Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Automatic extraction of the patient’s health information from the unstructured data concerning the discharge summary remains challenging.Discharge summary related documents contain various aspects of the patient health condition to examine the quality of treatment and thereby help improve decision-making in the medical field.Using a sentiment dictionary and feature engineering,the researchers primarily mine semantic text features.However,choosing and designing features requires a lot of manpower.The proposed approach is an unsupervised deep learning model that learns a set of clusters embedded in the latent space.A composite model including Active Learning(AL),Convolutional Neural Network(CNN),BiGRU,and Multi-Attention,called ACBMA in this research,is designed to measure the quality of treatment based on discharge summaries text sentiment detection.CNN is utilized for extracting the set of local features of text vectors.Then BiGRU network was utilized to extract the text’s global features to solve the issues that a single CNN cannot obtain global semantic information and the traditional Recurrent Neural Network(RNN)gradient disappearance.Experiments prove that the ACBMA method can demonstrate the effectiveness of the suggested method,achieve comparable results to state-of-arts methods in sentiment detection,and outperform them with accurate benchmarks.Finally,several algorithm studies ultimately determined that the ACBMA method is more precise for discharge summaries sentiment analysis.

Development and pilot implementation of a patient-oriented discharge summary for critically Ill patients

BACKGROUND Patients leaving the intensive care unit(ICU)often experience gaps in care due to deficiencies in discharge communication,leaving them vulnerable to increased stress,adverse events,readmission to ICU,and death.To facilitate discharge communication,written summaries have been implemented to provide patients and their families with information on medications,activity and diet restrictions,follow-up appointments,symptoms to expect,and who to call if there are questions.While written discharge summaries for patients and their families are utilized frequently in surgical,rehabilitation,and pediatric settings,few have been utilized in ICU settings.AIM To develop an ICU specific patient-oriented discharge summary tool(PODS-ICU),and pilot test the tool to determine acceptability and feasibility.METHODS Patient-partners(i.e.,individuals with lived experience as an ICU patient or family member of an ICU patient),ICU clinicians(i.e.,physicians,nurses),and researchers met to discuss ICU patients’specific informational needs and design the PODS-ICU through several cycles of discussion and iterative revisions.Research team nurses piloted the PODS-ICU with patient and family participants in two ICUs in Calgary,Canada.Follow-up surveys on the PODS-ICU and its impact on discharge were administered to patients,family participants,and ICU nurses.RESULTS Most participants felt that their discharge from the ICU was good or better(n=13;87.0%),and some(n=9;60.0%)participants reported a good understanding of why the patient was in ICU.Most participants(n=12;80.0%)reported that they understood ICU events and impacts on the patient’s health.While many patients and family participants indicated the PODS-ICU was informative and useful,ICU nurses reported that the PODS-ICU was“not reasonable”in their daily clinical workflow due to“time constraint”.CONCLUSION The PODS-ICU tool provides patients and their families with essential information as they discharge from the ICU.This tool has the potential to engage and empower patients and their families in ensuring continuity of care beyond ICU discharge.However,the PODS-ICU requires pairing with earlier discharge practices and integration with electronic clinical information systems to fit better into the clinical workflow for ICU nurses.Further refinement and testing of the PODS-ICU tool in diverse critical care settings is needed to better assess its feasibility and its effects on patient health outcomes.