“Can NLP techniques be utilized as a reliable tool for medical science?” - Building a NLP Framework to Classify Medical Reports

Sadman, Nafiz; Tasneem, Sumaiya; Haque, Ariful; Islam, Maminur; Ahsan, Md Manjurul; Gupta, Kishor Datta

doi:10.1109/iemcon51383.2020.9284834

Cited by 11 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Naive Bayes classifiers work better than expected, considering their naive design and simplified assumptions of independence. However, the simplicity of the Naive Bayes algorithm results in it having poorer performance compared with ensemble methods such as Random Forest classifiers [27]. In addition, radiology class labels are frequently correlated and not independent, thus making the Naive Bayes classifier a less ideal choice.…”

Section: Conventional Machine Learning Modelmentioning

confidence: 99%

Automated labelling of radiology reports using natural language processing: Comparison of traditional and newer methods

Chng

Tern

Kan³

et al. 2023

Health Care Science

View full text Add to dashboard Cite

Automated labelling of radiology reports using natural language processing allows for the labelling of ground truth for large datasets of radiological studies that are required for training of computer vision models. This paper explains the necessary data preprocessing steps, reviews the main methods for automated labelling and compares their performance. There are four main methods of automated labelling, namely: (1) rules‐based text‐matching algorithms, (2) conventional machine learning models, (3) neural network models and (4) Bidirectional Encoder Representations from Transformers (BERT) models. Rules‐based labellers perform a brute force search against manually curated keywords and are able to achieve high F1 scores. However, they require proper handling of negative words. Machine learning models require preprocessing that involves tokenization and vectorization of text into numerical vectors. Multilabel classification approaches are required in labelling radiology reports and conventional models can achieve good performance if they have large enough training sets. Deep learning models make use of connected neural networks, often a long short‐term memory network, and are similarly able to achieve good performance if trained on a large data set. BERT is a transformer‐based model that utilizes attention. Pretrained BERT models only require fine‐tuning with small data sets. In particular, domain‐specific BERT models can achieve superior performance compared with the other methods for automated labelling.

show abstract

Section: Conventional Machine Learning Modelmentioning

confidence: 99%

Automated labelling of radiology reports using natural language processing: Comparison of traditional and newer methods

Chng

Tern

Kan³

et al. 2023

Health Care Science

View full text Add to dashboard Cite

show abstract

“…In terms of textual form, there is some similarity between the adversarial and augmented examples in that they both generate similar copies of original examples by performing certain modification operations in the original example. In the natural language field, gradient-based adversarial training is effective in improving the accuracy and generalization of models [ 7 , 21 ] but has weak gains in adversarial robustness. In addition, adversarial data augmentation [ 37 , 38 ] and virtual adversarial data augmentation [ 21 ] also effectively improve the adversarial robustness of models, but such methods are prone to decrease model accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Physician writing styles vary widely, as well as different probabilities of disease outbreaks in different medical subfields. These objective factors lead to existing datasets with significant deficiencies: insufficient data volume [ 6 ], nonopen access [ 5 ], and unbalanced categories [ 7 ]. Abundant medical specialty categories with little and unbalanced data are seriously impacting the performance of the classification model, which is the greatest challenge in the task of medical specialty classification.…”

Section: Introductionmentioning

confidence: 99%

“…As far as we know, the existing work focuses on how to design a more optimal model and tune the best parameters [ 6 ] to improve accuracy, such as comparing the effectiveness of different machine-learning models and deep-learning models, determining the best combination of models [ 7 ] or algorithms [ 5 ]. An approach of integrated data analysis was proposed in [ 5 ], where the researchers applied various techniques to extract features, including the unified medical language system and semantic network.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Medical Specialty Classification Based on Semiadversarial Data Augmentation

Zhang,

Zhu,

Tan

et al. 2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Rapidly increasing adoption of electronic health record (EHR) systems has caused automated medical specialty classification to become an important research field. Medical specialty classification not only improves EHR system retrieval efficiency and helps general practitioners identify urgent patient issues but also is useful in studying the practice and validity of clinical referral patterns. However, currently available medical note data are imbalanced and insufficient. In addition, medical specialty classification is a multicategory problem, and it is not easy to remove sensitive information from numerous medical notes and tag them. To solve those problems, we propose a data augmentation method based on adversarial attacks. The semiadversarial examples generated during the dynamic process of adversarial attacking are added to the training set as augmented examples, which can effectively expand the coverage of the training data on the decision space. Besides, as nouns in medical notes are critical information, we design a classification framework incorporating probabilistic information of nouns, with confidence recalculation after the softmax layer. We validate our proposed method on an 18-class dataset with extremely unbalanced data, and comparison experiments with four benchmarks show that our method improves accuracy and F1 score to the optimal level, by an average of 14.9%.

show abstract

“…Along these lines, recent advances in AI, in particular those directed to Natural Language Processing (NLP) have been incorporating tools of semantic web analysis, such as conceptual relational networks ( 120 , 121 ), semantic-syntactic classification ( 122 ), and similarity mapping ( 123 ). The problem, again, is a matter of throughput: effective implementation (training, in particular) of such NLP tools is only enabled if one has extremely large data corpora being accessed on a concurrent fashion ( 124 ).…”

Section: Challenges To Computational Learning In Precision Medicinementioning

confidence: 99%

Data Integration Challenges for Machine Learning in Precision Medicine

Martínez-García

Hernández‐Lemus

2022

Front. Med.

View full text Add to dashboard Cite

A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.

show abstract

“Can NLP techniques be utilized as a reliable tool for medical science?” - Building a NLP Framework to Classify Medical Reports

Cited by 11 publications

References 21 publications

Automated labelling of radiology reports using natural language processing: Comparison of traditional and newer methods

Automated labelling of radiology reports using natural language processing: Comparison of traditional and newer methods

Medical Specialty Classification Based on Semiadversarial Data Augmentation

Data Integration Challenges for Machine Learning in Precision Medicine

Contact Info

Product

Resources

About