Faculty Publications
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Item Diagnostic Code Group Prediction by Integrating Structured and Unstructured Clinical Data(Springer Science and Business Media Deutschland GmbH, 2021) Prabhakar, A.; Shidharth, S.; S. Krishnan, G.S.; Kamath S․, S.Diagnostic coding is a process by which written, verbal and other patient-case related documentation are used for enabling disease prediction, accurate documentation, and insurance settlements. It is a prevalently manual process even in countries that have successfully adopted Electronic Health Record (EHR) systems. The problem is exacerbated in developing countries where widespread adoption of EHR systems is still not at par with Western counterparts. EHRs contain a wealth of patient information embedded in numerical, text, and image formats. A disease prediction model that exploits all this information, enabling accurate and faster diagnosis would be quite beneficial. We address this challenging task by proposing mixed ensemble models consisting of boosting and deep learning architectures for the task of diagnostic code group prediction. The models are trained on a dataset created by integrating features from structured (lab test reports) as well as unstructured (clinical text) data. We analyze the proposed model’s performance on MIMIC-III, an open dataset of clinical data using standard multi-label metrics. Empirical evaluations underscored the significant performance of our approach for this task, compared to state-of-the-art works which rely on a single data source. Our novelty lies in effectively integrating relevant information from both data sources thereby ensuring larger ICD-9 code coverage, handling the inherent class imbalance, and adopting a novel approach to form the ensemble models. © 2021, Springer Nature Switzerland AG.Item LATA – Label attention transformer architectures for ICD-10 coding of unstructured clinical notes(Institute of Electrical and Electronics Engineers Inc., 2021) Mayya, V.; Kamath S․, S.S.; Sugumaran, V.Effective code assignment for patient clinical records in a hospital plays a significant role in the process of standardizing medical records, mainly for streamlining clinical care delivery, billing, and managing insurance claims. The current practice employed is manual coding, usually carried out by trained medical coders, making the process subjective, error-prone, inexact, and time-consuming. To alleviate this cost-intensive process, intelligent coding systems built on patients’ structured electronic medical records are critical. Classification of medical diagnostic codes, like ICD-10, is widely employed to categorize patients’ clinical conditions and associated diagnoses. In this work, we present a neural model LATA, built on Label Attention Transformer Architectures for automatic assignment of ICD-10 codes. Our work is benchmarked on the CodiEsp dataset, a dataset for automatic clinical coding systems for multilingual medical documents, used in the eHealth CLEF 2020-Multilingual Information Extraction Shared Task. The experimental results reveal that the proposed LATA variants outperform their basic BERT counterparts by 33-49% in terms of standard metrics like precision, recall, F1-score and mean average precision. The label attention mechanism also enables direct extraction of textual evidence in medical documents that map to the clinical ICD-10 diagnostic codes. © 2021 IEEE.Item Explainable Deep Neural Models for COVID-19 Prediction from Chest X-Rays with Region of Interest Visualization(Institute of Electrical and Electronics Engineers Inc., 2021) Nedumkunnel, I.M.; Elizabeth George, L.; Kamath S․, S.S.; Rosh, N.A.; Mayya, V.COVID-19 has been designated as a once-in-a-century pandemic, and its impact is still being felt severely in many countries, due to the extensive human and green casualties. While several vaccines are under various stage of development, effective screening procedures that help detect the disease at early stages in a non-invasive and resource-optimized manner are the need of the hour. X-ray imaging is fairly accessible in most healthcare institutions and can prove useful in diagnosing this respiratory disease. Although a chest X-ray scan is a viable method to detect the presence of this disease, the scans must be analyzed by trained experts accurately and quickly if large numbers of tests are to be processed. In this paper, a benchmarking study of different preprocessing techniques and state-of-the-art deep learning models is presented to provide comprehensive insights into both the objective and subjective evaluation of their performance. To analyze and prevent possible sources of bias, we preprocessed the dataset in two ways-first, we segmented the lungs alone, and secondly, we formed a bounding box around the lung and used only this area to train. Among the models chosen to benchmark, which were DenseNet201, EfficientNetB7, and VGG-16, DenseNet201 performed better for all three datasets. © 2021 IEEE.Item Ontology-driven Text Feature Modeling for Disease Prediction