Background: Manual data extraction of colonoscopy quality indicators is time and labor intensive. Natural language processing (NLP), a computer-based linguistics technique, can automate the extraction of important clinical information, such as adverse events, from unstructured free-text reports. NLP information extraction can facilitate the optimization of clinical work by helping to improve quality control and patient management. Objective: We developed an NLP pipeline to analyze free-text colonoscopy and pathology reports and evaluated its ability to automatically assess adenoma detection rate (ADR), sessile serrated lesion detection rate (SDR), and postcolonoscopy surveillance intervals. Methods: The NLP tool for extracting colonoscopy quality indicators was developed using a data set of 2000 screening colonoscopy reports from a single health care system, with an associated 1425 pathology reports. The NLP system was then tested on a data set of 1000 colonoscopy reports and its performance was compared with that of 5 human annotators. Additionally, data from 54,562 colonoscopies performed between 2010 and 2019 were analyzed using the NLP pipeline. Results: The NLP pipeline achieved an overall accuracy of 0.99-1.00 for identifying polyp subtypes, 0.99-1.00 for identifying the anatomical location of polyps, and 0.98 for counting the number of neoplastic polyps. The NLP pipeline achieved performance similar to clinical experts for assessing ADR, SDR, and surveillance intervals. NLP analysis of a 10-year colonoscopy data set identified great individual variance in colonoscopy quality indicators among 25 endoscopists. Conclusions: The NLP pipeline could accurately extract information from colonoscopy and pathology reports and demonstrated clinical efficacy for assessing ADR, SDR, and surveillance intervals in these reports. Implementation of the system enabled automated analysis and feedback on quality indicators, which could motivate endoscopists to improve the quality of their performance and improve clinical decision-making in colorectal cancer screening programs.

Background Effective surveillance of influenza requires a broad network of health care providers actively reporting cases of influenza-like illnesses and positive laboratory results. Not only is this traditional surveillance system costly to establish and maintain but there is also a time lag between a change in influenza activity and its detection. A new surveillance system that is both reliable and timely will help public health officials to effectively control an epidemic and mitigate the burden of the disease. Objective This study aimed to evaluate the use of parent-reported data of febrile illnesses in children submitted through the Fever Coach app in real-time surveillance of influenza activities. Methods Fever Coach is a mobile app designed to help parents and caregivers manage fever in young children, currently mainly serviced in South Korea. The app analyzes data entered by a caregiver and provides tailored information for care of the child based on the child’s age, sex, body weight, body temperature, and accompanying symptoms. Using the data submitted to the app during the 2016-2017 influenza season, we built a regression model that monitors influenza incidence for the 2017-2018 season and validated the model by comparing the predictions with the public influenza surveillance data from the Korea Centers for Disease Control and Prevention (KCDC). Results During the 2-year study period, 70,203 diagnosis data, including 7702 influenza reports, were submitted. There was a significant correlation between the influenza activity predicted by Fever Coach and that reported by KCDC (Spearman ρ=0.878; P<.001). Using this model, the influenza epidemic in the 2017-2018 season was detected 10 days before the epidemic alert announced by KCDC. Conclusions The Fever Coach app successfully collected data from 7.73% (207,699/2,686,580) of the target population by providing care instruction for febrile children. These data were used to develop a model that accurately estimated influenza activity measured by the central government agency using reports from sentinel facilities in the national surveillance network.

Background and Aims: The utility of clinical information from esophagogastroduodenoscopy (EGD) reports has been limited because of its unstructured narrative format. We developed a natural language processing (NLP) pipeline that automatically extracts information about gastric diseases from unstructured EGD reports and demonstrated its applicability in clinical research. Methods: An NLP pipeline was developed using 2000 EGD and associated pathology reports that were retrieved from a single healthcare center. The pipeline extracted clinical information, including the presence, location, and size, for 10 gastric diseases from the EGD reports. It was validated with 1000 EGD reports by evaluating sensitivity, positive predictive value (PPV), accuracy, and F1 score. The pipeline was applied to 248,966 EGD reports from 2010–2019 to identify patient demographics and clinical information for 10 gastric diseases. Results: For gastritis information extraction, we achieved an overall sensitivity, PPV, accuracy, and F1 score of 0.966, 0.972, 0.996, and 0.967, respectively. Other gastric diseases, such as ulcers, and neoplastic diseases achieved an overall sensitivity, PPV, accuracy, and F1 score of 0.975, 0.982, 0.999, and 0.978, respectively. The study of EGD data of over 10 years revealed the demographics of patients with gastric diseases by sex and age. In addition, the study identified the extent and locations of gastritis and other gastric diseases, respectively. Conclusions: We demonstrated the feasibility of the NLP pipeline providing an automated extraction of gastric disease information from EGD reports. Incorporating the pipeline can facilitate large-scale clinical research to better understand gastric diseases.

Background: The definition of alcohol hangovers refers to a combination of mental and physical side effects that occur after drinking. One of the ways that hangovers can be ameliorated is by promoting the rapid and effective elimination of acetaldehyde to alleviate the discomfort it causes. This study aimed to investigate the effects of GSH (yeast extract containing 50 mg of glutathione) on the hangover-relieving effect. Methods: A randomized double-blind placebo-controlled crossover clinical trial was conducted with 40 participants who reported experiencing hangover symptoms. Participants consumed alcohol at a rate of 0.78 g per kg body weight with 40% whiskey, adjusted according to their weight. Alcohol and acetaldehyde concentrations in serum were analyzed at 0, 0.25, 1, 2, 4, 6, and 15 h after alcohol consumption. Results: In the GSH group, the serum alcohol concentration decreased, although this change was not statistically significant. The serum acetaldehyde concentration was significantly lower in the GSH group in comparison to the placebo group (at 0.25, 1, 4, and 6 h (p < 0.01) and at 0.5, 2, and 15 h (p < 0.001) after alcohol consumption). However, there was no significant difference between the two groups on questionnaires such as the Acute Hangover Scale and the Alcohol Hangover Severity Scale. Conclusions: Overall, we consider the discovery that GSH lowered acetaldehyde concentration, a crucial factor in alcohol metabolism, to be more considerable. Therefore, GSH administration effectively reduces acetaldehyde levels in serum. This result suggests that this effect may contribute to the relief of hangover symptoms.