Novel Natural Language Processing and Machine Learning Methods to Characterize Unstructured Patient-Reported Outcomes

2020 ◽  
Author(s):  
Zhaohua Lu ◽  
Jin-ah Sim ◽  
Christopher B. Forrest ◽  
Kevin R. Krull ◽  
Kumar Srivastava ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Patient Reported Outcomes ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Patient Reported

scholarly journals Neural Language Modeling for Molecule Generation

2021 ◽  
Author(s):  
Sanjar Adilov
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Drug Design ◽  
Natural Language ◽  
Language Processing ◽  
De Novo ◽  
Language Modeling ◽  
Machine Learning Methods

Generative neural networks have shown promising results in <i>de novo</i> drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this paper, we present a survey of various machine learning methods for SMILES-based language modeling and propose our benchmarking results on a standardized subset of ChEMBL database.

scholarly journals Neural Language Modeling for Molecule Generation

2021 ◽  
Author(s):  
Sanjar Adilov
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Drug Design ◽  
Natural Language ◽  
Language Processing ◽  
De Novo ◽  
Language Modeling ◽  
Machine Learning Methods

Generative neural networks have shown promising results in <i>de novo</i> drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this paper, we present a survey of various machine learning methods for SMILES-based language modeling and propose our benchmarking results on a standardized subset of ChEMBL database.

scholarly journals Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates through Natural Language Processing and Machine Learning

2016 ◽  
Vol 8 ◽  
pp. BII.S38308 ◽  
Author(s):  
Kevin Bretonnel Cohen ◽  
Benjamin Glass ◽  
Hansel M. Greiner ◽  
Katherine Holland-Bouley ◽  
Shannon Standridge ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Epilepsy Surgery ◽  
Classification Performance ◽  
Training Data ◽  
Pediatric Epilepsy ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Pediatric Epilepsy Surgery

Objective: We describe the development and evaluation of a system that uses machine learning and natural language processing techniques to identify potential candidates for surgical intervention for drug-resistant pediatric epilepsy. The data are comprised of free-text clinical notes extracted from the electronic health record (EHR). Both known clinical outcomes from the EHR and manual chart annotations provide gold standards for the patient's status. The following hypotheses are then tested: 1) machine learning methods can identify epilepsy surgery candidates as well as physicians do and 2) machine learning methods can identify candidates earlier than physicians do. These hypotheses are tested by systematically evaluating the effects of the data source, amount of training data, class balance, classification algorithm, and feature set on classifier performance. The results support both hypotheses, with F-measures ranging from 0.71 to 0.82. The feature set, classification algorithm, amount of training data, class balance, and gold standard all significantly affected classification performance. It was further observed that classification performance was better than the highest agreement between two annotators, even at one year before documented surgery referral. The results demonstrate that such machine learning methods can contribute to predicting pediatric epilepsy surgery candidates and reducing lag time to surgery referral.

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