Multi-label natural language processing to identify diagnosis and procedure codes from MIMIC-III inpatient notes

In the United States, 25% or greater than 200 billion dollars of hospital spending accounts for administrative costs that involve services for medical coding and billing. With the increasing number of patient records, manual assignment of the codes performed is overwhelming, time-consuming and error-prone, causing billing errors. Natural language processing can automate the extraction of codes/labels from unstructured clinical notes, which can aid human coders to save time, increase productivity, and verify medical coding errors. Our objective is to identify appropriate diagnosis and procedure codes from clinical notes by performing multi-label classification. We used de-identified data of critical care patients from the MIMIC-III database and subset the data to select the ten (top-10) and fifty (top-50) most common diagnoses and procedures, which covers 47.45% and 74.12% of all admissions respectively. We implemented state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) to fine-tune the language model on 80% of the data and validated on the remaining 20%. The model achieved an overall accuracy of 87.08%, an F1 score of 85.82%, and an AUC of 91.76% for top-10 codes. For the top-50 codes, our model achieved an overall accuracy of 93.76%, an F1 score of 92.24%, and AUC of 91%. When compared to previously published research, our model outperforms in predicting codes from the clinical text. We discuss approaches to generalize the knowledge discovery process of our MIMIC-BERT to other clinical notes. This can help human coders to save time, prevent backlogs, and additional costs due to coding errors.Comment: This is a shortened version of the Capstone Project that was accepted by the Faculty of Indiana University, in partial fulfillment of the requirements for the degree of Master of Science in Health Informatic

Artificial intelligence for prediction of International Classification of Disease codes

Background: The automatic coding of electronic medical records with ICD (International Classification of Diseases) codes is an area of interest due to its potential in improving efficiency and streamlining processes such as billing and outcome tracking. artificial intelligence (AI), and particularly convolutional neural networks (CNN), have been suggested as a possible mechanism for automatic coding. To this end, a rapid review has been undertaken in order to assess the current use of CNN in predicting ICD codes from electronic medical records. Methods: After screening PubMed, IEEE Xplore, Scopus, and Google Scholar, 11 studies were analyzed for the use of CNN in predicting ICD codes. We used artificial intelligence and ICD prediction as keywords in the search strategy. Results: The analysis yielded a recommendation to further explore and research CNN frameworks as a promising lead to automatic ICD coding when paired with word embedding and/or neural transfer learning, while keeping research open to a wide variety of AI techniques. Conclusion: CNN frameworks are promising for the prediction of ICD codes from clinical notes. Bangabandhu Sheikh Mujib Medical University Journal 2023;16(2): 118-12

Automated machine learning for healthcare and clinical notes analysis

Machine learning (ML) has been slowly entering every aspect of our lives and its positive impact has been astonishing. To accelerate embedding ML in more applications and incorporating it in real-world scenarios, automated machine learning (AutoML) is emerging. The main purpose of AutoML is to provide seamless integration of ML in various industries, which will facilitate better outcomes in everyday tasks. In healthcare, AutoML has been already applied to easier settings with structured data such as tabular lab data. However, there is still a need for applying AutoML for interpreting medical text, which is being generated at a tremendous rate. For this to happen, a promising method is AutoML for clinical notes analysis, which is an unexplored research area representing a gap in ML research. The main objective of this paper is to fill this gap and provide a comprehensive survey and analytical study towards AutoML for clinical notes. To that end, we first introduce the AutoML technology and review its various tools and techniques. We then survey the literature of AutoML in the healthcare industry and discuss the developments specific to clinical settings, as well as those using general AutoML tools for healthcare applications. With this background, we then discuss challenges of working with clinical notes and highlight the benefits of developing AutoML for medical notes processing. Next, we survey relevant ML research for clinical notes and analyze the literature and the field of AutoML in the healthcare industry. Furthermore, we propose future research directions and shed light on the challenges and opportunities this emerging field holds. With this, we aim to assist the community with the implementation of an AutoML platform for medical notes, which if realized can revolutionize patient outcomes