Prediction of ICU Readmission Using Clinical Notes

Unplanned readmissions to the ICU result in higher medical costs and an increase in the likelihood of adverse events, extended hospital stays, and mortality. Machine learning models can leverage the large amount of data stored in electronic health records to predict these cases and provide physicians with more information about patient risk at the time of ICU discharge. Most prior work in this area has focused on developing models using only the structured data found in electronic health records and neglects the large amount of unstructured information stored in clinical notes. This work applies deep learning techniques to these notes to predict ICU readmission and develops models that outperform prior work that focuses only on structured data

Predicting early psychiatric readmission with natural language processing of narrative discharge summaries

The ability to predict psychiatric readmission would facilitate the development of interventions to reduce this risk, a major driver of psychiatric health-care costs. The symptoms or characteristics of illness course necessary to develop reliable predictors are not available in coded billing data, but may be present in narrative electronic health record (EHR) discharge summaries. We identified a cohort of individuals admitted to a psychiatric inpatient unit between 1994 and 2012 with a principal diagnosis of major depressive disorder, and extracted inpatient psychiatric discharge narrative notes. Using these data, we trained a 75-topic Latent Dirichlet Allocation (LDA) model, a form of natural language processing, which identifies groups of words associated with topics discussed in a document collection. The cohort was randomly split to derive a training (70%) and testing (30%) data set, and we trained separate support vector machine models for baseline clinical features alone, baseline features plus common individual words and the above plus topics identified from the 75-topic LDA model. Of 4687 patients with inpatient discharge summaries, 470 were readmitted within 30 days. The 75-topic LDA model included topics linked to psychiatric symptoms (suicide, severe depression, anxiety, trauma, eating/weight and panic) and major depressive disorder comorbidities (infection, postpartum, brain tumor, diarrhea and pulmonary disease). By including LDA topics, prediction of readmission, as measured by area under receiver-operating characteristic curves in the testing data set, was improved from baseline (area under the curve 0.618) to baseline+1000 words (0.682) to baseline+75 topics (0.784). Inclusion of topics derived from narrative notes allows more accurate discrimination of individuals at high risk for psychiatric readmission in this cohort. Topic modeling and related approaches offer the potential to improve prediction using EHRs, if generalizability can be established in other clinical cohorts

Design and implementation of a deep recurrent model for prediction of readmission in urgent care using electronic health records

There has been a steady growth in machine learning research in healthcare, however, progress is difficult to measure because of the use of different cohorts, task definitions and input variables. To take the advantage of the availability and value of digital health data, we aim to predict unplanned readmissions to the intensive care unit (ICU) from a publicly available Critical Care dataset called Medical Information Mart for Intensive Care (MIMIC-III). In this research, we formulate a heterogeneous LSTM and CNN architecture specifically to create a model of readmission risk. Our proposed predictive framework outperformed all the benchmark classifiers such as support vector machine, random forest and logistic regression models on all performance measures (AUC, accuracy and precision) except on recall where random forest performed slightly better. Predictions from these models will help in resource planning and decrease mortality or length of stay in clinical care settings

Nursing-Relevant Patient Outcomes and Clinical Processes in Data Science Literature: 2019 Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this paper, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (e.g., natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope the studies described in this paper help readers: (a) understand the breadth and depth of data science’s ability to improve clinical processes and patient outcomes that are relevant to nurses and (b) identify gaps in the literature that are in need of exploration

Linking patient data to scientific knowledge to support contextualized mining

Tese de mestrado, Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2022ICU readmissions are a critical problem associated with either serious conditions, ill nesses, or complications, representing a 4 times increase in mortality risk and a financial burden to health institutions. In developed countries 1 in every 10 patients discharged comes back to the ICU. As hospitals become more and more data-oriented with the adop tion of Electronic Health Records (EHR), there as been a rise in the development of com putational approaches to support clinical decision. In recent years new efforts emerged, using machine learning approaches to make ICU readmission predictions directly over EHR data. Despite these growing efforts, machine learning approaches still explore EHR data directly without taking into account its mean ing or context. Medical knowledge is not accessible to these methods, who work blindly over the data, without considering the meaning and relationships the data objects. Ontolo gies and knowledge graphs can help bridge this gap between data and scientific context, since they are computational artefacts that represent the entities in a domain and how the relate to each other in a formalized fashion. This opportunity motivated the aim of this work: to investigate how enriching EHR data with ontology-based semantic annotations and applying machine learning techniques that explore them can impact the prediction of 30-day ICU readmission risk. To achieve this, a number of contributions were developed, including: (1) An enrichment of the MIMIC-III data set with annotations to several biomedical ontologies; (2) A novel ap proach to predict ICU readmission risk that explores knowledge graph embeddings to represent patient data taking into account the semantic annotations; (3) A variant of the predictive approach that targets different moments to support risk prediction throughout the ICU stay. The predictive approaches outperformed both state-of-the-art and a baseline achieving a ROC-AUC of 0.815 (an increase of 0.2 over the state of the art). The positive results achieved motivated the development of an entrepreneurial project, which placed in the Top 5 of the H-INNOVA 2021 entrepreneurship award

Social and Behavioral Domains in Acute Care Electronic Health Records: Barriers, Facilitators, Relevance, and Value.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018

A Case for Delirium Risk Prediction Models to Aid in Triaging Resources to those Most at Risk an Integrative Literature Review

Abstract Delirium is a complex syndrome resulting from compounding effects of acute illness, comorbidities, and the environment. It results in adverse outcomes: elevated mortality rates, length of stay, readmissions, institutionalization, long-term cognitive changes, and diminished quality of life. The rate of iatrogenic delirium is astounding, ranging from 10%-89%. There are no curative treatments; thus, primary prevention is the key. The purpose of this literature review is to identify and critique the research for the accuracy of risk stratification and feasibility in practice. Support for interventions that prevent delirium is mounting; however, interventions are resource-intensive and often not implemented. Researchers have responded to this problem by developing risk stratification tools to triage interventions toward those of the highest risk. There is evidence that some of the models\u27 implementation is successful; however, they are not yet widely operationalized. A compilation of seven published models of risk prediction was critiqued and compared using the Stetler Model of Evidence-Based Practice as a guiding model. The Newcastle-Ottawa Scale and the Critical Appraisal and the Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS checklist) are employed to aid in the critical appraisal, evaluation of the study\u27s quality, and aid in data abstraction. The models show the ability to stratify risk. Still, their effectiveness in practice cannot be studied without directed interventions because they risk prediction models are created to aid healthcare staff in making clinical decisions. Therefore, a complete clinical pathway with evidence-based interventions should be employed with a delirium risk prediction model to triage the interventions to patients at the highest risk. Recommendations are to implement an automated electronic model (automatic calculation using the EMR or a machine learning model) into clinical practice along with a delirium prevention care pathway. Electronic versions of risk scores allow for an opportunity to achieve clinical efficiency and show statistical superiority to the other models. Published evidence on the impact of the models is diminutive. Their ability to triage patients and aid in clinical decision-making should be published in an impact study. Keywords: Delirium, risk assessment, risk prediction, risk model, risk score, patient safety, patient-centered outcomes researc