using Unstructured Radiological Notes(Instituto Politecnico Nacional revista@cic.ipn.mx, 2019) S. Krishnan, G.S.; Kamath S?, S.Clinical Decision Support Systems (CDSSs) support medical personnel by offering aid in decision-making and timely interventions in patient care. Typically such systems are built on structured Electronic Health Records (EHRs), which, unfortunately have a very low adoption rate in developing countries at present. In such situations, clinical notes recorded by medical personnel, though unstructured, can be a significant source for rich patient related information. However, conversion of unstructured clinical notes to a structured EHR form is a manual and time consuming task, underscoring a critical need for more efficient, automated methods. In this paper, a generic disease prediction CDSS built on unstructured radiology text reports is proposed. We incorporate word embeddings and clinical ontologies to model the textual features of the patient data for training a feed-forward neural network for ICD9 disease group prediction. The proposed model built on unstructured text outperformed the state-of-the-art model built on structured data by 9% in terms of AUROC and 23% in terms of AUPRC, thus eliminating the dependency on the availability of structured clinical data. © 2019 Instituto Politecnico Nacional. All rights reserved.Item Multi-channel, convolutional attention based neural model for automated diagnostic coding of unstructured patient discharge summaries(Elsevier B.V., 2021) Mayya, V.; Kamath S?, S.S.; S. Krishnan, G.S.; Gangavarapu, T.Effective coding of patient records in hospitals is an essential requirement for epidemiology, billing, and managing insurance claims. The prevalent practice of manual coding, carried out by trained medical coders, is error-prone and time-consuming. Mitigating this labor-intensive process by developing diagnostic coding systems built on patients’ Electronic Medical Records (EMRs) is vital. However, developing nations with low digitization rates have limited availability of structured EMRs, thereby necessitating a need for systems that leverage unstructured data sources. Despite the rich clinical information available in such unstructured data, modeling them is complex, owing to the variety and sparseness of diagnostic codes, complex structural and temporal nature of summaries, and prolific use of medical jargon. This work proposes a context-attentive network to facilitate automatic diagnostic code assignment as a multi-label classification problem. The proposed model facilitates information aggregation across a patient's discharge summary via multi-channel, variable-sized convolutional filters to extract multi-granular snippets. The attention mechanism enables selecting vital segments in those snippets that map to the clinical codes. The model's superior performance underscores its effectiveness compared to the state-of-the-art on the MIMIC-III database. Additionally, experimental validation using the CodiEsp dataset exhibited the model's interpretability and explainability. © 2021 Elsevier B.V.Item Multimodal medical tensor fusion network-based DL framework for abnormality prediction from the radiology CXRs and clinical text reports(Springer, 2023) Shetty, S.; S, A.V.; Mahale, A.Pulmonary disease is a commonly occurring abnormality throughout this world. The pulmonary diseases include Tuberculosis, Pneumothorax, Cardiomegaly, Pulmonary atelectasis, Pneumonia, etc. A timely prognosis of pulmonary disease is essential. Increasing progress in Deep Learning (DL) techniques has significantly impacted and contributed to the medical domain, specifically in leveraging medical imaging for analysis, prognosis, and therapeutic decisions for clinicians. Many contemporary DL strategies for radiology focus on a single modality of data utilizing imaging features without considering the clinical context that provides more valuable complementary information for clinically consistent prognostic decisions. Also, the selection of the best data fusion strategy is crucial when performing Machine Learning (ML) or DL operation on multimodal heterogeneous data. We investigated multimodal medical fusion strategies leveraging DL techniques to predict pulmonary abnormality from the heterogeneous radiology Chest X-Rays (CXRs) and clinical text reports. In this research, we have proposed two effective unimodal and multimodal subnetworks to predict pulmonary abnormality from the CXR and clinical reports. We have conducted a comprehensive analysis and compared the performance of unimodal and multimodal models. The proposed models were applied to standard augmented data and the synthetic data generated to check the model’s ability to predict from the new and unseen data. The proposed models were thoroughly assessed and examined against the publicly available Indiana university dataset and the data collected from the private medical hospital. The proposed multimodal models have given superior results compared to the unimodal models. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.