pre-trained word embeddings from word2Vec to represent a document, and the doc2Vec vectors from the PV-DM model [11], respectively. The National Drug Code (NDC), is a unique, numeric identifier given to medications. The Word2vec model can be trained in two different ways: using the hierarchical softmax algorithm or using the negative sampling method. For the randomly initialized words vectors, they are iteratively updated in the back-propagation process during the training stage until the training process is done, and this is another way of representation learning. The number of valid records with non-empty symptom description in the EHR is significantly reduced after all preprocessing steps. Each jacket typically stores one patient record. It has been found out that the analysis of the big EHR can help accomplish precision medicine for patients to improve the quality of human healthcare. Compared with LSTM, GRU has fewer parameters and thus may train the deep learning model in a faster way. In this paper, we develop NLP and deep learning algorithms to categorize patients diseases according to the ICD-10 coding standard. In this paper, we can only annotate disease examples for the 14 popular disease categories to train a 14-class classifier. Our team is growing all the time, so were always on the lookout for smart people who want to help us reshape the world of scientific publishing. American Psychiatric Association. For a sentence, the CBOW model is used to predict the current word from its left side and right side context words, which are within a window centered at the current word. During the last quarter, New Hope hospital ended up with a number of records that were not completely coded until 6 days after the patient was discharged, mostly due to missing pathology reports. For feature extraction, the matrix is convolved with some filters of different sizes, respectively. This is for the fact that as the number of classes increases for supervised machine learning, the required annotated training data increases significantly, however we do not have so many patient records now. We select the CBOW with the negative sampling to get the pre-trained word embeddings, and the feature vector of the SVM classifier is obtained as discussed above. When optimizing the word2Vec to get the pre-trained word embeddings, we have tried 4 models and they are the Skip-gram with the hierarchical softmax, Skip-gram with the negative sampling, CBOW with the hierarchical softmax, and CBOW with the negative sampling. CPT coding manual is updated: A) Navigate through the coding pathways, assign appropriate codes, and result in DRG assignment. The first segment identifies the product labeler (manufacturer, marketer, repackager, or distributor of the product). As shown in Figure 8 , the reset gate determines how to combine the input x(t) with the previous memory h(t 1), and the update gate defines how much of the previous memory can be kept. Secondly, we extract each patients historical disease descriptions in the clinic profiles and take each of them as a document for categorization. The words vector representations can be either from the pre-trained word embeddings, for example, the word2Vec embeddings, or randomly initialized. Within the CNN architecture, each channel of the texts is represented as a matrix, in which, the rows of the matrix represent the sequence of words according to their order, and each row is a words vector representation with the number of columns being the dimension size of the embedded vector space. For each filter size, we use 100 convolutional filters to extract features. As demonstrated in Figure 5 , different channels of the data can be used as inputs of the CNN architecture for feature extraction and feature selection via convolutional operations together with the pooling process and nonlinear activation. The prediction results of 14-class SVM classifier with L1 regularity. Current Procedural Terminology (CPT) codes are developed by the American Medical Association to describe every type of service (i.e., tests, surgeries, evaluations, and any other medical procedures) a healthcare provider provides to a patient. This helps because otherwise the high-frequency words are more likely to be sampled than the low-frequency words for updating their vectors. The third gate is the output gate which includes the filtered cell state C(t) and a sigmoid layer. After some preprocessing for the tokens, we then represent each word with the pre-trained embedded vector representation with the language model of distributed representation learning algorithm word2Vec [3]. Trisha Torrey is a patient empowerment and advocacy consultant. It has attracted great attention in text classification [4] and computer vision [5]. We set the hyperparameters to be: batch size 32, hidden size 64, epoch size 50, the dimension of word embeddings 300, dropout 0.5, and L2 regularization lambda 0.7. What type of coding are they using? Publishing on IntechOpen allows authors to earn citations and find new collaborators, meaning more people see your work not only from your own field of study, but from other related fields too. The second segment identifies the product itself (drug-specific strength, dosage form, and formulation). All filters have the same number of columns as the words embeddings. For text classification applications, a matrix of word embeddings stacked by the words embedded vectors according to the order of the words in the sequence, is usually used as the input of the CNN architecture.
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