國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
APPENDIX 1 – Supportive paragraphs for participants
Food Borne Illness
[Task 1]
Food poisoning symptoms vary with the source of contamination. Most types of food poisoning cause nausea, vomiting, watery or bloody diarrhea, abdominal pain and cramps and fever.
Signs and symptoms may start within hours after eating the contaminated food, or they may begin days or even weeks later. Sickness caused by food poisoning generally lasts from a few hours to several days. Sometimes, serious complication happens. Whether you become ill after eating contaminated food depends on the organism, the amount of exposure, your age and your health. High-risk groups include: older adults, pregnant women, infants and young children, people with chronic disease highly affected by their immune system or changes in metabolism and circulation. Food poisoning is especially serious and potentially life-threatening for them. Further, preventions like keep raw foods separate from ready-to-eat foods, wash hands before eating, defrost foods safely, etc. are people can do at home.
Lisa just got a call from her aunt who is a nurse in the nearby hospital. There were a lot of students sent to the emergency this afternoon because of the foodborne. It is said that the food dealer did not check the expiration date of their meat and sent to several chain restaurants. Unluckily, Lisa’s favorite restaurant cooperates with the same food dealer and she just went there for brunch. Therefore, Lisa looked for the food contamination information on Google, just in case. The paragraph below this preface is what she found.
Please type down questions you may want to ask on the forum if you are in a similar situation. It’s ok to assume you are either Lisa or yourself to post.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
[Task 2]
Food poisoning syndrome results from ingestion of water and wide variety of food contaminated with pathogenic organisms (bacteria, viruses, parasites, and fungi), their toxin and chemicals. Food poisoning must be suspected when an acute illness with gastrointestinal or neurological manifestation affect two or more persons or animals who have shared a meal during the previous 72 hours. The term generally used encompasses both food related infection and food related intoxication. Some microbiologists consider microbial food poisoning to be different from food born infections. In microbial food poisoning, the microbes multiply readily in the food prior to consumption, whereas in food born infection, food is merely the vector for microbes that do not grow on their transient substrate. Other considers food poisoning as intoxication of food by chemicals or toxins from bacteria or fungi.
Food borne illness (FBI) often called food poisoning, it’s caused by pathogens or certain chemicals present in ingested food bacteria, viruses, molds, worms and protozoa causing diseases are all pathogens, although there are also harmless and beneficial bacteria that are used to make yogurt and cheese. Some chemicals that causes food borne illness are natural components of food, while other may be accidentally added during production and processing, either through carelessness or pollution. The two most common types of food borne illness are intoxication and infection. Intoxication occurs when toxin produced by the pathogens cause food poisoning, while infection is caused by the ingestion of food containing pathogens.
[Reference]
https://www.omicsonline.org/open-access/a-review-on-major-food-borne-bacterial-illnesses-2329-891X-1000176.pdf
https://www.mayoclinic.org/diseases-conditions/food-poisoning/symptoms-causes/syc-20356230
You are doing a term project related to foodborne illnesses. The professor asked you to organize some questions that can be discussed in class and posted them on the Discussion Online Board before next week class. It’s ok to share your opinion in the post. You may also propose questions like concepts you don’t understand after reading the supportive paragraph, or alternatively, guess what questions corresponds to it.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y Allergy
[Task 1]
Some people suffer with seasonal allergies for years before they learn that there are effective treatments. If allergy symptoms aren’t treated early, they can actually get worse over time.
Here are five symptoms you shouldn’t ignore: runny or stuffy nose, sinus pressure, sneezing, itchy eyes, and postnasal drip. You may avoid your allergy triggers or ask doctors other ways to get relief. Next, food allergy is an immune system reaction that occurs soon after eating a certain food. It's easy to confuse a food allergy with a much more common reaction known as food intolerance. While bothersome, food intolerance is a less serious condition that does not involve the immune system. Itching in the mouth, swelling of the lips, face, or other parts of the body, etc. are common signs of the food allergy. People who have similar symptoms should keep away from triggered food, for example, shellfish, peanuts, and fish.
