Natural language processing (NLP) and machine learning (ML) model for predicting CMS OP-35 categories among patients receiving chemotherapy.

e13591 Background: The Hospital Outpatient Quality Reporting Program is a pay-for-quality data reporting program implemented by the Centers for Medicare & Medicaid Services (CMS). Hospitals collect data on various measures of the quality of care provided in outpatient settings for the CMS. One such measure is OP-35, where data about patients who received chemotherapy in outpatient settings are collected. Such quality measures help hospitals assess their performance and allow patients to compare the quality of care among different hospitals in that region. Currently, the process to label data for OP-35 categories is manual. This study aims to develop a model using NLP and ML to predict the ten OP-35 complication categories and automate the process. Methods: Data from 1000 adult cancer patients who received chemotherapy at a comprehensive cancer center in the South Florida region between Sept and Oct 2019 were extracted to train the ML models. Text from the Chief Complaint field was manually labeled into ten binary categories: anemia, nausea, dehydration, neutropenia, diarrhea, emesis, pneumonia, fever, sepsis, and pain. The data were divided into a training set (80%) and a test set (20%). After initial pre-processing of the text, term frequency–inverse document frequency (TF-IDF) feature extraction method with a vocabulary size of 10,000 was applied. Various models (stochastic gradient descent, support vector classification [SVC], and binary relevance, etc.) were trained to predict multiple labels. These models were evaluated using Jaccard score, accuracy, F1 score, and Hamming loss. Additionally, two deep learning approaches: a single dense output layer and multiple dense output layer models, were also used for comparison. Python version 3.8 was utilized for the analysis. Results: The best performing model was SVC, with a Jaccard score of 85.13 and 90% accuracy. In the first deep learning approach, a single dense output layer was used with multiple neurons where each neuron represented only one label. In the second approach, a separate dense layer for each label was created with one neuron. The model with a single output layer produced an accuracy score of 32%, and the model with multiple output layer had an accuracy score of 31%. Both deep learning models with single and multiple output layers did not perform well compared to SVC. Conclusions: Our study shows an early indication regarding the feasibility of modern ML techniques in predicting multiple label categories or outcomes. As a potential clinical decision support system, this model could replace manual data entry, minimize human error, and decrease resources for data collection. In the next stage, healthcare providers will validate this model by manually checking the predicted labels. In the final stage, model will be deployed in real-time to predict OP-35 categories automatically.

Analysis of Lumber Prices Time Series Using Long Short-Term Memory Artificial Neural Networks

This manuscript confirms the feasibility of using a long short-term memory (LSTM) recurrent neural network (RNN) to forecast lumber stock prices during the great and Coronavirus disease 2019 (COVID-19) pandemic recessions in the USA. The database was composed of 5012 data entries divided into recession periods. We applied a timeseries cross-validation that divided the dataset into an 80:20 training/validation ratio. The network contained five LSTM layers with 50 units each followed by a dense output layer. We evaluated the performance of the network via mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) for 30, 60, and 120 timesteps and the recession periods. The metrics results indicated that the network was able to capture the trend for both recession periods with a remarkably low degree of error. Timeseries forecasting may help the forest and forest product industries to manage their inventory, transportation costs, and response readiness to critical economic events.

Gait-Based Identification Using Deep Recurrent Neural Networks and Acceleration Patterns

This manuscript presents an approach to the challenge of biometric identification based on the acceleration patterns generated by a user while walking. The proposed approach uses the data captured by a smartphone’s accelerometer and gyroscope sensors while the users perform the gait activity and optimizes the design of a recurrent neural network (RNN) to optimally learn the features that better characterize each individual. The database is composed of 15 users, and the acceleration data provided has a tri-axial format in the X-Y-Z axes. Data are pre-processed to estimate the vertical acceleration (in the direction of the gravity force). A deep recurrent neural network model consisting of LSTM cells divided into several layers and dense output layers is used for user recognition. The precision results obtained by the final architecture are above 97% in most executions. The proposed deep neural network-based architecture is tested in different scenarios to check its efficiency and robustness.

EFFECTIVE COMPUTATION OF THE POINCARÉ MAP FOR THE ANALYSIS OF NONLINEAR DYNAMIC CIRCUITS/SYSTEMS USING RUNGE-KUTTA TRIPLES

An enhancement of the classical Runge—Kutta technique for numerical simulations is presented for the computer-aided global analysis of nonlinear dynamic circuits/systems. With Runge—Kutta triples a remarkable saving of calculation time can be achieved by using an interpolation polynomial for dense output. The Runge—Kutta triples are applied to calculate the Poincaré map for autonomous models/systems.