Automated Prediction of Demographic Information from Medical User Reviews

Tutubalina, Elena; Nikolenko, Sergey I.

doi:10.1007/978-3-319-58130-9_17

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…Previous studies aimed at data analysis of medical forums focused on sentiment analysis (Bobicev & Sokolova, 2018), identification of possible adverse events (Metke‐Jimenez & Karimi, 2016), automated prediction of demographic information (Tutubalina & Nikolenko, 2017) and various strategies to extract information. To the best of our knowledge, this is the first study that used scraping methods to extract in bulk questions asked by a specific patient population (BNP patients) from online forums, and to categorize them with algorithms that include word frequency analysis, explicit‐fear related themes and to categorize the findings.…”

Section: Discussionmentioning

confidence: 99%

Individuals with back and neck pain on medical forums: What do they mention? What do they fear?

Mintz

Weisman

Springer

et al. 2020

European Journal of Pain

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Background: The use of online medical forums is on the rise globally. Data scraping is a method of extracting website content using an automated computer program. We scraped users' questions regarding back and neck pain (BNP) from popular Israeli online medical forums. We aimed to identify the sort of questions being asked about BNP, and to analyse explicit themes that characterize their questions. Methods: Six leading Israeli BNP forums were identified. In phase 1, Python scripts scraped 12,418 questions into a data set. In phase 2-five themes were identified: Surgery (n = 2,957); health care professions (n = 2,361); Sports (n = 2,304); drugs (n = 1,419) and interpretation of imaging (n = 845). Phase 3-included the categorization of explicit fear-related words by the authors. Phase 4-analysis of explicit fear-related themes yielded 402 questions. Results: Gender was identified for 394 users, and age was identified for 181 users. A total of 248 users (61.6%) were women and 146 men (36.3%). Mean age 36.3 ± 16.15 for women and 35.5 ± 16.1 for men. The most commonly expressed fears were related to: invasive procedures, 30.9% (131 questions); fear of serious condition or misdiagnosis, 17.0% (72 questions); General concerns, 13.7% (58 questions); fear of worsening or relapse, 12.3% (52 questions); adverse effects of oral drugs or radiation, 10.8% (46 questions) and concerns related to lifestyle, 9.7% (41 questions). Conclusions: Web scraping is a feasible strategy with which to explore medical forums and the above-mentioned themes, all of which are of potential clinical significance. Significance: Using automated algorithms, a total of 12,369 questions from online back and neck medical forums were scraped and analysed. Secondary analysis categorized fear-related themes that were mentioned by users. Identifying and addressing patients' fear has potential to improve communication and therapeutic outcome. For example, questions regarding surgery were typically asked after the option was mentioned by a physician. This insight should encourage physicians to devote extra time explaining the possible implications of surgery, should they consider it as an option.

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Section: Discussionmentioning

confidence: 99%

Individuals with back and neck pain on medical forums: What do they mention? What do they fear?

Mintz

Weisman

Springer

et al. 2020

European Journal of Pain

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“…We also note that such demographic information is not commonly provided in discussion groups and websites. In a recent study [ 60 ], several approaches to automated mining of demographic information from texts about drugs were evaluated including neural networks, supervised machine learning, and topic modeling.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews

Tutubalina

Nikolenko

2017

Journal of Healthcare Engineering

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Adverse drug reactions (ADRs) are an essential part of the analysis of drug use, measuring drug use benefits, and making policy decisions. Traditional channels for identifying ADRs are reliable but very slow and only produce a small amount of data. Text reviews, either on specialized web sites or in general-purpose social networks, may lead to a data source of unprecedented size, but identifying ADRs in free-form text is a challenging natural language processing problem. In this work, we propose a novel model for this problem, uniting recurrent neural architectures and conditional random fields. We evaluate our model with a comprehensive experimental study, showing improvements over state-of-the-art methods of ADR extraction.

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