Steven has a nasal allergy. When the weather changes, his illness gets worse. To sneeze and blow his nose, he even exhausted a tissue box in a short time yesterday. On the other hand, Steven found his 5-year-old son is allergic to seafood recently, especially crab and shrimp. If the food is not fresh enough, his son has a rash all over the body and feels an itchy. The symptom is totally different from him. Although they have eaten some medicine that can cure allergy, Steven is wondering if they need to see a doctor. The paragraph below is what he found on Google to have a brief overview. Please type down questions you may want to ask on the forum if you are in a similar situation. It’s ok to assume you are either Steven or yourself to post.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
[Task 2]
Allergies involve almost every organ of the body in variable combinations with a broad spectrum of possible symptoms, and thus their manifestations cover a wide range of phenotypes. Studies in Europe have shown that up to 30% of the population suffers from allergic rhinoconjunctivitis, while up to 20% suffer from asthma and 15% from allergic skin conditions. These numbers match those reported for other parts of the world, such as the USA and Australia. Food allergies, are becoming more frequent and severe; occupational allergies, drug allergies and allergies to insect stings (occasionally fatal), further aggravate the burden of the allergy epidemic. In contrast to the popular belief that allergies are mild conditions, a considerable and increasing proportion of patients (15%-20%) have severe, debilitating disease and are under constant fear of death from a possible asthma attack or anaphylactic shock. Within the EU, there are nevertheless wide geographical variations in the incidence of allergies with a south to north and east to west gradient. An alarming observation is that most allergic conditions start in childhood and peak during highly productive years of individuals, with allergic rhinitis affecting up to 45% of 20 to 40-year-old Europeans. The numbers may even be an underestimation, as many patients do not report their symptoms or are not properly diagnosed. Indeed, it is estimated that approximately 45% of patients have never received a diagnosis. Notwithstanding evidence suggesting a plateau in some areas, the European Academy of Allergy and Clinical Immunology (EAACI) warns that in less than 15 years more than half of the European population will suffer from some type of allergy!
[Reference]
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3539924/
https://www.webmd.com/allergies/features/allergy-symptoms#2
https://www.mayoclinic.org/diseases-conditions/food-allergy/symptoms-causes/syc-20355095
You are doing a term project related to allergy proportion in the world. The professor asked you to organize some questions that can be discussed in class and posted them on the Discussion Online Board before next week class. It’s ok to share your opinion in the post.
You may also propose questions like concepts you don’t understand after reading the supportive paragraph, or alternatively, guess what questions corresponds to it.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y Flu
[Task 1]
I. Seasonal influenza (or “flu”) is most often caused by type A or B influenza viruses.
Symptoms include sudden onset of fever, cough, headache, muscle and joint pain, sore throat and a runny nose. The cough can be severe and can last 2 or more weeks. Most people recover from fever and other symptoms within a week without requiring medical attention.
However, influenza can cause severe illness or death in high-risk groups.
II. Someone with the flu may have a high fever — for example, that person's temperature may be around 104°F (40°C). People with the flu often feel achy and extra tired. They may lose their appetites. The fever and aches usually disappear within a few days, but the sore throat, cough, stuffy nose, and tiredness may continue for a week or more. The flu also can cause vomiting, belly pain, and diarrhea. Most people who get the flu get better on their own after the virus runs its course. But call your doctor if you have the flu and any of these things happen:
(a) you're getting worse instead of better (b) you have trouble breathing or develop other complications, such as a sinus infection (c) you have a medical condition (for example, diabetes, heart problems, asthma, or other lung problems). Most teens can take acetaminophen or ibuprofen to help with fever and aches.
There are one in four students of Tommy’s school got the flu, so the junior school committee claimed closing the school for disinfection is necessary. Tommy’s Mom was concerned with symptoms of this flu because she forgot to let Tommy get the vaccination this year. Please type down questions you may want to ask on the forum if you are in a similar situation. It’s ok to assume you are either Tommy’s parent or yourself to the post. The following information seems useful to Tommy’s Mom. Feel free to refer to it if you have no ideas what to say.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
[Task 2]
What scientists dream of is a vaccine that can protect against any flu strain for years or even a lifetime. This so-called universal flu vaccine is still a long way off, if it's even possible. But many labs are dusting off past projects on broad flu vaccines, spurred by new funding and fears that H5N1, the deadly avian influenza that has swept across half the world, could acquire the ability to be transmitted from human to human. Until now, “flu has never been before high enough on the radar screen” for companies in particular to follow through with a strong push for a universal vaccine, says Gary Nabel, director of the Vaccine Research Center at the U.S.
National Institute of Allergy and Infectious Diseases (NIAID) in Bethesda, Maryland.
Doing so, however, means coming up with an alternative way to stimulate immunity to the virus. The tried-and-true technique for seasonal flu uses a killed virus vaccine that works mainly by triggering antibodies to hemagglutinin (HA), the glycoprotein on the virus's surface that it uses to bind to human cells. Hemagglutinin and neuraminidase (NA), another surface glycoprotein that helps newly made viruses exit cells, give strains their names (H5N1, for example). The sequences of HA and NA mutate easily, which is why each season's flu strain—
although it may be the same in subtype, such as H3N2— “drifts” slightly from the previous year's, and the annual vaccine must be tailor-made.
