An Open-Source Platform for Real-Time Preliminary Diagnosis amongst Adults using Data Analytics

Depression can be defined as a mental health disorder characterized by persistently depressed mood, loss of interest in activities, causing significant impairment in daily life.  Technical intervention to screen depression in non-clinical population which records, classify depression on the basis of severity and provide features or predictors that discriminate the classification of depression among non-clinical population comprising of college students is the main area of the study. Beck Depression Inventory – II (BDI-II), as per Diagnostic and Statistical manual of Mental disorder (DSM IV) is used to screen depression and its severity. Indicators are determined on the basis of how well the features or predictors can discriminate the classes of depression severity.  Providing quality indicators which help in supporting the process can be considered as symptoms for screening depression.  Descriptive analytics is used in order to find the underlying pattern of the responses captured, factor analysis groups variables on the basis of correlation between patterns of the responses to reduce dimension.  The approach for supervised descriptive analysis method that takes BDI-II questions as features and refine the features using information gain and linear discriminant analysis as feature selection algorithm. The classification of severity of depression is done using Support vector machine (SVM).

Mental Health Information Reporting Assistant (MHIRA)—an open-source software facilitating evidence-based assessment for clinical services

Evidence-based assessment (EBA) in mental health is a critical aspect of improving patient outcomes and addressing the gaps in mental health care. EBA involves the use of psychometric instruments to gather data that can inform clinical decision-making, inform policymakers, and serve as a basis for research and quality management. Despite its potential, EBA is often hindered by barriers such as workload and cost, leading to its underutilization. Regarding low- and middle-income countries (LMIC), the implementation of EBA is recognized as a key strategy to address and close the prevalent mental health treatment gap.To simplify the application of EBA including in LMIC, an international team of researchers and practitioners from Tanzania, Kosovo, Chile, and Switzerland developed the Mental Health Information Reporting Assistant (MHIRA). MHIRA is an open-source electronic health record that streamlines EBA by digitising psychometric instruments and organising patient data in a user-friendly manner. It provides immediate and convenient reports to inform clinical decision-making.The current article provides a comprehensive overview of the features and technical details of MHIRA, as well as insights from four implementation scenarios. The experience gained during the implementations as well as the user-feedback suggests that MHIRA has the potential to be successfully implemented in a variety of clinical contexts and simplify the use of EBA. However, further research is necessary to establish its potential to sustainably transform healthcare services and impact patient outcomes.In conclusion, MHIRA represents an important step in promoting the widespread adoption of EBA in mental health. It offers a promising solution to the barriers that have limited the use of EBA in the past and holds the potential to improve patient outcomes and support the ongoing efforts to address gaps in mental health care

Attention bias for negative semantic stimuli in late life depression and clinical research portfolio

Background: Recent studies indicate that depressed individuals may have difficulties disengaging visual-spatial attention from negative information. Preliminary studies in depressed older adults provide evidence for the existence of biased attention to negative stimuli. However, the specific components of attention driving the detected bias effects in this population are not known. Aims: This study examined the mechanisms underlying attention biases in Late Life Depression (LLD). It was predicted that depressed older adults, like their younger counterparts, would demonstrate an impaired ability to disengage attention from negative stimuli relative to neutral and positive stimuli, as compared to non-depressed older adult controls. Methods: 16 clinically depressed older adults and 22 older adult controls matched for age, gender and pre-morbid verbal IQ performed an emotional spatial cueing task that required classifying a target stimulus. The location of the target was correctly or incorrectly cued by a neutral, positive or negative word. Results: Planned comparisons did not support the primary hypotheses. However, participants in the depressed group, in general, were slower to respond than participants in the control group. Conclusions: Results suggest that the ability to disengage attention from negative words is not impaired in LLD; however methodological limitations prevent firm conclusions being drawn. Possible explanations for the results are discussed along with directions for future research

Multi-Modality Human Action Recognition

Human action recognition is very useful in many applications in various areas, e.g. video surveillance, HCI (Human computer interaction), video retrieval, gaming and security. Recently, human action recognition becomes an active research topic in computer vision and pattern recognition. A number of action recognition approaches have been proposed. However, most of the approaches are designed on the RGB images sequences, where the action data was collected by RGB/intensity camera. Thus the recognition performance is usually related to various occlusion, background, and lighting conditions of the image sequences. If more information can be provided along with the image sequences, more data sources other than the RGB video can be utilized, human actions could be better represented and recognized by the designed computer vision system.;In this dissertation, the multi-modality human action recognition is studied. On one hand, we introduce the study of multi-spectral action recognition, which involves the information from different spectrum beyond visible, e.g. infrared and near infrared. Action recognition in individual spectra is explored and new methods are proposed. Then the cross-spectral action recognition is also investigated and novel approaches are proposed in our work. On the other hand, since the depth imaging technology has made a significant progress recently, where depth information can be captured simultaneously with the RGB videos. The depth-based human action recognition is also investigated. I first propose a method combining different type of depth data to recognize human actions. Then a thorough evaluation is conducted on spatiotemporal interest point (STIP) based features for depth-based action recognition. Finally, I advocate the study of fusing different features for depth-based action analysis. Moreover, human depression recognition is studied by combining facial appearance model as well as facial dynamic model

