This study explores the creation of a machine learning model to automatically identify whether a Neonatal Intensive Care Unit (NICU) patient was diagnosed with neonatal jaundice during a particular hospitalization based on their associated clinical notes. We develop a number of techniques for text preprocessing and feature selection and compare the effectiveness of different classification models. We show that using ensemble decision tree classification, both with AdaBoost and with bagging, outperforms support vector machines (SVM), the current state-of-the-art technique for neonatal jaundice coding.
At least one attempt has been made to use deep learning (namely character-level recurrent neural networks) to build more general coding models, but with limited success @cite_7 . The main limitation with this method is that deep learning models require a large amount of data to train @cite_24 , but even a large clinical dataset may only contain a few examples of case files with any given ICD code. Additionally, the ICD-9 standard contains over 14,000 different codes; treating coding as a simple multi-class classification problem with 14,000 different classes is infeasible.
@cite_24 @cite_7
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Despite the recent advances in automatically describing image contents, their applications have been mostly limited to image caption datasets containing natural images (e.g., Flickr 30k, MSCOCO). In this paper, we present a deep learning model to efficiently detect a disease from an image and annotate its contexts (e.g., location, severity and the affected organs). We employ a publicly available radiology dataset of chest x-rays and their reports, and use its image annotations to mine disease names to train convolutional neural networks (CNNs). In doing so, we adopt various regularization techniques to circumvent the large normal-vs-diseased cases bias. Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features. Moreover, we introduce a novel approach to use the weights of the already trained pair of CNN RNN on the domain-specific image text dataset, to infer the joint image text contexts for composite image labeling. Significantly improved image annotation results are demonstrated using the recurrent neural cascade model by taking the joint image text contexts into account. Deep learning algorithms require large amounts of labeled data which is difficult to attain for medical imaging. Even if a particular dataset is accessible, a learned classifier struggles to maintain the same level of performance on a different medical imaging dataset from a new or never-seen data source domain. Utilizing generative adversarial networks in a semi-supervised learning architecture, we address both problems of labeled data scarcity and data domain overfitting. For cardiac abnormality classification in chest X-rays, we demonstrate that an order of magnitude less data is required with semi-supervised learning generative adversarial networks than with conventional supervised learning convolutional neural networks. In addition, we demonstrate its robustness across different datasets for similar classification tasks. Neural network models and other machine learning methods have successfully been applied to several medical classification problems. These models can be periodically refined and retrained as new cases become available. Since training neural networks by backpropagation is time consuming, it is desirable that a minimum number of representative cases be kept in the training set (i.e., redundant cases should be removed). The removal of redundant cases should be carefully monitored so that classification performance is not significantly affected. We made experiments on data removal on a data set of 700 patients suspected of having myocardial infarction and show that there is no statistical difference in classification performance (measured by the differences in areas under the ROC curve on two previously unknown sets of 553 and 500 cases) when as many as 86 of the cases are randomly removed. A proportional reduction in the amount of time required to train the neural network model is achieved. This study proposes a methodology to support coding professionals in assigning ICD-9-CM codes to inpatient episodes. This subject has been predominantly addressed through the use of natural language processing methods, which show limited generalizability. To surpass this issue, this paper proposes a methodology entailing an adaptive data processing method based on structured electronic health record data, whereby raw clinical data is mapped into a feature set, and based on which supervised learning algorithms are trained. After applying a filter method for feature selection, support vector machine (SVM) classifiers are trained to obtain predictions for assigning codes to each episode. This approach is tested using a dataset of inpatient episodes from a department of Internal Medicine. Classifiers exhibited F1-measure values around 52 . Recall was generally higher than precision, which is considered valuable for coding support purposes. Analyzing results on an individual code basis sheds light on some key-issues regarding the use of structured electronic health record data in supporting clinical coding.