To make a universal vaccine for influenza A, which includes the main seasonal flu strains and bird flu, as well as past pandemic strains, some scientists are hoping to use “conserved” flu proteins that don't mutate much year to year. (Influenza B, the other type, occurs only in humans and causes milder symptoms.) Some of the conserved protein vaccines in the works stimulate production of antibodies as do conventional flu vaccines, whereas others rouse certain immune system cells to battle the virus.
[Reference]
http://science.sciencemag.org/content/312/5772/380 http://www.who.int/features/qa/seasonal-influenza/en/
https://kidshealth.org/en/teens/flu.html
You are doing a term project related to the flu. The professor asked you to organize some questions with details that can be discussed in class and posted them on the Discussion Online Board before next week class. It’s ok to share your opinion in the post. You may also propose questions like concepts that you don’t understand after reading the supportive paragraph, or alternatively, guess what questions will be provided by the human society to find information like the following paragraphs.
‧
Agichtein, E., Castillo, C., Donato, D., Gionis, A., & Mishne, G. (2008). Finding high-quality content in social media. Proceedings of the International Conference on Web Search and
Web Data Mining - WSDM ’08, 183.
Aigner, M., & Ziegler, G. M. (2018). Completing Latin squares. Proofs from THE BOOK, 1–
326.
Arora, S., Li, Y., Liang, Y., Ma, T., & Risteski, A. (2016). A latent variable model approach to PMI-based word embeddings. Transactions of the Association for Computational
Linguistics, 4, 385–399.
Baltadzhieva, A. (2015). Question quality in community question answering forums : a survey.
Sigkdd, 17(1), 8–13.
Beel, J., Langer, S., Genzmehr, M., Gipp, B., Breitinger, C., & Nurnberger, A. (2013).
Research paper recommender system evaluation : A quantitative literature survey.
Proceedings of the Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys) at the ACM Recommender System Conference, 20(April
2013), 15–22.Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey.
Knowledge-Based Systems, 46, 109–132.
Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and
User-Adapted Interaction, 12. https://doi.org/10.1023/A:1021240730564
Denecke, K., & Nejdl, W. (2009). How valuable is medical social media data? Content analysis of the medical web. Information Sciences, 179(12), 1870–1880.
Dror, G., Koren, Y., Maarek, Y., & Szpektor, I. (2010). I want to answer, who has a question?
Yahoo! Answers recommender system. Proceedings of the 17th ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining.
Feng, M., Xiang, B., Glass, M. R., Wang, L., & Zhou, B. (2016). Applying deep learning to answer selection: A study and an open task. In 2015 IEEE Workshop on Automatic Speech
Recognition and Understanding, ASRU 2015 - Proceedings.
Fox, S. (2013). Health Online 2013: 35% of U.S. adults have gone online to figure out a medical condition; of these, half followed up with a visit to a medical professional. PEW
INTERNET & AMERICAN LIFE PROJECT, (January).
Fox, S., & Fallows, D. (2003). Internet health resources: Health searches and email have become more commonplace , but there is room for improvement in searches and overall Internet access Findings. PEW INTERNET & AMERICAN LIFE PROJECT, (July).
Gittens, A., Achlioptas, D., & Mahoney, M. W. (2017). Skip-Gram - Zipf + Uniform = Vector Additivity. Proceedings of the 55th Annual Meeting of the Association for
Computational Linguistics (Volume 1: Long Papers), 69–76.
Höchstötter, N., & Lewandowski, D. (2006). What users see structures in search engine results pages.
Isern, D., & Moreno, A. (2016). A systematic literature review of agents applied in healthcare.
J Med Syst., 40(2).
Jeon, J., Croft, W. B., & Lee, J. H. (2005). Finding similar questions in large question and answer archives. Proceedings of the 14th ACM International Conference on Information
and Knowledge Management - CIKM ’05, 84.
John, M. (1996). A new statistical parser based on bigram lexical dependencies. Science, 184–
191.
Kim, J.-H., Lee, J.-H., Park, J.-S., Lee, Y., & Rim, K. (2009). Design of diet recommendation
system for healthcare service based on user information. Convergence Information
Technology, International Conference on (Vol. 0).
‧
Lai, S., Liu, K., He, S., & Zhao, J. (2016). How to generate a good word embedding. IEEE
Intelligent Systems, 31(6), 5–14.
Li, B., Jin, T., Lyu, M. R., King, I., & Mak, B. (2012). Analyzing and predicting question quality in community question answering services. Proceedings of the 21st International
Conference Companion on World Wide Web - WWW ’12 Companion, 775.