a randomized controlled trial

학위논문(박사) -- 서울대학교대학원 : 의과대학 의과학과, 2021.8. 최형진.Background - Since lifestyle modification is the cornerstone of the obesity treatment, digital therapeutics (DTx) became one of the compelling and easily accessible treatment modalities. Objective - This research proposes to validate the treatment efficacy, understand behavioral changes by eating behavioral analysis, identify the predictive digital phenotypes for engagement and clinical outcomes, and examine genetic precision medicine of a novel digital therapeutic for obesity (dCBT-O). Method – This was an open-label, active-comparator, randomized controlled trial. Seventy female participants with body mass index (BMI) scores above 24kg/m² and no clinical problems besides obesity were randomized into experimental and control groups. The experimental group (dCBT-O group; 45 participants) was connected with a therapist intervention using a digital healthcare service that provided daily feedback and assignments for 8 weeks. The control group (25 participants) also used the digital healthcare service but practiced self-care without therapist intervention. Regarding the validating treatment efficacy, the primary outcomes of this study were objectively measured: weight in kg as well as other body compositions at 0, 8, and 24 weeks. Also, several eating behavioral phenotypes were assessed by buffet test-meal and food diary in app to examine the healthy behavioral change. Regarding the predictors for treatment efficacy, multidimensional digital phenotypes within time-series data were analyzed by elastic net regression method and obesity-related SNPs were genotyped from dCBT-O group. Result – Both weight (–3.1%, SD 4.5, vs –0.7%, SD 3.4; p = 0.036) and fat mass (–6.3%, SD 8.8, vs –0.8%, SD 8.1; p = 0.021) reduction at 8 weeks in the dCBT-O group were significantly higher than in the control group. Applying the machine learning approach, sixteen types of digital phenotypes (i.e., lower intake of high calorie food and evening snack, higher interaction frequency with mentors) predicted engagement rates, thirteen different digital phenotypes (i.e., lower intake of high calorie food and carb, higher intake of low calorie food) predicted the short-term weight change, and eight measures of digital phenotypes (i.e., lower intake of carb and evening snack, higher motivation) predicted the long-term weight change. The dCBT-O was also successful in promoting healthy eating behaviors that led to physiological and psychological adjustment for the metabolic mechanisms and consequences of healthy eating behavior. Lastly, CETP and APOA2 SNPs were significantly associated with the change in BMI (p = 0.028 and p = 0.005, respectively) at 24 weeks and eating behavioral phenotypes (p = 0.007 for healthy diet diversity and p = 0.036 for healthy diet proportion, respectively), the clinical efficacy markers of this study. Conclusion – These findings confirm that the multidisciplinary approach via digital modalities enhances the clinical efficacy of digital-based interventions for obesity. Moreover, it contributes to better understand the mechanisms of human eating behavior related to weight control. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics.비만은 대표적인 생활습관 질병으로 알려져 있다. 따라서, 효과적인 비만 치료를 위해서는 다차원적인 치료적 접근이 중요시되는데, 디지털 치료제(Digital Therapeutics; DTx)는 이러한 접근에 최적화 되어있다. 본 연구의 목적은 새로 개발한 비만 디지털 치료제의 효과를 임상적 지표들과 섭식 행동 표현형들의 변화를 기반으로 검증하며, 치료적 순응도와 효과성을 예측할 수 있는 디지털 표현형들과 유전형들을 탐색하는 것이다. 본 연구에서는 BMI 24 이상, 기타 임상적인 증상을 보이지 않는 70명의 2-30대 여성들을 대상으로 대조군 대비 비만 디지털 치료제군(Digital Therapeutic for Obesity; dCBT-O군)에 1:2 비율의 무작위배정 임상시험을 시행하였다. dCBT-O군의 비만 치료는 임상심리학 전공 및 디지털 헬스케어 전문가가 8주 동안 진행하였으며, 24주차에는 치료 후 경과에 대한 평가를 실시하였다. 비만 디지털 치료제 효과 검증의 주요 지표는 체중을 비롯한 다양한 신체 계측 지표들의 변화이다. 이차 지표는 뷔폐실험과 모바일 어플리케이션 내 식단기록에서 수집된 섭식행동 표현형들을 기반으로 건강한 섭식행동 변화이다. 치료 순응도 및 효과 예측 인자들을 발굴하기 위해서는 다차원적인 시계열 디지털 표현형들을 머신러닝 기법으로 분석하였다. 그리고, 치료 반응 수준을 예측하는 유전형들을 찾기 위해 단일염기다형(Single Nucleotide Polymorphisms; SNP) 분석을 시행하였다. 본 연구의 주요 결과로 첫째, 8주간 치료 직후 dCBT-O군의 체중 변화가 대조군의 체중 변화에 비해 유의미하게 감량하였으며, 치료 종료 후 24주차도 체중이 감량 및 유지되었다. 둘째, dCBT-O군의 섭식행동이 대조군의 섭식행동에 비해 유의미하게 건강한 섭식행동으로 증진되었다. 셋째, 머신러닝 분석의 결과 16가지 디지털 표현형들이 치료적 순응도를 예측하고, 13가지 디지털 표현형들이 단기적인 치료효과를 예측하며, 8가지 디지털 표현형들이 장기적인 치료효과를 예측하였다. 마지막으로, CETP와 APOA2 SNP 유전형들이 신체계측 변화와 섭식행동변화와 유의미한 상관을 보였다. 본 연구는 디지털 기술을 활용한 다학제적인 접근이 비만 디지털 치료제의 임상 효과를 향상시킨다는 것을 보여준다. 또한 다차원적인 분석을 통해 체중 조절과 관련된 인간의 섭식 행동의 메커니즘을 더 잘 이해하는 데 기여한다. 본 연구는 첨단 예방의학과 정밀의학을 위한 디지털 치료제 개발에 중요한 패러다임을 제시할 것이다.Chapter 1. Introduction 1 Part I. Validating the treatment efficacy and finding its predictive markers: development of a dCBT-O 6 Part II. Eating behavioral analysis using buffet test-meal and food diary in app: understanding human eating behavior change by dCBT-O 8 Part III. Digital phenotyping using machine-learning analysis: identifying a predictive model for engagement in application and clinical outcomes of dCBT-O 11 Part IV. Genetic analysis for predicting the clinical responses: genetic precision medicine of dCBT-O 14 Chapter 2. Method 19 Chapter 3. Results 40 Chapter 4. Discussion 75 Perspectives A. Main issues related to DTx for obesity and eating behavior problems 91 Perspectives B. Limitations of DTx being applied in the clinics 96 Perspectives C. Future perspectives and recommendations 96 Chapter 5. Conclusion 99 Bibliography 100 Abstract in Korean 118 Acknowledgement 120박

