Evaluation of Neural Machine translation for conversion of International Classification of disease codes to the Abbreviated injury Scale

The Abbreviated Injury Scale (AIS) is an essential tool for injury research since it allows for comparisons of injury severity among patients, however, the International Classification of Diseases (ICD) is more widely used to capture medical information. The problem of conversion between these two medical coding systems has similarities to the challenges encountered in language translation. We therefore hypothesize that neural machine translation (NMT), a deep learning technique which is commonly used for human language translation, could be used to convert ICD codes to AIS. The objective of this study was to compare the accuracy of a NMT model for determining injury severity compared to two established methods of conversion. The injury severity classifications used for this study were Injury Severity Score (ISS) ≥ 16, Maximum AIS severity (MAIS) ≥ 3, and MAIS ≥ 2. Data from a US national trauma registry, which has patient injuries coded in both AIS and ICD, was used to train a NMT model. Testing data from a separate year was used to determine the accuracy of the NMT model predictions against the actual ISS recorded in the registry. The prediction accuracy of the NMT model was compared to that of the official Association for the Advancement of Automotive Medicine (AAAM) ICD-AIS map and the R package 'ICD Program for Injury Categorization in R' (ICDPIC-R). The results show that the NMT model was the most accurate across all injury severity classifications, followed by the ICD-AIS map and then ICDPIC-R package. The NMT model also showed the highest correlation between the predicted and observe ISS scores. Overall, NMT appears to be a promising method for predicting injury severity from ICD codes, however, validation in external databases is needed.

Reconstruction of lossless molecular representations from fingerprints

The simplified molecular-input line-entry system (SMILES) is the most prevalent molecular representation used in AI-based chemical applications. However, there are innate limitations associated with the internal structure of SMILES representations. In this context, this study exploits the resolution and robustness of unique molecular representations, i.e., SMILES and SELFIES (SELF-referencIng Embedded strings), reconstructed from a set of structural fingerprints, which are proposed and used herein as vital representational tools for chemical and natural language processing (NLP) applications. This is achieved by restoring the connectivity information lost during fingerprint transformation with high accuracy. Notably, the results reveal that seemingly irreversible molecule-to-fingerprint conversion is feasible. More specifically, four structural fingerprints, extended connectivity, topological torsion, atom pairs, and atomic environments can be used as inputs and outputs of chemical NLP applications. Therefore, this comprehensive study addresses the major limitation of structural fingerprints that precludes their use in NLP models. Our findings will facilitate the development of text- or fingerprint-based chemoinformatic models for generative and translational tasks.

An intelligent Chatbot using deep learning with Bidirectional RNN and attention model

This paper shows the modeling and performance in deep learning computation for an Assistant Conversational Agent (Chatbot). The utilization of Tensorflow software library, particularly Neural Machine Translation (NMT) model. Acquiring knowledge for modeling is one of the most important task and quite difficult to preprocess it. The Bidirectional Recurrent Neural Networks (BRNN) containing attention layers is used, so that input sentence with large number of tokens (or sentences with more than 20–40 words) can be replied with more appropriate conversation. The dataset used in the paper for training of model is used from Reddit. The model is developed to perform English to English translation. The main purpose of this work is to increase the perplexity and learning rate of the model and find Bleu Score for translation in same language. The experiments are conducted using Tensorflow using python 3.6. The perplexity, leaning rate, Bleu score and Average time per 1000 steps are 56.10, 0.0001, 30.16 and 4.5 respectively. One epoch is completed at 23,000 steps. The paper also study MacBook Air as a system for neural network and deep learning.