Background:
The rising number of cancer survivors and the shortage of health care professionals challenge the accessibility of cancer care. Health technologies are necessary for sustaining optimal patient journeys. To understand individuals’ daily lives during their patient journey, qualitative studies are crucial. However, not all patients wish to share their stories with researchers.

Objective:
This study aims to identify and assess patient experiences on a large scale using a novel machine learning–supported approach, leveraging data from patient forums.

Methods:
Forum posts of patients with colorectal cancer (CRC) from the Cancer Survivors Network USA were used as the data source. Topic modeling, as a part of machine learning, was used to recognize the topic patterns in the posts. Researchers read the most relevant 50 posts on each topic, dividing them into “home” or “hospital” contexts. A patient community journey map, derived from patients stories, was developed to visually illustrate our findings. CRC medical doctors and a quality-of-life expert evaluated the identified topics of patient experience and the map.

Results:
Based on 212,107 posts, 37 topics and 10 upper clusters were produced. Dominant clusters included “Daily activities while living with CRC” (38,782, 18.3%) and “Understanding treatment including alternatives and adjuvant therapy” (31,577, 14.9%). Topics related to the home context had more emotional content compared with the hospital context. The patient community journey map was constructed based on these findings.

Conclusions:
Our study highlighted the diverse concerns and experiences of patients with CRC. The more emotional content in home context discussions underscores the personal impact of CRC beyond clinical settings. Based on our study, we found that a machine learning-supported approach is a promising solution to analyze patients’ experiences. The innovative application of patient community journey mapping provides a unique perspective into the challenges in patients’ daily lives, which is essential for delivering appropriate support at the right moment.@en

Human–computer interaction (HCI) has received growing interest in both academic research and the design of information technological applications. Automated facial expression estimation of image is a difficult, yet crucial, problem in the design of HCI system. Although artificial neural network has achieved many remarkable results, few smart wearable devices can benefit from it. Most of these devices are constrained by limited computing and storage capacity. An effective solution is to allow servers to handle multiple tasks simultaneously. Toward this goal, we have been building an Efficient multitask scheme for facial expression estimation (EM-FEE). A multitask neural network is designed to enable the HCI system to accomplish different related tasks at the same time, that is, locating the user’s facial landmarks and estimating facial expressions. Experimental results demonstrate that our proposed scheme outperforms state-of-the-art. Finally, we review the remaining challenges and corresponding opportunities as well as future directions of the design of facial expression estimation systems for smart wearable devices.

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