Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration

Aken, Betty van; Papaioannou, Jens-Michalis; Mayrdorfer, Manuel; Budde, Klemens; Gers, Felix A.; Löser, Alexander

doi:10.48550/arxiv.2102.04110

Cited by 2 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, in 2021 Aken et al [15], have introduced a model, called CORe, on top of BioBERT for predicting multiple clinical outcomes along with the duration of ICU stay. The authors devised a distinct note extracted from the discharge summary, leveraging it within their predictive framework.…”

Section: Building Predictive Models For Patient Carementioning

confidence: 99%

Deciphering Clinical Narratives - Augmented Intelligence for Decision Making in Healthcare Sector

Dey,

Jana,

Dasgupta

et al. 2023

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Clinical notes that describe details about diseases, symptoms, treatments, and observed reactions of patients to them, are valuable resources to generate insights about the effectiveness of treatments. Their role in designing better clinical decision making systems is being increasingly acknowledged. However, the availability of clinical notes is still an issue due to privacy violation concerns. Hence most of the work done are on small datasets and neither the power of machine learning is fully utilized, nor is it possible to validate the models properly. With the availability of the Medical Information Mart for Intensive Care (MIMIC-III v1.4) dataset for researchers though, the problem has been somewhat eased. In this paper we have presented an overview of our earlier work on designing deep neural models for prediction of outcomes and hospital stay for patients using MIMIC data. We have also presented new work on patient stratification and explanation generation for patient cohorts. This is early work targeted towards studying trajectories for treatment for different cohorts of patients, which can ultimately lead to discovery of low-risk models for individual patients to ensure better outcomes.

show abstract

Section: Building Predictive Models For Patient Carementioning

confidence: 99%

Deciphering Clinical Narratives - Augmented Intelligence for Decision Making in Healthcare Sector

Dey,

Jana,

Dasgupta

et al. 2023

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model

Gao,

Liu,

Kang

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

Background Clinical notes contain contextualized information beyond structured data related to patients’ past and current health status. Objective This study aimed to design a multimodal deep learning approach to improve the evaluation precision of hospital outcomes for heart failure (HF) using admission clinical notes and easily collected tabular data. Methods Data for the development and validation of the multimodal model were retrospectively derived from 3 open-access US databases, including the Medical Information Mart for Intensive Care III v1.4 (MIMIC-III) and MIMIC-IV v1.0, collected from a teaching hospital from 2001 to 2019, and the eICU Collaborative Research Database v1.2, collected from 208 hospitals from 2014 to 2015. The study cohorts consisted of all patients with critical HF. The clinical notes, including chief complaint, history of present illness, physical examination, medical history, and admission medication, as well as clinical variables recorded in electronic health records, were analyzed. We developed a deep learning mortality prediction model for in-hospital patients, which underwent complete internal, prospective, and external evaluation. The Integrated Gradients and SHapley Additive exPlanations (SHAP) methods were used to analyze the importance of risk factors. Results The study included 9989 (16.4%) patients in the development set, 2497 (14.1%) patients in the internal validation set, 1896 (18.3%) in the prospective validation set, and 7432 (15%) patients in the external validation set. The area under the receiver operating characteristic curve of the models was 0.838 (95% CI 0.827-0.851), 0.849 (95% CI 0.841-0.856), and 0.767 (95% CI 0.762-0.772), for the internal, prospective, and external validation sets, respectively. The area under the receiver operating characteristic curve of the multimodal model outperformed that of the unimodal models in all test sets, and tabular data contributed to higher discrimination. The medical history and physical examination were more useful than other factors in early assessments. Conclusions The multimodal deep learning model for combining admission notes and clinical tabular data showed promising efficacy as a potentially novel method in evaluating the risk of mortality in patients with HF, providing more accurate and timely decision support.

show abstract

Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration

Cited by 2 publications

References 5 publications

Deciphering Clinical Narratives - Augmented Intelligence for Decision Making in Healthcare Sector

Deciphering Clinical Narratives - Augmented Intelligence for Decision Making in Healthcare Sector

Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model

Contact Info

Product

Resources

About