Machine Learning: Review

Machine Learning is that the core subarea of AI . It makes computers get into a self-learning mode without explicit programming. When fed new data, these computers learn, grow, change, and develop by themselves. The concept of machine learning has been around for a short time now. However, the power to automatically and quickly apply mathematical calculations to big data is now gaining a touch of momentum. Machine learning has been utilized in several places just like the self-driving Google car, the web recommendation engines – friend recommendations on Facebook, offer suggestions from Amazon, and in cyber fraud detection. In this paper, we will learn basic terms in machine learning.

Exploring Types of Information Sources Used When Choosing Doctors: Observational Study in an Online Health Care Community

Background Patients attempt to make appropriate decisions based on their own knowledge when choosing a doctor. In this process, the first question usually faced is that of how to obtain useful and relevant information. This study investigated the types of information sources that are used widely by patients in choosing a doctor and identified ways in which the preferred sources differ in various situations. Objective This study aims to address the following questions: (1) What is the proportion in which each of the various information sources is used? (2) How does the information source preferred by patients in choosing a doctor change when there is a difference in the difficulty of medical decision making, in the level of the hospital, or in a rural versus urban situation? (3) How do information sources used by patients differ when they choose doctors with different specialties? Methods This study overcomes a major limitation in the use of the survey technique by employing data from the Good Doctor website, which is now China's leading online health care community, data which are objective and can be obtained relatively easily and frequently. Multinomial logistic regression models were applied to examine whether the proportion of use of these information sources changes in different situations. We then used visual analysis to explore the question of which type of information source patients prefer to use when they seek medical assistance from doctors with different specialties. Results The 3 main information sources were online reviews (OR), family and friend recommendations (FR), and doctor recommendations (DR), with proportions of use of 32.93% (559,345/1,698,666), 23.68% (402,322/1,698,666), and 17.48% (296,912/1,698,666), respectively. Difficulty in medical decision making, the hospital level, and rural-urban differences were significantly associated with patients’ preferred information sources for choosing doctors. Further, the sources of information that patients prefer to use were found to vary when they looked for doctors with different medical specialties. Conclusions Patients are less likely to use online reviews when medical decisions are more difficult or when the provider is not a tertiary hospital, the former situation leading to a greater use of online reviews and the latter to a greater use of family and friend recommendations. In addition, patients in large cities are more likely to use information from online reviews than family and friend recommendations. Among different medical specialties, for those in which personal privacy is a concern, online reviews are the most common source. For those related to children, patients are more likely to refer to family and friend recommendations, and for those related to surgery, they value doctor recommendations more highly. Our results can not only contribute to aiding government efforts to further promote the dissemination of health care information but may also help health care industry managers develop better marketing strategies.

Exploring Types of Information Sources Used When Choosing Doctors: Observational Study in an Online Health Care Community (Preprint)

BACKGROUND Patients attempt to make appropriate decisions based on their own knowledge when choosing a doctor. In this process, the first question usually faced is that of how to obtain useful and relevant information. This study investigated the types of information sources that are used widely by patients in choosing a doctor and identified ways in which the preferred sources differ in various situations. OBJECTIVE This study aims to address the following questions: (1) What is the proportion in which each of the various information sources is used? (2) How does the information source preferred by patients in choosing a doctor change when there is a difference in the difficulty of medical decision making, in the level of the hospital, or in a rural versus urban situation? (3) How do information sources used by patients differ when they choose doctors with different specialties? METHODS This study overcomes a major limitation in the use of the survey technique by employing data from the Good Doctor website, which is now China's leading online health care community, data which are objective and can be obtained relatively easily and frequently. Multinomial logistic regression models were applied to examine whether the proportion of use of these information sources changes in different situations. We then used visual analysis to explore the question of which type of information source patients prefer to use when they seek medical assistance from doctors with different specialties. RESULTS The 3 main information sources were online reviews (OR), family and friend recommendations (FR), and doctor recommendations (DR), with proportions of use of 32.93% (559,345/1,698,666), 23.68% (402,322/1,698,666), and 17.48% (296,912/1,698,666), respectively. Difficulty in medical decision making, the hospital level, and rural-urban differences were significantly associated with patients’ preferred information sources for choosing doctors. Further, the sources of information that patients prefer to use were found to vary when they looked for doctors with different medical specialties. CONCLUSIONS Patients are less likely to use online reviews when medical decisions are more difficult or when the provider is not a tertiary hospital, the former situation leading to a greater use of online reviews and the latter to a greater use of family and friend recommendations. In addition, patients in large cities are more likely to use information from online reviews than family and friend recommendations. Among different medical specialties, for those in which personal privacy is a concern, online reviews are the most common source. For those related to children, patients are more likely to refer to family and friend recommendations, and for those related to surgery, they value doctor recommendations more highly. Our results can not only contribute to aiding government efforts to further promote the dissemination of health care information but may also help health care industry managers develop better marketing strategies.

Exploiting User Check-In Data for Geo-Friend Recommendations in Location-Based Social Networks

The development of Web 2.0 technologies has meant that online social networks can both help the public facilitate sharing and communication and help them make new friends through their cyberspace social circles. Generating more accurate and geographically related results to help users find more friends in real life is gradually becoming a research hotspot. Recommending geographically related friends and alleviating check-in data sparsity problems in location-based social networks allows those to divide a day into different time slots and automatically collect user check-in data at each time slot over a certain period. Second, some important location points or regions are extracted from raw check-in trajectories, temporal periodic trajectories are constructed, and a geo-friend recommendation framework is proposed that can help users find geographically related friends. Finally, empirical studies from a real-world dataset demonstrate that this paper's method outperforms other existing methods for geo-friend recommendations in location-based social networks.

The impact of consumers’ positive online recommendations on the omnichannel webrooming experience

Purpose This purpose of this paper is to analyze how consumers’ online recommendations affect the omnichannel webrooming experience based on the internet, physical and mobile channels. Design/methodology/approach Two experimental studies are implemented. Study 1 analyzes the impact of an online review on the physical interaction with the product. Study 2 modifies the moment of receiving the online recommendation and its social tie. Findings Webrooming improves the shopping experience. Online recommendations from anonymous customers increase confidence in the product’s adequacy, although this effect depends on the moment of receiving the recommendation and the level of confidence before interacting physically with the product. Friend recommendations reinforce preferences regardless of previous online experiences. Research limitations/implications This research examines the effects of different types of online recommendations on offline shopping experiences, choice and confidence. Confidence is stressed as a key variable in omnichannel behavior. Practical implications The findings offer practical value for electronic word-of-mouth marketing, omnichannel marketing, as well as online and physical channel management. Originality/value This is one of the first studies that examine the impact of online consumer recommendations on shopping experiences combining online, mobile and physical channels. The results reveal the importance of recommendations’ source and moment of reception for determining consumers’ preferences, choice and confidence.