Fairness and bias correction in machine learning for depression prediction across four study populations

A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML) models learned from data can reinforce these structural inequalities or biases. Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations. We find that standard ML approaches regularly present biased behaviors. We also show that mitigation techniques, both standard and our own post-hoc method, can be effective in reducing the level of unfair bias. There is no one best ML model for depression prediction that provides equality of outcomes. This emphasizes the importance of analyzing fairness during model selection and transparent reporting about the impact of debiasing interventions. Finally, we also identify positive habits and open challenges that practitioners could follow to enhance fairness in their models.</p

Fairness and bias correction in machine learning for depression prediction across four study populations

A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML) models learned from data can reinforce these structural inequalities or biases. Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations. We find that standard ML approaches regularly present biased behaviors. We also show that mitigation techniques, both standard and our own post-hoc method, can be effective in reducing the level of unfair bias. There is no one best ML model for depression prediction that provides equality of outcomes. This emphasizes the importance of analyzing fairness during model selection and transparent reporting about the impact of debiasing interventions. Finally, we also identify positive habits and open challenges that practitioners could follow to enhance fairness in their models.</p

Fairness and bias correction in machine learning for depression prediction: results from four study populations

A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML) models leart from data can reinforce these structural inequalities or biases. Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations. We find that standard ML approaches show regularly biased behaviors. We also show that mitigation techniques, both standard and our own post-hoc method, can be effective in reducing the level of unfair bias. No single best ML model for depression prediction provides equality of outcomes. This emphasizes the importance of analyzing fairness during model selection and transparent reporting about the impact of debiasing interventions. Finally, we provide practical recommendations to develop bias-aware ML models for depression risk prediction.Comment: 11 pages, 2 figure

Nanomaterial for Adjuvants Vaccine: Practical Applications and Prospects

Vaccines contain adjuvants to strengthen the immune responses of the receiver against pathogen infection or malignancy. A new generation of adjuvants is being developed to give more robust antigen-specific responses, specific types of immune responses, and a high margin of safety. By changing the physical and chemical properties of nanomaterials, it is possible to make antigen-delivery systems with high bioavailability, controlled and sustained release patterns, and the ability to target and image. Nanomaterials can modulate the immune system so that cellular and humoral immune responses more closely resemble those desired. The use of nanoparticles as adjuvants is believed to significantly improve the immunological outcomes of vaccination because of the combination of their immunomodulatory and delivery effects. In this review, we discuss the recent developments in new adjuvants using nanomaterials. Based on three main vaccines, the subunit, DNA, and RNA vaccines, the possible ways that nanomaterials change the immune responses caused by vaccines, such as a charge on the surface or a change to the surface, and how they affect the immunological results have been studied. This study aims to provide succinct information on the use of nanomaterials for COVID-19 vaccines and possible new applications

Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation

Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors. First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image. Second, due to the complexity of vascular trees and the small size of vessels, it is challenging to obtain the amount of annotated training data typically needed by deep learning methods. To address these problems, we propose a novel annotation-efficient deep learning vessel segmentation framework. The framework avoids pixel-wise annotations, only requiring weak patch-level labels to discriminate between vessel and non-vessel 2D patches in the training set, in a setup similar to the CAPTCHAs used to differentiate humans from bots in web applications. The user-provided weak annotations are used for two tasks: (1) to synthesize pixel-wise pseudo-labels for vessels and background in each patch, which are used to train a segmentation network, and (2) to train a classifier network. The classifier network allows to generate additional weak patch labels, further reducing the annotation burden, and it acts as a second opinion for poor quality images. We use this framework for the segmentation of the cerebrovascular tree in Time-of-Flight angiography (TOF) and Susceptibility-Weighted Images (SWI). The results show that the framework achieves state-of-the-art accuracy, while reducing the annotation time by ∼77% w.r.t. learning-based segmentation methods using pixel-wise labels for training

Antibiotic use and prescription and its effects on Enterobacteriaceae in the gut in children with mild respiratory infections in Ho Chi Minh City, Vietnam. A prospective observational outpatient study.