POTENTIAL FOR NON-CONVENTIONAL USE OF SPLIT-BEAM PHASE DATA IN BOTTOM DETECTION

Because the safety of the navigation depends on accurate knowledge of the submerged features, any improvements in the ability to resolve those features are of major interest. Ultimately this reflects the bottom detection performance of the bathymetric measuring system (most commonly multibeam sonar) utilized. To this end, different algorithms for the detection of the seafloor or other targets have previously been developed, all presenting advantages and disadvantages. The two most common techniques are designed around time-series or angle-series analysis, although by far the most focus has been on time-series. The option of recording both amplitude and phase data of the water column permit the development and testing of new algorithms to be carried out in post-processing.This research evaluates the use of water column data to perform bottom detection in a nonconventional way, that is, analyzing angle-series instead of time-series. The proposed algorithm is based on the Beam Deviation Indicator (BDI) method with the inclusion of phase information. It sequentially evaluates each time-slice (angle-series), applying absolute and relative threshold filters to select echo envelopes based on intensity data. Then, the phase data of each echo envelope is analyzed for zero-crossings (across beams), which are then converted into angles. Thus, time-angle pairs are obtained, defining depth measurements. Such a method, herein termed Phase Deviation Indicator (PDI), can be applied in an alternative and mainly complementary way to the currently existing methods. Four different shallow waters seafloor relief types were investigated using an EM 2040P MkII multibeam echosounder, and collected datasets were evaluated with a focus on target or seafloor detection in many different geometries. The results obtained indicate that there are cases in which the analysis made only within a time-series, even using multi-detection features, can be incomplete and could be complemented by the analysis made within a beam-series (PDI). Particularly notable geometries included mast-like-objects, discontinuous surfaces or features whose lateral extent is confined mostly within a short range of incidence angles, thereby requiring multiple detections within the same beam. Such results emphasize the idea that the best detection method results from the integrated use of all available techniques

