Juan Carlos Quiroz

• PhD
• Research Associate at UNSW Sydney

This scoping review maps the literature on clinical decision support systems (CDSSs) that aid clinicians in treatment decision-making for youth mental health. The objectives were to identify CDSSs used in youth mental health services, as well as grade and evaluate these systems with regard to performance, usability, and clinical utility. Electronic...

Aims The importance of early life factors in determining health in later adulthood is increasingly recognized. This study evaluated the association of adverse childhood experiences with cardiovascular magnetic resonance (CMR) phenotypes. Methods and Results UK Biobank participants who had completed CMR and the self-reported questionnaire on trauma...

Background The identification of predictors of treatment response is crucial for improving treatment outcome for children with anxiety disorders. Machine learning methods provide opportunities to identify combinations of factors that contribute to risk prediction models. Methods A machine learning approach was applied to predict anxiety disorder r...

Objective To identify factors associated with receiving electroconvulsive therapy (ECT) for serious psychiatric conditions. Methods Retrospective observational study using hospital administrative data linked with death registrations and outpatient mental health data in New South Wales (NSW), Australia. The cohort included patients admitted with a...

Objectives To assess the effects of digital patient decision-support tools for atrial fibrillation (AF) treatment decisions in adults with AF. Study design Systematic review and meta-analysis. Eligibility criteria Eligible randomised controlled trials (RCTs) evaluated digital patient decision-support tools for AF treatment decisions in adults wit...

Aims: To evaluate the relationship between neuroticism personality traits and cardiovascular magnetic resonance (CMR) measures of cardiac morphology and function, considering potential differential associations in men and women. Methods and results: The analysis includes 36,309 UK Biobank participants (average age= 63.9±7.7 years; 47.8% men) wit...

Background: Cardiovascular disease (CVD) risk prediction is important in guiding the intensity of therapy in CVD prevention. Whilst current risk prediction algorithms use traditional statistical approaches, machine learning (ML) presents an alternative method that may improve risk prediction accuracy. This systematic review and meta-analysis aimed...

Objective: To develop prognostic survival models for predicting adverse outcomes after catheter ablation treatment for non-valvular atrial fibrillation (AF). Methods: We used a linked dataset including hospital administrative data, prescription medicine claims, emergency department presentations, and death registrations of patients in New South Wal...

Research has posited that machine learning could improve suicide risk prediction models, which have traditionally performed poorly. This systematic review and meta-analysis evaluated the performance of machine learning models in predicting longitudinal outcomes of suicide-related outcomes of ideation, attempt, and death and examines outcome, data,...

Objectives To examine i) the use of mobile apps and fitness trackers in adults during the COVID-19 pandemic to support health behaviors; ii) the use of COVID-19 apps; iii) associations between using mobile apps and fitness trackers, and health behaviors; iv) differences in usage amongst population subgroups. Methods An online cross-sectional surve...

Background Estimations of causal effects from observational data are subject to various sources of bias. One method for adjusting for the residual biases in the estimation of treatment effects is through the use of negative control outcomes, which are outcomes not believed to be affected by the treatment of interest. The empirical calibration proce...

Objectives To investigate the feasibility of the be.well app and its personalization approach which regularly considers users’ preferences, amongst university students. Methods We conducted a mixed-methods, pre-post experiment, where participants used the app for 2 months. Eligibility criteria included: age 18–34 years; owning an iPhone with Inter...

Objective To investigate clinical and health system factors associated with receiving catheter ablation (CA) and earlier ablation for non-valvular atrial fibrillation (AF). Methods We used hospital administrative data linked with death registrations in New South Wales, Australia for patients with a primary diagnosis of AF between 2009 and 2017. Ou...

Common data models standardize the structures and semantics of health datasets, enabling reproducibility and large-scale studies that leverage the data from multiple locations and settings. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) is one of the leading common data models. While there is a strong incentive to conve...

Observational health data can be leveraged to measure the real-world use and potential benefits or risks of existing medical interventions. However, lack of programming proficiency and advanced knowledge of causal inference methods excludes some clinicians and non-computational researchers from performing such analyses. Code-free dashboard tools pr...

Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infec...

