Monitoring COVID-19 on Social Media: Development of an End-to-End Natural Language Processing Pipeline Using a Novel Triage and Diagnosis Approach (Preprint)

BACKGROUND The COVID-19 pandemic has created a pressing need for integrating information from disparate sources in order to assist decision makers. Social media is important in this respect; however, to make sense of the textual information it provides and be able to automate the processing of large amounts of data, natural language processing methods are needed. Social media posts are often noisy, yet they may provide valuable insights regarding the severity and prevalence of the disease in the population. Here, we adopt a triage and diagnosis approach to analyzing social media posts using machine learning techniques for the purpose of disease detection and surveillance. We thus obtain useful prevalence and incidence statistics to identify disease symptoms and their severities, motivated by public health concerns. OBJECTIVE This study aims to develop an end-to-end natural language processing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts in order to provide researchers and public health practitioners with additional information on the symptoms, severity, and prevalence of the disease rather than to provide an actionable decision at the individual level. METHODS The text processing pipeline first extracted COVID-19 symptoms and related concepts, such as severity, duration, negations, and body parts, from patients’ posts using conditional random fields. An unsupervised rule-based algorithm was then applied to establish relations between concepts in the next step of the pipeline. The extracted concepts and relations were subsequently used to construct 2 different vector representations of each post. These vectors were separately applied to build support vector machine learning models to triage patients into 3 categories and diagnose them for COVID-19. RESULTS We reported macro- and microaveraged F&lt;sub&gt;1&lt;/sub&gt; scores in the range of 71%-96% and 61%-87%, respectively, for the triage and diagnosis of COVID-19 when the models were trained on human-labeled data. Our experimental results indicated that similar performance can be achieved when the models are trained using predicted labels from concept extraction and rule-based classifiers, thus yielding end-to-end machine learning. In addition, we highlighted important features uncovered by our diagnostic machine learning models and compared them with the most frequent symptoms revealed in another COVID-19 data set. In particular, we found that the most important features are not always the most frequent ones. CONCLUSIONS Our preliminary results show that it is possible to automatically triage and diagnose patients for COVID-19 from social media natural language narratives, using a machine learning pipeline in order to provide information on the severity and prevalence of the disease for use within health surveillance systems. </sec

Monitoring COVID-19 on Social Media: Development of an End-to-End Natural Language Processing Pipeline Using a Novel Triage and Diagnosis Approach

Background The COVID-19 pandemic has created a pressing need for integrating information from disparate sources in order to assist decision makers. Social media is important in this respect; however, to make sense of the textual information it provides and be able to automate the processing of large amounts of data, natural language processing methods are needed. Social media posts are often noisy, yet they may provide valuable insights regarding the severity and prevalence of the disease in the population. Here, we adopt a triage and diagnosis approach to analyzing social media posts using machine learning techniques for the purpose of disease detection and surveillance. We thus obtain useful prevalence and incidence statistics to identify disease symptoms and their severities, motivated by public health concerns. Objective This study aims to develop an end-to-end natural language processing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts in order to provide researchers and public health practitioners with additional information on the symptoms, severity, and prevalence of the disease rather than to provide an actionable decision at the individual level. Methods The text processing pipeline first extracted COVID-19 symptoms and related concepts, such as severity, duration, negations, and body parts, from patients’ posts using conditional random fields. An unsupervised rule-based algorithm was then applied to establish relations between concepts in the next step of the pipeline. The extracted concepts and relations were subsequently used to construct 2 different vector representations of each post. These vectors were separately applied to build support vector machine learning models to triage patients into 3 categories and diagnose them for COVID-19. Results We reported macro- and microaveraged F1 scores in the range of 71%-96% and 61%-87%, respectively, for the triage and diagnosis of COVID-19 when the models were trained on human-labeled data. Our experimental results indicated that similar performance can be achieved when the models are trained using predicted labels from concept extraction and rule-based classifiers, thus yielding end-to-end machine learning. In addition, we highlighted important features uncovered by our diagnostic machine learning models and compared them with the most frequent symptoms revealed in another COVID-19 data set. In particular, we found that the most important features are not always the most frequent ones. Conclusions Our preliminary results show that it is possible to automatically triage and diagnose patients for COVID-19 from social media natural language narratives, using a machine learning pipeline in order to provide information on the severity and prevalence of the disease for use within health surveillance systems. </jats:sec

