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1. Determining Associations with Word Embedding in Heterogeneous Network for Detecting Off-label Drug Uses

   https://doi.org/DOI 10.1109/ICHI.2017.78  

   Yang, Christopher C.; Zhao, Mengnan (January 2017, IEEE International Conference on Healthcare Informatics 2017)

   Off-label drug use is quite common in clinical practice and inevitable to some extent. Such uses might deliver effective treatment and suggest clinical innovation sometimes, however, they have the unknown risk to cause serious outcomes due to lacking scientific support. As gaining information about off-label drug use could present a clue to the stakeholders such as healthcare professionals and medication manufacturers to further the investigation on drug efficacy and safety, it raises the need to develop a systematic way to detect off-label drug uses. Considering the increasing discussions in online health communities (OHCs) among the health consumers, we proposed to harness the large volume of timely information in OHCs to develop an automated method for detecting off-label drug uses from health consumer generated data. From the text corpus, we extracted medical entities (diseases, drugs, and adverse drug reactions) with lexicon-based approaches and measured their interactions with word embedding models, based on which, we constructed a heterogeneous healthcare network. We defined several meta-path-based indicators to describe the drug-disease associations in the heterogeneous network and used them as features to train a binary classifier built on Random Forest algorithm, to recognize the known drug-disease associations. The classification model obtained better results when incorporating word embedding features and achieved the best performance when using both association rule mining features and word embedding features, with F1-score reaching 0.939, based on which, we identified 2,125 possible off-label drug uses and checked their potential by searching evidence in PubMed and FAERS. 

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2. Advice from a Doctor or AI? Understanding Willingness to Disclose Information Through Remote Patient Monitoring to Receive Health Advice

   https://doi.org/10.1145/3686925  

   Mendel, Tamir; Nov, Oded; Wiesenfeld, Batia (November 2024, Proceedings of the ACM on Human-Computer Interaction)

   Remote Patient Monitoring (RPM) devices transmit patients' medical indicators (e.g., blood pressure) from the patient's home testing equipment to their healthcare providers, in order to monitor chronic conditions such as hypertension. AI systems have the potential to enhance access to timely medical advice based on the data that RPM devices produce. In this paper, we report on three studies investigating how the severity of users' medical condition (normal vs. high blood pressure), security risk (low vs. modest vs. high risk), and medical advice source (human doctor vs. AI) influence user perceptions of advisor trustworthiness and willingness to disclose RPM-acquired information. We found that trust mediated the relationship between the advice source and users' willingness to disclose health information: users trust doctors more than AI and are more willing to disclose their RPM-acquired health information to a more trusted advice source. However, we unexpectedly discovered that conditional on trust, users disclose RPM-acquired information more readily to AI than to doctors. We observed that the advice source did not influence perceptions of security and privacy risks. We conclude by discussing how our findings can support the design of RPM applications. 

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3. Join, Stay or Go?: A Closer Look at Members' Life Cycles in Online Health Communities

   https://doi.org/10.1145/3449245  

   Yao, Zheng; Yang, Diyi; Levine, John_M; Low, Carissa_A; Smith, Tenbroeck; Zhu, Haiyi; Kraut, Robert_E (April 2021, Proceedings of the ACM on Human-Computer Interaction)

   Online health communities (OHCs) have become important resources from which members can obtain social support. Since most benefits of OHCs are provided by its members, it is crucial that OHCs maintain a critical mass of active members. This paper examines temporal changes in members' participation in a cancer-orientedOHC, focusing on the changes in members' motivations and behavior as they transition from newcomers to other roles or when they ultimately leave the community. Our work used mixed methods, combining behavioral log analysis, automated content analysis, surveys and interviews. We found that shifts in members' motivations seemed to be driven by two sources: the internal dynamics common to becoming a member of most online communities and the external needs associated with their cancer journey. When members' disease-driven needs for support decreased, most members quit the site. The motivations of those who stayed shifted from receiving support to providing it to others in the community. As in many online communities, old-timers contributed the vast majority of content. However, they encountered challenges that threatened their commitment, including negative emotions related to other members' deaths, which led them to take leaves of absence from the community or to drop out permanently. Implications for the motivation changes ofOHC members are discussed. 

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4. Let's Talk about Sext: How Adolescents Seek Support and Advice about Their Online Sexual Experiences

   https://doi.org/10.1145/3313831.3376400  

   Razi, Afsaneh; Badillo-Urquiola, Karla; Wisniewski, Pamela J. (April 2020, Let’s Talk about Sext: How Teens Seek Support and Advice for Online Sexual Interactions)

   We conducted a thematic content analysis of 4,180 posts by adolescents (ages 12-17) on an online peer support mental health forum to understand what and how adolescents talk about their online sexual interactions. Youth used the platform to seek support (83%), connect with others (15%), and give advice (5%) about sexting, their sexual orientation, sexual abuse, and explicit content. Females often received unwanted nudes from strangers and struggled with how to turn down sexting requests from people they knew. Meanwhile, others who sought support complained that they received unwanted sexual solicitations while doing so—to the point that adolescents gave advice to one another on which users to stay away from. Our research provides insight into the online sexual experiences of adolescents and how they seek support around these issues. We discuss how to design peer-based social media platforms to support the well-being and safety of youth. 

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5. Automated Off-label Drug Use Detection from User Generated Content

   https://doi.org/http://dx.doi.org/10.1145/3107411.3107475  

   Zhao, Mengnan; Yang, Christopher C. (January 2017, ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics)

   Off-label drug use refers to using marketed drugs for indications that are not listed in their FDA labeling information. Such uses are very common and sometimes inevitable in clinical practice. To some extent, off-label drug uses provide a pathway for clinical innovation, however, they could cause serious adverse effects due to lacking scientific research and tests. Since identifying the off-label uses can provide a clue to the stakeholders including healthcare providers, patients, and medication manufacturers to further the investigation on drug efficacy and safety, it raises the demand for a systematic way to detect off-label uses. Given data contributed by health consumers in online health communities (OHCs), we developed an automated approach to detect off-label drug uses based on heterogeneous network mining. We constructed a heterogeneous healthcare network with medical entities (e.g. disease, drug, adverse drug reaction) mined from the text corpus, which involved 50 diseases, 1,297 drugs, and 185 ADRs, and determined 13 meta paths between the drugs and diseases. We developed three metrics to represent the meta-path-based topological features. With the network features, we trained the binary classifiers built on Random Forest algorithm to recognize the known drug-disease associations. The best classification model that used lift to measure path weights obtained F1-score of 0.87, based on which, we identified 1,009 candidates of off-label drug uses and examined their potential by searching evidence from PubMed and FAERS. 

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