Background: Estimations of causal effects from observational data are subject to various sources of bias. These biases can be adjusted by using negative control outcomes not affected by the treatment. The empirical calibration procedure uses negative controls to calibrate p-values and both negative and positive controls to calibrate coverage of the...

Objective: To investigate clinical and health system factors associated with receiving catheter ablation (CA) for non-valvular atrial fibrillation (AF). Study Design and Setting: We used hospital administrative data linked with death registrations in New South Wales, Australia for patients with a primary diagnosis of AF between 2009 and 2017. We in...

Speech summarization techniques take human speech as input and then output an abridged version as text or speech. Speech summarization has applications in many domains from information technology to health care, for example improving speech archives or reducing clinical documentation burden. This scoping review maps close to 2 decades of speech sum...

Objective: Develop an extract, transform, load (ETL) framework for the conversion of health databases to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) that supports transparency of the mapping process, readability, refactoring, and maintainability. Materials and Methods: We propose an ETL framework that is metadata-dri...

BACKGROUND Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. OBJECTIVE This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information...

Given that the one-size-fits-all approach to mobile health interventions have limited effects, a personalized approach might be necessary to promote healthy behaviors and prevent chronic conditions. Our systematic review aims to evaluate the effectiveness of personalized mobile interventions on lifestyle behaviors (i.e., physical activity, diet, sm...

Objective To determine the effectiveness of physical activity interventions involving mobile applications (apps) or trackers with automated and continuous self-monitoring and feedback. Design Systematic review and meta-analysis. Data sources PubMed and seven additional databases, from 2007 to 2020. Study selection Randomised controlled trials in...

Background Smartphone apps, fitness trackers, and online social networks have shown promise in weight management and physical activity interventions. However, there are knowledge gaps in identifying the most effective and engaging interventions and intervention features preferred by their users. Objective This 6-month pilot study on a social networ...

To inform the development of automated summarization of clinical conversations, this study sought to estimate the proportion of doctor-patient communication in general practice (GP) consultations used for generating a consultation summary. Two researchers with a medical degree read the transcripts of 44 GP consultations and highlighted the phrases...

Background: Coronavirus disease 2019 (COVID-19) has overwhelmed health systems worldwide. It is important to identify severe cases as early as possible, so that resources can be mobilized and treatment can be escalated. Objective: This study aims to develop a machine learning approach for automated severity assessment of COVID-19 patients based...

BACKGROUND COVID-19 has overwhelmed health systems worldwide. It is important to identify severe cases as early as possible, such that resources can be mobilized and treatment can be escalated. OBJECTIVE This study aims to develop a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data. METHODS...

Objective: The study sought to understand the potential roles of a future artificial intelligence (AI) documentation assistant in primary care consultations and to identify implications for doctors, patients, healthcare system, and technology design from the perspective of general practitioners. Materials and methods: Co-design workshops with ge...

Speech summarisation techniques take human speech as input and then output an abridged version as text or speech. Speech summarisation has applications in many domains from information technology to health care, for example improving speech archives or reducing clinical documentation burden. This scoping review maps the speech summarisation literat...

Objectives This study aims to develop a machine learning approach for automated severity assessment of COVID-19 patients based on clinical and imaging data. Materials and Methods Clinical data, including demographics, signs, symptoms, comorbidities and blood test results and chest CT scans of 346 patients from two hospitals in the Hubei province, C...

Mental health resources available via websites and mobile apps provide support such as advice, journaling, and elements from cognitive behavioral therapy. The proliferation of spoken conversational agents, such as Alexa, Siri, and Google Home, has led to an increasing interest in developing mental health apps for these devices. We present the pilot...

Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infec...

UNSTRUCTURED Effective behavior change interventions may require ongoing personalized support for users. Rapid developments in digital technology and artificial intelligence are giving rise to more advanced types of personalized interventions that can analyze large amounts of data to provide real-time, contextualized support. Despite growing resear...

BACKGROUND Smartphone applications (apps), fitness trackers, and online social networks have shown promise in weight management and physical activity interventions. However, knowledge gaps remain regarding which are the most effective and engaging interventions, and what intervention features are preferred by their users. OBJECTIVE This 6-month st...

