Predicting ICD-9 Codes Using Self-Report of Patients

Singaravelan, Anandakumar; Hsieh, Chung-Ho; Liao, Yikai; Hsu, Jia-Lien

doi:10.3390/app112110046

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…For 2833 ICD-9 codes, Xie et al [ 47 ] employed a deep learning model trained on the diagnosis description and attained a sensitivity of 0.29. From a subjective feature of clinical notes, Singaravelan et al [ 48 ] developed a deep learning model and attained a recall of 0.57 for 1871 ICD-9 codes.…”

Section: Discussionmentioning

confidence: 99%

Applying Deep Learning Model to Predict Diagnosis Code of Medical Records

et al. 2023

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The International Classification of Diseases (ICD) code is a diagnostic classification standard that is frequently used as a referencing system in healthcare and insurance. However, it takes time and effort to find and use the right diagnosis code based on a patient’s medical records. In response, deep learning (DL) methods have been developed to assist physicians in the ICD coding process. Our findings propose a deep learning model that utilized clinical notes from medical records to predict ICD-10 codes. Our research used text-based medical data from the outpatient department (OPD) of a university hospital from January to December 2016. The dataset used clinical notes from five departments, and a total of 21,953 medical records were collected. Clinical notes consisted of a subjective component, objective component, assessment, plan (SOAP) notes, diagnosis code, and a drug list. The dataset was divided into two groups: 90% for training and 10% for test cases. We applied natural language processing (NLP) technique (word embedding, Word2Vector) to process the data. A deep learning-based convolutional neural network (CNN) model was created based on the information presented above. Three metrics (precision, recall, and F-score) were used to calculate the achievement of the deep learning CNN model. Clinically acceptable results were achieved through the deep learning model for five departments (precision: 0.53–0.96; recall: 0.85–0.99; and F-score: 0.65–0.98). With a precision of 0.95, a recall of 0.99, and an F-score of 0.98, the deep learning model performed the best in the department of cardiology. Our proposed CNN model significantly improved the prediction performance for an automated ICD-10 code prediction system based on prior clinical information. This CNN model could reduce the laborious task of manual coding and could assist physicians in making a better diagnosis.

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Section: Discussionmentioning

confidence: 99%

Applying Deep Learning Model to Predict Diagnosis Code of Medical Records

et al. 2023

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“…Two types of code ontology embeddings are constructed from ICD-10 ontology and NDC ontology . Because medical codes in raw EMR data are leaf nodes in code ontology trees, code ontology embedding can be obtained by using graph attention network (GAT) [ 8 , 10 , 12 , 13 ]. It can encode the classification knowledge in diagnostic and drug code trees as external domain features.…”

Section: Methodsmentioning

confidence: 99%

“…for a single admission. More and more attention has been paid to EMR-based auxiliary diagnosis and treatment, such as clinical knowledge question answering [1,2], health risk warning [3][4][5][6], auxiliary diagnostic [7,8] and electronic prescription recommendation [9,10]. Medication recommendation is an important research direction in EMRbased applications.…”

Section: Introductionmentioning

confidence: 99%

MR-KPA: medication recommendation by combining knowledge-enhanced pre-training with a deep adversarial network

et al. 2022

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Background Medication recommendation based on electronic medical record (EMR) is a research hot spot in smart healthcare. For developing computational medication recommendation methods based on EMR, an important challenge is the lack of a large number of longitudinal EMR data with time correlation. Faced with this challenge, this paper proposes a new EMR-based medication recommendation model called MR-KPA, which combines knowledge-enhanced pre-training with the deep adversarial network to improve medication recommendation from both feature representation and the fine-tuning process. Firstly, a knowledge-enhanced pre-training visit model is proposed to realize domain knowledge-based external feature fusion and pre-training-based internal feature mining for improving the feature representation. Secondly, a medication recommendation model based on the deep adversarial network is developed to optimize the fine-tuning process of pre-training visit model and alleviate over-fitting of model caused by the task gap between pre-training and recommendation. Result The experimental results on EMRs from medical and health institutions in Hainan Province, China show that the proposed MR-KPA model can effectively improve the accuracy of medication recommendation on small-scale longitudinal EMR data compared with existing representative methods. Conclusion The advantages of the proposed MR-KPA are mainly attributed to knowledge enhancement based on ontology embedding, the pre-training visit model and adversarial training. Each of these three optimizations is very effective for improving the capability of medication recommendation on small-scale longitudinal EMR data, and the pre-training visit model has the most significant improvement effect. These three optimizations are also complementary, and their integration makes the proposed MR-KPA model achieve the best recommendation effect.

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International Classification of Diseases Prediction from MIMIIC-III Clinical Text Using Pre-Trained ClinicalBERT and NLP Deep Learning Models Achieving State of the Art

Aden,

Child,

Reyes-Aldasoro

2024

BDCC

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The International Classification of Diseases (ICD) serves as a widely employed framework for assigning diagnosis codes to electronic health records of patients. These codes facilitate the encapsulation of diagnoses and procedures conducted during a patient’s hospitalisation. This study aims to devise a predictive model for ICD codes based on the MIMIC-III clinical text dataset. Leveraging natural language processing techniques and deep learning architectures, we constructed a pipeline to distill pertinent information from the MIMIC-III dataset: the Medical Information Mart for Intensive Care III (MIMIC-III), a sizable, de-identified, and publicly accessible repository of medical records. Our method entails predicting diagnosis codes from unstructured data, such as discharge summaries and notes encompassing symptoms. We used state-of-the-art deep learning algorithms, such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, bidirectional LSTM (BiLSTM) and BERT models after tokenizing the clinical test with Bio-ClinicalBERT, a pre-trained model from Hugging Face. To evaluate the efficacy of our approach, we conducted experiments utilizing the discharge dataset within MIMIC-III. Employing the BERT model, our methodology exhibited commendable accuracy in predicting the top 10 and top 50 diagnosis codes within the MIMIC-III dataset, achieving average accuracies of 88% and 80%, respectively. In comparison to recent studies by Biseda and Kerang, as well as Gangavarapu, which reported F1 scores of 0.72 in predicting the top 10 ICD-10 codes, our model demonstrated better performance, with an F1 score of 0.87. Similarly, in predicting the top 50 ICD-10 codes, previous research achieved an F1 score of 0.75, whereas our method attained an F1 score of 0.81. These results underscore the better performance of deep learning models over conventional machine learning approaches in this domain, thus validating our findings. The ability to predict diagnoses early from clinical notes holds promise in assisting doctors or physicians in determining effective treatments, thereby reshaping the conventional paradigm of diagnosis-then-treatment care. Our code is available online.

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Predicting ICD-9 Codes Using Self-Report of Patients

Cited by 5 publications

References 35 publications

Applying Deep Learning Model to Predict Diagnosis Code of Medical Records

Applying Deep Learning Model to Predict Diagnosis Code of Medical Records

MR-KPA: medication recommendation by combining knowledge-enhanced pre-training with a deep adversarial network

International Classification of Diseases Prediction from MIMIIC-III Clinical Text Using Pre-Trained ClinicalBERT and NLP Deep Learning Models Achieving State of the Art

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