POTENTIAL FOR NON-CONVENTIONAL USE OF SPLIT-BEAM PHASE DATA IN BOTTOM DETECTION

Because the safety of the navigation depends on accurate knowledge of the submerged features, any improvements in the ability to resolve those features are of major interest. Ultimately this reflects the bottom detection performance of the bathymetric measuring system (most commonly multibeam sonar) utilized. To this end, different algorithms for the detection of the seafloor or other targets have previously been developed, all presenting advantages and disadvantages. The two most common techniques are designed around time-series or angle-series analysis, although by far the most focus has been on time-series. The option of recording both amplitude and phase data of the water column permit the development and testing of new algorithms to be carried out in post-processing.This research evaluates the use of water column data to perform bottom detection in a nonconventional way, that is, analyzing angle-series instead of time-series. The proposed algorithm is based on the Beam Deviation Indicator (BDI) method with the inclusion of phase information. It sequentially evaluates each time-slice (angle-series), applying absolute and relative threshold filters to select echo envelopes based on intensity data. Then, the phase data of each echo envelope is analyzed for zero-crossings (across beams), which are then converted into angles. Thus, time-angle pairs are obtained, defining depth measurements. Such a method, herein termed Phase Deviation Indicator (PDI), can be applied in an alternative and mainly complementary way to the currently existing methods. Four different shallow waters seafloor relief types were investigated using an EM 2040P MkII multibeam echosounder, and collected datasets were evaluated with a focus on target or seafloor detection in many different geometries. The results obtained indicate that there are cases in which the analysis made only within a time-series, even using multi-detection features, can be incomplete and could be complemented by the analysis made within a beam-series (PDI). Particularly notable geometries included mast-like-objects, discontinuous surfaces or features whose lateral extent is confined mostly within a short range of incidence angles, thereby requiring multiple detections within the same beam. Such results emphasize the idea that the best detection method results from the integrated use of all available techniques

Psyment: web-based platform to support psychologists’ evaluation methods

The Covid-19 pandemic was a turning point for healthcare professionals as it forced the development of Information and Communication Technology (ICT)-based healthcare systems to provide better patient interaction and simplify healthcare professionals’ working methods. As a result, this project aims to develop a novel web-based platform to assist these professionals in creating clinical assessment tools (i.e., Mini-Mental State Examination) for clinical populations and evaluate its results more efficiently. The project’s purpose is threefold: 1) to develop cybersecurity measures to safeguard sensitive information of the clinical population, 2) to develop the system’s backend, and 3) to focus on the user experience while developing the front end of the platform. Here, in this part of the thesis, we will focus on and discuss the user experience design while developing the front end of this platform. To develop such a web-based platform for healthcare professionals to create cognitive assessment tools, we had to 1) understand how they use pencil and paper-based cognitive assessment tools with clinical populations and 2) how they are evaluated. We performed 4 interviews with healthcare professionals; one was done while observing the interaction between healthcare professionals and the healthcare center’s clinical population. Moreover, we researched similar existing platforms in the scientific literature and online services while evaluating the strengths and weaknesses of each platform. Developing a visually pleasing platform with easy functionalities adapted to the user experience of healthcare professionals is the key to achieving more accurate results for their patient’s diagnoses while enhancing performance and productivity.A pandemia de Covid-19 foi um ponto importante para que houvesse uma mudança nos métodos de trabalho dos profissionais de saúde, pois esta incentivou o desenvolvimento de Tecnologias de Informação e Comunicação (TIC) nos sistemas de saúde de modo a proporcionar uma melhor interação com os pacientes e simplificar os métodos de trabalho dos profissionais de saúde. Consequentemente, este projeto visa desenvolver uma nova plataforma web-based para auxiliar os profissionais de saúde na criação de ferramentas de avaliação clínica (ex., Mini-Mental State Examination) para populações clínicas e avaliar os seus resultados de forma mais eficiente. O objetivo do projeto está divido em 3 partes: 1) desenvolver medidas de cyber segurança para salvaguardar informações sensíveis e confidenciais das populações clínicas, 2) desenvolver o back-end do sistema, e 3) focar-se na experiência do utilizador enquanto o front-end da plataforma também é desenvolvido. Aqui, nesta parte da dissertação, nós iremos nos focar em e discutir sobre o design de experiência do utilizador enquanto desenvolvemos o front-end desta plataforma. Para desenvolver uma plataforma web based para os profissionais de saúde conseguirem criar ferramentas de avaliação cognitiva, nós tivemos de 1) perceber como eles utilizam o papel e o lápis para executar os testes de avaliação cognitiva nas populações clínicas e 2) como estes são avaliados. Nós realizámos 4 entrevistas com profissionais de saúde; uma foi feita enquanto observávamos a interação entre o profissional de saúde e a população clínica do centro de saúde. Para além disto, pesquisámos por plataformas semelhantes que já existissem em literaturas científicas ou em serviços online enquanto estudávamos os pontos fortes e os pontos fracos de cada plataforma. Desenvolver uma plataforma que seja visualmente atrativa com funcionalidades fáceis adaptada à experiência do utilizador dos profissionais de saúde foi a chave para conseguir uns resultados mais corretos dos diagnósticos dos seus pacientes enquanto melhoramos o seu desempenho e produtividade