BACKGROUND AND OBJECTIVES: Treatment guidelines do not recommend antibiotic use for acute respiratory infections (ARI), except for streptococcal pharyngitis/tonsillitis and pneumonia. However, antibiotics are prescribed frequently for children with ARI, often in absence of evidence for bacterial infection. The objectives of this study were 1) to assess the appropriateness of antibiotic prescriptions for mild ARI in paediatric outpatients in relation to available guidelines and detected pathogens, 2) to assess antibiotic use on presentation using questionnaires and detection in urine 3) to assess the carriage rates and proportions of resistant intestinal Enterobacteriaceae before, during and after consultation. MATERIALS AND METHODS: Patients were prospectively enrolled in Children's Hospital 1, Ho Chi Minh City, Vietnam and diagnoses, prescribed therapy and outcome were recorded on first visit and on follow-up after 7 days. Respiratory bacterial and viral pathogens were detected using molecular assays. Antibiotic use before presentation was assessed using questionnaires and urine HPLC. The impact of antibiotic usage on intestinal Enterobacteriaceae was assessed with semi-quantitative culture on agar with and without antibiotics on presentation and after 7 and 28 days. RESULTS: A total of 563 patients were enrolled between February 2009 and February 2010. Antibiotics were prescribed for all except 2 of 563 patients. The majority were 2nd and 3rd generation oral cephalosporins and amoxicillin with or without clavulanic acid. Respiratory viruses were detected in respiratory specimens of 72.5% of patients. Antibiotic use was considered inappropriate in 90.1% and 67.5%, based on guidelines and detected pathogens, respectively. On presentation parents reported antibiotic use for 22% of patients, 41% of parents did not know and 37% denied antibiotic use. Among these three groups, six commonly used antibiotics were detected with HPLC in patients' urine in 49%, 40% and 14%, respectively. Temporary selection of 3rd generation cephalosporin resistant intestinal Enterobacteriaceae during antibiotic use was observed, with co-selection of resistance to aminoglycosides and fluoroquinolones. CONCLUSIONS: We report overuse and overprescription of antibiotics for uncomplicated ARI with selection of resistant intestinal Enterobacteriaceae, posing a risk for community transmission and persistence in a setting of a highly granular healthcare system and unrestricted access to antibiotics through private pharmacies. REGISTRATION: This study was registered at the International Standard Randomised Controlled Trials Number registry under number ISRCTN32862422: http://www.isrctn.com/ISRCTN32862422

Evaluation of awake prone positioning effectiveness in moderate to severe COVID-19

Evidence mainly from high income countries suggests that lying in the prone position may be beneficial in patients with COVID-19 even if they are not receiving invasive ventilation. Studies indicate that increased duration of prone position may be associated with improved outcomes, but achieving this requires additional staff time and resources. Our study aims to support prolonged (≥ 8hours/day) awake prone positioning in patients with moderate to severe COVID-19 disease in Vietnam. We use a specialist team to support prone positioning of patients and wearable devices to assist monitoring vital signs and prone position and an electronic data registry to capture routine clinical data

Wearable devices for remote monitoring of hospitalized patients with COVID-19 in Vietnam

Patients with severe COVID-19 disease require monitoring with pulse oximetry as a minimal requirement. In many low- and middle- income countries, this has been challenging due to lack of staff and equipment. Wearable pulse oximeters potentially offer an attractive means to address this need, due to their low cost, battery operability and capacity for remote monitoring. Between July and October 2021, Ho Chi Minh City experienced its first major wave of SARS-CoV-2 infection, leading to an unprecedented demand for monitoring in hospitalized patients. We assess the feasibility of a continuous remote monitoring system for patients with COVID-19 under these circumstances as we implemented 2 different systems using wearable pulse oximeter devices in a stepwise manner across 4 departments