P114 PERIANAL EXAMINATION AT TIME OF COLONOSCOPY - A MISSED OPPORTUNITY FOR IBD ASSESSMENT

Abstract Introduction The subtype of inflammatory bowel disease (IBD) is often not known at index colonoscopy and examining for perianal disease (PAD) can assist in establishing the diagnosis. PAD is common in Crohn’s disease (CD) but can also be seen in patients with ulcerative colitis (UC) in the form of fissures, abscesses and fistulae. Absence of perianal symptoms does not exclude PAD. Performing a routine perianal examination in a busy outpatient setting is not ideal and the endoscopy suite may be more appropriate. We hypothesise that perianal examinations are being omitted during IBD assessment colonoscopy. Methods Unisoft GI Reporting Tool was used to identify the last 70 consecutive CD and UC assessment colonoscopies performed over a 12 month period (August 2018 and July 2019) at a London-based district general hospital. Data was collected on demographics, known PAD, previous imaging and performance of perianal examination at colonoscopy. Results 140 patients undergoing colonoscopy for IBD assessment were included in this study (70 CD, 70 UC).Median age 42 (IQR 32 – 55), Female 66 (47.1%). 15 (10.7%) had known perianal disease. Pelvic MRI had previously been performed in 20(14.3%). Perianal examination was performed in only 3 (2%) patients at the time of their last clinic consultation. Although digital rectal examination (DRE) was performed in 132 (93.6%) of patients at the time of colonoscopy, only 9 (6.4%) had a perianal examination documented. Conclusion About 10% of patients in our cohort undergoing IBD assessment colonoscopy were known to have PAD but perianal examination was performed in only 2% of patients during clinic consultation and 6% during colonoscopy. Perianal examination at time of endoscopic assessment is an ideal setting to perform an intimate examination as you have an exposed, sedated and chaperoned patient. The omission of perianal examination at colonoscopy is a missed opportunity and improvement in this key element of disease assessment is required. </jats:sec

P012 HOSPITAL RE-ADMISSION IN PATIENTS WITH INFLAMMATORY BOWEL DISEASE – WHAT ARE THE RISK FACTORS?

Abstract Introduction Early re-admission after hospitalisation for an inflammatory bowel disease (IBD) flare is a negative quality indicator and causes unnecessary healthcare expense. Scoring systems to predict IBD readmissions have been shown to be ineffective. We aimed to describe the IBD re-admission rate at our hospital and investigate the risk factors. Methods Retrospective study of patients admitted to a London-based district general hospital under the gastroenterology team with a flare of inflammatory bowel disease between 2015 and 2018. Characteristic including but not limited to demographics, disease type, length of stay during index admission, biochemistry and biologic use were recorded. Hospital software (Sunquest Integrated Clinical Environment, Medway) was used to identify patients re-admitted at 30 and 90 days after discharge. Multivariate logistic regression was performed. Results 138 patients were admitted with an IBD flare during the study period (74 (53.6%) Crohn’s disease (CD), 56 (40.6%) ulcerative colitis (UC), 8 (5.8%) IBD-U). Median age 33.5 (IQR 26 – 52), 71 (51.4%) female. Median length of stay was 4.5 days (IQR 1.8 – 8). 36 (26%) patients were taking a biologic. Re-admissions occurred within 30 days in 19 patients (13.7%) and within 90 days in 30 patients (21.7%). Multivariate logistic regression showed that a raised CRP on discharge was associated with re-admission. For every increased unit of CRP by one there was an increased risk of readmission by 1.1 times (p=0.05). Patients aged 22–39 were significantly less likely to be readmitted (OR: 0.38, p=0.015). Male patients were significantly more likely to be readmitted (OR: 2.52, p=0.05). Conclusion The 30 day and 90 day re-admission rate for our IBD population is just over 10% and 20%, respectively. CRP at discharge is significantly associated with both 30 and 90 day re-admission. After adjusting for confounders; CRP, age older than 40 and male gender were associated with re-admission to hospital. We advise caution in discharging IBD patients with raised inflammatory markers. Close follow up within a few days of discharge would be appropriate in this high risk sub-group. </jats:sec