The erythrocyte sedimentation rate (ESR) is a commonly used test to screen for inflammatory conditions such as infections, autoimmune diseases, and cancers. However, it is a bulk macroscale test that requires a relatively large blood sample and takes a long time to run. Moreover, it provides no information regarding cell sizes or interactions, which can be highly variable. To overcome these drawbacks, we developed a microfluidic microscopy-based protocol to dynamically track settling red blood cells (RBCs) to quantify velocity of cell settling, as a surrogate for the ESR. We imaged individual cells in a vertical microfluidic channel and applied a hybrid cell detection and tracking algorithm to compute settling velocities. We combined eigenvalue background subtraction and centroid detection together with the Kalman filter and Hungarian assignment solver algorithms to increase accuracy and computational speed. Our algorithm is designed to track settling RBCs/aggregates in high cellularity samples rather than single cells in suspension. Detection accuracy was 79.3%, which is comparable to state-of-the-art cell-tracking techniques. Compared with conventional ESR tests, our approach has the advantages of being automated, using microliter volumes of blood samples, and rapid turnaround.
Portable microfluidic diagnostic devices, including flow cytometers, are being developed for point-of-care settings, especially in conjunction with inexpensive imaging devices such as mobile phone cameras. However, two pervasive drawbacks of these have been the lack of automated sample preparation processes and cells settling out of sample suspensions, leading to inaccurate results. We report an automated blood sample preparation unit (ABSPU) to prevent blood samples from settling in a reservoir during loading of samples in flow cytometers. This apparatus automates the preanalytical steps of dilution and staining of blood cells prior to microfluidic loading. It employs an assembly with a miniature vibration motor to drive turbulence in a sample reservoir. To validate performance of this system, we present experimental evidence demonstrating prevention of blood cell settling, cell integrity, and staining of cells prior to flow cytometric analysis. This setup is further integrated with a microfluidic imaging flow cytometer to investigate cell count variability. With no need for prior sample preparation, a drop of whole blood can be directly introduced to the setup without premixing with buffers manually. Our results show that integration of this assembly with microfluidic analysis provides a competent automation tool for low-cost point-of-care blood-based diagnostics.
Background Recommender systems have great potential in mental health care to personalize self-guided content for patients, allowing them to supplement their mental health treatment in a scalable way. Objective In this paper, we describe and evaluate 2 knowledge-based content recommendation systems as parts of Ginger, an on-demand mental health platform, to bolster engagement in self-guided mental health content. Methods We developed two algorithms to provide content recommendations in the Ginger mental health smartphone app: (1) one that uses users' responses to app onboarding questions to recommend content cards and (2) one that uses the semantic similarity between the transcript of a coaching conversation and the description of content cards to make recommendations after every session. As a measure of success for these recommendation algorithms, we examined the relevance of content cards to users’ conversations with their coach and completion rates of selected content within the app measured over 14,018 users. Results In a real-world setting, content consumed in the recommendations section (or “Explore” in the app) had the highest completion rates (3353/7871, 42.6%) compared to other sections of the app, which had an average completion rate of 37.35% (21,982/58,614; P<.001). Within the app’s recommendations section, conversation-based content recommendations had 11.4% (1108/2364) higher completion rates per card than onboarding response-based recommendations (1712/4067; P=.003) and 26.1% higher than random recommendations (534/1440; P=.005). Studied via subject matter experts’ annotations, conversation-based recommendations had a 16.1% higher relevance rate for the top 5 recommended cards, averaged across sessions of varying lengths, compared to a random control (110 conversational sessions). Finally, it was observed that both age and gender variables were sensitive to different recommendation methods, with responsiveness to personalized recommendations being higher if the users were older than 35 years or identified as male. Conclusions Recommender systems can help scale and supplement digital mental health care with personalized content and self-care recommendations. Onboarding-based recommendations are ideal for “cold starting” the process of recommending content for new users and users that tend to use the app just for content but not for therapy or coaching. The conversation-based recommendation algorithm allows for dynamic recommendations based on information gathered during coaching sessions, which is a critical capability, given the changing nature of mental health needs during treatment. The proposed algorithms are just one step toward the direction of outcome-driven personalization in mental health. Our future work will involve a robust causal evaluation of these algorithms using randomized controlled trials, along with consumer feedback–driven improvement of these algorithms, to drive better clinical outcomes.
BACKGROUND Recommender systems have great potential in mental health care to provide self-guided content to supplement the mental health journey for patients scalably; however, traditional filtering approaches often have skewed input data distributions, are static or may not account for changes in symptoms and clinical presentation. In this study, we describe and evaluate two knowledge-based content recommendation system models as part of Ginger, an on-demand mental health platform that seek to address the issues above. OBJECTIVE In this study, we describe and evaluate two knowledge-based content recommendation system models as part of Ginger, an on-demand mental health platform that seek to address the issues above. METHODS We developed two models to provide content recommendations for the Ginger mental health app content. First, a method that uses members' responses to onboarding questions to recommend content cards from the content library of the mental health platform. Second, a dynamic conversation-based recommendation method that matches the semantic similarity between the content of a conversation and the text description of content cards to make recommendations suitable for a conversational snippet. As a measure of success for these recommendation models, we examined the relevance of content cards to members’ conversations with their coach and completion rates of selected content within the app measured over 14018 users. RESULTS Conversation-based recommendation models performed better than random recommendations for all lengths of conversational sessions considering fractions of relevant cards as well as very relevant cards. In an offline analysis, conversation-based recommendations had a 16.1% higher relevance rate (for the top 5 recommended cards) averaged across sessions of varying lengths as compared to a random control algorithm. Comparing completion rates of content delivered in the app to over 14018 users, conversation-based content recommendations that had 11.4% higher completion rates per card than onboarding response based recommendations (P=.003) and 26.1% higher than random recommendations (P=.005)). CONCLUSIONS Recommender systems can help scale and supplement digital mental health care with relevant content and self-care recommendations. Conversation-based recommendation models allow for dynamic recommendations based on information gathered during the course of text-based coaching sessions, which is a critical capability given the changing nature of mental health over the course of treatment.
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