Conversational agents have increasingly been deployed in healthcare applications. However, significant challenges remain in developing this technology. Recent research in this area has highlighted that: i) patient safety was rarely evaluated; ii) health outcomes were poorly measured, and iii) no standardised evaluation methods were employed. The co...

Background Bayesian modelling and statistical text analysis rely on informed probability priors to encourage good solutions. Objective This paper empirically analyses whether text in medical discharge reports follow Zipf’s law, a commonly assumed statistical property of language where word frequency follows a discrete power-law distribution. Meth...

Background: Conversational agents (CAs) are systems that mimic human conversations using text or spoken language. Their widely used examples include voice-activated systems such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. The use of CAs in health care has been on the rise, but concerns about their potential safety risks o...

Background: The personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. Objective: The goal of this systematic review was to understand the ways in which personalization h...

Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognitio...

BACKGROUND Conversational agents (CAs) are systems that mimic human conversations using text or spoken language. Their widely-used examples include voice-activated systems like Apple Siri, Google Assistant, Amazon Alexa, or Microsoft Cortana. The use of CAs in healthcare has been on the rise, but concerns about their potential safety risks often re...

BACKGROUND The personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. OBJECTIVE The goal of this systematic review was to understand the ways in which personalization has...

Objective The objective of this study is to characterize the dynamic structure of primary care consultations by identifying typical activities and their inter-relationships to inform the design of automated approaches to clinical documentation using natural language processing and summarization methods. Materials and Methods This is an observation...

Background Technological interventions such as mobile apps, Web-based social networks, and wearable trackers have the potential to influence physical activity; yet, only a few studies have examined the efficacy of an intervention bundle combining these different technologies. Objective This study aimed to pilot test an intervention composed of a s...

This study investigates the use of movement sensor data from a smart watch to infer an individual's emotional state. We present our findings on a user study with 50 participants. The experimental design is a mixed-design study; within-subjects (emotions; happy, sad, neutral) and between-subjects (stimulus type: audio-visual "movie clips", audio "mu...

Background: Research in psychology has shown that the way a person walks reflects that person’s current mood (or emotional state). Recent studies have used smartphones to detect emotional states from movement data. Objective: This study investigates the use of movement sensor data from a smart watch to infer an individual’s emotional state. We pres...

We present a computational model of creative design based on collaborative interactive genetic algorithms. In our model, designers individually guide interactive genetic algorithms (IGAs) to generate and explore potential design solutions quickly. Collaboration is supported by allowing designers to share solutions amongst each other while using IGA...

This paper proposes a new approach to diagnose broken rotor bar failure in a line start-permanent magnet synchronous motor (LS-PMSM) using random forests. The transient current signal during the motor startup was acquired from a healthy motor and a faulty motor with a broken rotor bar fault. We extracted 13 statistical time domain features from the...

This paper proposes a new approach to diagnose broken rotor bar failure in a line start-permanent magnet synchronous motor (LS-PMSM) using random forests. The transient current signal during the motor startup was acquired from a healthy motor and a faulty motor with a broken rotor bar fault. We extracted 13 statistical time domain features from the...

This study investigates the use of accelerometer data from a smart watch to infer an individual's emotional state. We present our preliminary findings on a user study with 50 participants. Participants were primed either with audio-visual (movie clips) or audio (classical music) to elicit emotional responses. Participants then walked while wearing...

This study investigates the use of accelerometer data from a smart watch to infer an individual’s emotional state. We present our preliminary findings on a user study with 50 participants. Participants were primed either with audio-visual (movie clips) or audio (classical music) to elicit emotional responses. Participants then walked while wearing...

We use the public Human Activity Recognition Using Smartphones (HARUS) data-set to investigate and identify the most informative features for determining the physical activity performed by a user based on smartphone accelerometer and gyroscope data. The HARUS data-set includes 561 time domain and frequency domain features extracted from sensor read...

We present a web application for the procedural generation of perturbations of 3D models. We generate the perturbations by generating vertex shaders that change the positions of vertices that make up the 3D model. The vertex shaders are created with an interactive genetic algorithm, which displays to the user the visual effect caused by each vertex...

This paper presents an overview of the research and development of Brain Computer Interfaces (BCIs), focused mainly on the software component. A classification of BCIs is proposed, providing a general sense of the various ways one can approach such systems. The different types of BCI systems are the main focus of the paper, and related research and...