Li, B., & King, I. (2010). Routing questions to appropriate answerers in community question answering services. In Proceedings of the 19th ACM international conference on
Information and knowledge management - CIKM ’10.
Liang, K.-Y., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models.
Biometrika, 73(1), 13–22.
Lopez-Nores, M., Blanco-Fern´ndez, Y., Pazos-Arias, J. J., Garcia-Duque, J., & Martin-Vicente, M. I. (2011). Enhancing recommender systems with access to electronic health records and groups of interest in social networks. In 2011 Seventh International
Conference on Signal Image Technology & Internet-Based Systems (pp. 105–110).
Mccray, A. T., Loane, R. F., Browne, A. C., & Bangalore, A. K. (1999). Terminology issues in user access to web-based medical information. Proceedings of AMIA Symposium 1999, (October), 107–111.
McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22(3), 276–282.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Proceedings of the 26th International Conference
on Neural Information Processing Systems, 2, 3111–3119.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review. US: American Psychological Association.
Mimno, D., & Thompson, L. (2017). The strange geometry of skip-gram with negative sampling. Emnlp 2017, 2863–2868.
Morrell, T. G., & Kerschberg, L. (2012). Personal health explorer: a semantic health recommendation system. In 2012 IEEE 28th International Conference on Data
Engineering Workshops (pp. 55–59).
Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert Systems with Applications, 39(11), 10059–
10072.
Pattaraintakorn, P., Zaverucha, G. M., & Cercone, N. (2007). Web based health recommender system using rough sets, survival analysis and rule-based expert systems. In A. An, J.
Stefanowski, S. Ramanna, C. J. Butz, W. Pedrycz, & G. Wang (Eds.) (pp. 491–499).
Berlin, Heidelberg: Springer Berlin Heidelberg.
Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2008). A design science research methodology for information systems research. Journal of Management
Information Systems, 24(3), 45–77.
Pu, P., Chen, L., & Hu, R. (2011). A user-centric evaluation framework for recommender systems. In Proceedings of the Fifth ACM Conference on Recommender Systems (pp.
157–164). New York, NY, USA: ACM.
Riahi, F., Zolaktaf, Z., Shafiei, M., & Milios, E. (2012). Finding expert users in community question answering. Proceedings of the 21st International Conference Companion on
World Wide Web - WWW ’12 Companion, (i), 791.
Rose, D. E., & Levinson, D. (2004). Understanding user goals in web search. Proceedings of
WWW 2004, 13–19.
Sami, A., Nagatomi, R., Terabe, M., & Hashimoto, K. (2008). Design of physical activity recommendation system.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
Sapatinas, T. (2004). The elements of statistical learning. Journal of the Royal Statistical
Society: Series A (Statistics in Society), 167(1), 192–192.
Sezgin, E., & Özkan, S. (2013). A systematic literature review on health recommender systems.
In The 4th IEEE International Conference on E-Health and Bioengineering.
Shen, Y., Rong, W., Sun, Z., Ouyang, Y., & Xiong, Z. (2015). Question/answer matching for cqa system via combining lexical and sequential information. Proceedings of the
Twenty-Ninth AAAI Conference on Artificial Intelligence, 275–281.
Viera, A. J., & Garrett, J. M. (2005). Understanding interobserver agreement: The kappa statistic. Family Medicine, 37(5), 360–363.
Wiesner, M., & Pfeifer, D. (2010). Adapting recommender systems to the requirements of personal health record systems. IHI’10 - Proceedings of the 1st ACM International Health
Informatics Symposium.
Wildemuth, B. B. M., Ph, D., Friedman, C. P., Ph, D., Hill, C., & Carolina, N. (1994).
lnformation seeking behaviors of medical students : A classification of questions asked of librarians and physicians. Bulletin of Medical Library Association, 82(July), 295–304.
Williams, R. L., & Cothrel, J. (2000). Four smart ways to run online communities.
Zeng-treitler, Q., Kogan, S., Ash, N., & Greenes, R. A. (2002). Characteristics of consumer terminology for health information retrieval. Methods of Information in Medicine,
41(February), 289–298.
Zhang, Y. (2010). Contextualizing consumer health information searching : An analysis of questions in a social q & a community. Proceedings of the 1st ACM International Health
Informatics Symposium, 210–219.
Zhao, J., Collins, C., Chevalier, F., & Balakrishnan, R. (2013). Interactive exploration of implicit and explicit relations in faceted datasets. IEEE Transactions on Visualization and
Computer Graphics, 19(12), 2080–2089.
Zhou, G., He, T., Zhao, J., & Hu, P. (2015). Learning continuous word embedding with metadata for question retrieval in community question answering. Acl, 250–259.