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Sentiment Analysis Based on the Nursing Notes on In-Hospital 28-Day Mortality of Sepsis Patients Utilizing the MIMIC-III Database

Qiaoyan Gao, Dandan Wang, Pingping Sun, Xiaorong Luan, Wenfeng Wang

2021Computational and Mathematical Methods in Medicine18 citationsDOIOpen Access PDF

Abstract

In medical visualization, nursing notes contain rich information about a patient’s pathological condition. However, they are not widely used in the prediction of clinical outcomes. With advances in the processing of natural language, information begins to be extracted from large-scale unstructured data like nursing notes. This study extracted sentiment information in nursing notes and explored its association with in-hospital 28-day mortality in sepsis patients. The data of patients and nursing notes were extracted from the MIMIC-III database. A COX proportional hazard model was used to analyze the relationship between sentiment scores in nursing notes and in-hospital 28-day mortality. Based on the COX model, the individual prognostic index (PI) was calculated, and then, survival was analyzed. Among eligible 1851 sepsis patients, 580 cases suffered from in-hospital 28-day mortality (dead group), while 1271 survived (survived group). Significant differences were shown between two groups in sentiment polarity, Simplified Acute Physiology Score II (SAPS-II) score, age, and intensive care unit (ICU) type (all <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>P</a:mi> <a:mo>&lt;</a:mo> <a:mn>0.001</a:mn> </a:math> ). Multivariate COX analysis exhibited that sentiment polarity (HR: 0.499, 95% CI: 0.409-0.610, <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>P</c:mi> <c:mo>&lt;</c:mo> <c:mn>0.001</c:mn> </c:math> ) and sentiment subjectivity (HR: 0.710, 95% CI: 0.559-0.902, <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:mi>P</e:mi> <e:mo>=</e:mo> <e:mn>0.005</e:mn> </e:math> ) were inversely associated with in-hospital 28-day mortality, while the SAPS-II score (HR: 1.034, 95% CI: 1.029-1.040, <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" id="M4"> <g:mi>P</g:mi> <g:mo>&lt;</g:mo> <g:mn>0.001</g:mn> </g:math> ) was positively correlated with in-hospital 28-day mortality. The median death time of patients with <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" id="M5"> <i:mtext>PI</i:mtext> <i:mo>≥</i:mo> <i:mn>0.561</i:mn> </i:math> was significantly earlier than that of patients with <k:math xmlns:k="http://www.w3.org/1998/Math/MathML" id="M6"> <k:mtext>PI</k:mtext> <k:mo>&lt;</k:mo> <k:mn>0.561</k:mn> </k:math> (13.5 vs. 49.8 days, <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" id="M7"> <m:mi>P</m:mi> <m:mo>&lt;</m:mo> <m:mn>0.001</m:mn> </m:math> ). In conclusion, sentiments in nursing notes are associated with the in-hospital 28-day mortality and survival of sepsis patients.

Topics & Concepts

MedicineSepsisProportional hazards modelPathologicalMultivariate analysisHazard ratioIntensive care unitSAPS IIInternal medicineEmergency medicineAPACHE IIConfidence intervalMachine Learning in HealthcareSepsis Diagnosis and TreatmentHeart Rate Variability and Autonomic Control