The role of collaboration in the realm of social creativity has been the focus of cutting edge research in design studies. In this paper, the authors investigate the role of collaboration in the process of creative design and propose a computational model of creativity based on the newly proposed meta-design approach. Meta-design is a unique partic...

We present an implementation of a framework for creative design based on collaborative interactive genetic algorithms. The hypothesis is that creative designs can be produced if the design search space can be continuously expanded not just by modifying the values of variables but also by adding new ones. The framework presented herein transforms we...

The role of collaboration in the realm of social creativity has been the focus of cutting edge research in design studies. In this paper, the authors investigate the role of collaboration in the process of creative design and propose a computational model of creativity based on the newly proposed meta-design approach. Meta-design is a unique partic...

We present human guided evolution of brochure documents. The user interacts with a genetic algorithm, which evolves placeholders, each placeholder represented with one of three shapes: (1) ellipse, (2) rectangle, and (3) rounded rectangle. The user guides the evolutionary process by evaluating a small subset taken from a large population of documen...

We present a computational model of creative design based on collaborative interactive genetic algorithms. We test our model on floorplanning. We guide the evolution of floorplans based on subjective and objective criteria. The subjective criteria consists of designers picking the floorplan they like the best from a population of floorplans, and th...

We present IGAP, a peer to peer interactive genetic algo- rithm which reflects the real world methodology followed in team design. We apply our methodology to floorplanning. Through collaboration users are able to visualize designs done by peers on the network, while using case injection to allow them to bias their populations and the fitness funct...

We propose a computational model for creative design based on collaborative interactive genetic algorithms, and present an implementation for evolving creative floorplans and widget layout/colors for individual UI panels. We map our model and its implementation to earlier models of creative design from literature. We also address critical research...

In this paper we present a computational approach to developing effective training systems for virtual simulation environments. In particular, we focus on a Naval simulation system, used for training of conning officers. The currently existing training solutions require multiple expert personnel to control each vessel in a training scenario, or are...

We attack the problem of user fatigue in using an interactive genetic algorithm to evolve user interfaces in the XUL interface definition language. The interactive genetic algorithm combines computable user interface design metrics with subjective user input to guide evolution. Individuals in our population represent interface specifications and we...

The development of network-attached devices has ushered in an era of autonomous, multi-function equipment demanding minimal human interaction: the only requirements are data and electricity. Despite these advances, these machines continue underutilized in network environments due to operating system limitations regarding the management of these dev...

We investigate the trade off between investing effort in improving the features of a research environment that increases productivity and investing such effort in actually conducting the research experiments using a less elaborated, albeit sufficiently operational environment. The study case presented is an interactive genetic algorithm environment...

Research in context-aware user interfaces aims to improve human-computer interaction by providing more effective, smarter and user-friendlier solutions for computer applications. Currently, software available for performing such research and developing context-aware interfaces is very limited both in scope and possibilities of extension. Sycophant...

We attack the problem of user fatigue by using an interactive genetic algorithm to evolve user interfaces in the XUL inter- face definition language. The interactive genetic algorithm combines a set of computable user interface design metrics with subjective user input to guide the evolution of inter- faces. Our goal is to provide user interface de...

We investigate the use of genetic algorithms to evolve AI players for real-time strategy games. To overcome the knowledge acquisition bottleneck found in using traditional expert systems, scripts, or decision trees we evolve players through co-evolution. Our game players are implemented as resource allocation systems. Influence map trees are used t...

Graphical user interface design is a time consuming, expensive, and complex software design process. User interface design is both art and science in that we use both objective and subjective design metrics to evaluate interfaces. An automated process that relies on both subjective and objective metrics to guide the evolution of effective, personal...

V-FIRE is a 3D fire simulation and visualization software tool that allows users to harness and observe fire evolution and fire-related processes in a controlled virtual environment. This paper presents details of the tool's requirements specification, software architecture, medium and low-level design, and prototype user interface. As the tool is...

We attack the problem of user fatigue in using an interactive genetic algorithm to evolve user interfaces in the XUL interface definition language. The interactive gene tic algorithm combines computable user interface design metrics with subjective user input to guide evolution. Individuals in our population represent interface specifications and w...