Despite recent methodology advancements in clinical natural language processing (NLP), the adoption of clinical NLP models within the translational research community remains hindered by process heterogeneity and human factor variations. Concurrently, these factors also dramatically increase the difficulty in developing NLP models in multi-site settings, which is necessary for algorithm robustness and generalizability. Here, we reported on our experience developing an NLP solution for Coronavirus Disease 2019 (COVID-19) signs and symptom extraction in an open NLP framework from a subset of sites participating in the National COVID Cohort (N3C). We then empirically highlight the benefits of multi-site data for both symbolic and statistical methods, as well as highlight the need for federated annotation and evaluation to resolve several pitfalls encountered in the course of these efforts.

The COVID-19 pandemic swept across the world rapidly, infecting millions of people. An efficient tool that can accurately recognize important clinical concepts of COVID-19 from free text in electronic health records (EHRs) will be valuable to accelerate COVID-19 clinical research. To this end, this study aims at adapting the existing CLAMP natural language processing tool to quickly build COVID-19 SignSym, which can extract COVID-19 signs/symptoms and their 8 attributes (body location, severity, temporal expression, subject, condition, uncertainty, negation, and course) from clinical text. The extracted information is also mapped to standard concepts in the Observational Medical Outcomes Partnership common data model. A hybrid approach of combining deep learning-based models, curated lexicons, and pattern-based rules was applied to quickly build the COVID-19 SignSym from CLAMP, with optimized performance. Our extensive evaluation using 3 external sites with clinical notes of COVID-19 patients, as well as the online medical dialogues of COVID-19, shows COVID-19 SignSym can achieve high performance across data sources. The workflow used for this study can be generalized to other use cases, where existing clinical natural language processing tools need to be customized for specific information needs within a short time. COVID-19 SignSym is freely accessible to the research community as a downloadable package (https://clamp.uth.edu/covid/nlp.php) and has been used by 16 healthcare organizations to support clinical research of COVID-19.

Bots (i.e. automated accounts) involve in social campaigns typically for two obvious reasons: to inorganically sway public opinion and to build social capital exploiting the organic popularity of social campaigns. In the process, bots interact with each other and engage in human activities (e.g. likes, retweets, and following).

In an online review system, a user writes a review with the intention of helping fellow consumers (i.e. the readers) to make informed decisions. However, product owners often provide incentives (e.g. coupons, bonus points, referral rewards) to the writers, motivating the writing of biased reviews. These biased reviews, while beneficial for both writers and product owners, pollute the review space and destroy readers' trust significantly. In this paper, we analyze incentivized reviews in the Google Play store and identify a wide range of anomalous review types such as copying, spamming, advertising, and hidden-beneficiary reviews. We also find an increasing trend in the number of apps being targeted by abusers, which, if continued, will render review systems as crowd advertising platforms rather than an unbiased source of helpful information.

How can we automatically clean and curate online reviews to better mine them for knowledge discovery? Typical online reviews are full of noise and abnormalities, hindering semantic analysis and leading to a poor customer experience. Abnormalities include non-standard characters, unstructured punctuation, different/multiple languages, and misspelled words. Worse still, people will leave “junk” text, which is either completely nonsensical, spam, or fraudulent. In this paper, we describe three types of noisy and abnormal reviews, discuss methods to detect and filter them, and, finally, show the effectiveness of our cleaning process by improving the overall distributional characteristics of review datasets.

Dynamic Time Warping (DTW) distance has been effectively used in mining time series data in a multitude of domains. However, in its original formulation DTW is extremely inefficient in comparing long sparse time series, containing mostly zeros and some unevenly spaced non-zero observations. Original DTW distance does not take advantage of this sparsity, leading to redundant calculations and a prohibitively large computational cost for long time series. We derive a new time warping similarity measure (AWarp) for sparse time series that works on the run-length encoded representation of sparse time series. The complexity of AWarp is quadratic on the number of observations as opposed to the range of time of the time series. Therefore, AWarp can be several orders of magnitude faster than DTW on sparse time series. AWarp is exact for binary-valued time series and a close approximation of the original DTW distance for any-valued series. We discuss useful variants of AWarp: bounded (both upper and lower), constrained, and multidimensional. We show applications of AWarp to three data mining tasks including clustering, classification, and outlier detection, which are otherwise not feasible using classic DTW, while producing equivalent results. Potential areas of application include bot detection, human activity classification, and unusual review pattern mining.