Towards adaptive technology in routine mental health care.

This paper summarizes the information technology-related research findings after 5 years with the INTROducing Mental health through Adaptive Technology project. The aim was to improve mental healthcare by introducing new technologies for adaptive interventions in mental healthcare through interdisciplinary research and development. We focus on the challenges related to internet-delivered psychological treatments, emphasising artificial intelligence, human-computer interaction, and software engineering. We present the main research findings, the developed artefacts, and lessons learned from the project before outlining directions for future research. The main findings from this project are encapsulated in a reference architecture that is used for establishing an infrastructure for adaptive internet-delivered psychological treatment systems in clinical contexts. The infrastructure is developed by introducing an interdisciplinary design and development process inspired by domain-driven design, user-centred design, and the person based approach for intervention design. The process aligns the software development with the intervention design and illustrates their mutual dependencies. Finally, we present software artefacts produced within the project and discuss how they are related to the proposed reference architecture. Our results indicate that the proposed development process, the reference architecture and the produced software can be practical means of designing adaptive mental health care treatments in correspondence with the patients' needs and preferences. In summary, we have created the initial version of an information technology infrastructure to support the development and deployment of Internet-delivered mental health interventions with inherent support for data sharing, data analysis, reusability of treatment content, and adaptation of intervention based on user needs and preferences.

A Reference Architecture for Data-Driven and Adaptive Internet-Delivered Psychological Treatment Systems: Software Architecture Development and Validation Study.

BACKGROUND: Internet-delivered psychological treatment (IDPT) systems are software applications that offer psychological treatments via the internet. Such IDPT systems have become one of the most commonly practiced and widely researched forms of psychotherapy. Evidence shows that psychological treatments delivered by IDPT systems can be an effective way of treating mental health morbidities. However, current IDPT systems have high dropout rates and low user adherence. The primary reason is that the current IDPT systems are not flexible, adaptable, and personalized as they follow a fixed tunnel-based treatment architecture. A fixed tunnel-based architecture follows predefined, sequential treatment content for every patient, irrespective of their context, preferences, and needs. Moreover, current IDPT systems have poor interoperability, making it difficult to reuse and share treatment materials. There is a lack of development and documentation standards, conceptual frameworks, and established (clinical) guidelines for such IDPT systems. As a result, several ad hoc forms of IDPT models exist. Consequently, developers and researchers have tended to reinvent new versions of IDPT systems, making them more complex and less interoperable. OBJECTIVE: This study aimed to design, develop, and evaluate a reference architecture (RA) for adaptive systems that can facilitate the design and development of adaptive, interoperable, and reusable IDPT systems. METHODS: This study was conducted in collaboration with a large interdisciplinary project entitled INTROMAT (Introducing Mental Health through Adaptive Technology), which brings together information and communications technology researchers, information and communications technology industries, health researchers, patients, clinicians, and patients' next of kin to reach its vision. First, we investigated previous studies and state-of-the-art works based on the project's problem domain and goals. On the basis of the findings from these investigations, we identified 2 primary gaps in current IDPT systems: lack of adaptiveness and limited interoperability. Second, we used model-driven engineering and Domain-Driven Design techniques to design, develop, and validate the RA for building adaptive, interoperable, and reusable IDPT systems to address these gaps. Third, based on the proposed RA, we implemented a prototype as the open-source software. Finally, we evaluated the RA and open-source implementation using empirical (case study) and nonempirical approaches (software architecture analysis method, expert evaluation, and software quality attributes). RESULTS: This paper outlines an RA that supports flexible user modeling and the adaptive delivery of treatments. To evaluate the proposed RA, we developed an open-source software based on the proposed RA. The open-source framework aims to improve development productivity, facilitate interoperability, increase reusability, and expedite communication with domain experts. CONCLUSIONS: Our results showed that the proposed RA is flexible and capable of adapting interventions based on patients' needs, preferences, and context. Furthermore, developers and researchers can extend the proposed RA to various health care interventions.

Diversity-Aware Anonymization for Structured Health Data.

Patients' health data are captured by local hospital facilities, which has the potential for data analysis. However, due to privacy and legal concerns, local hospital facilities are unable to share the data with others which makes it difficult to apply data analysis and machine learning techniques over the health data. Analysis of such data across hospitals can provide valuable information to health professionals. Anonymization methods offer privacy-preserving solutions for sharing data for analysis purposes. In this paper, we propose a novel method for anonymizing and sharing data that addresses the record-linkage and attribute-linkage attack models. Our proposed method achieves anonymity by formulating and solving this problem as a constrained optimization problem which is based on the k-anonymity, l-diversity, and t-closeness privacy models. The proposed method has been evaluated with respect to the utility and privacy of data after anonymization in comparison to the original data.

Monitoring Motor Activity Data for Detecting Patients' Depression Using Data Augmentation and Privacy-Preserving Distributed Learning.

Wearable devices are currently being considered to collect personalized physiological information, which is lately being used to provide healthcare services to individuals. One application is detecting depression by utilization of motor activity signals collected by the ActiGraph wearable wristbands. However, to develop an accurate classification model, we require to use a sufficient volume of data from several subjects, taking the sensitivity of such data into account. Therefore, in this paper, we present an approach to extract classification models for predicting depression based on a new augmentation technique for motor activity data in a privacy-preserving fashion. We evaluate our approach against the state-of-the-art techniques and demonstrate its performance based on the mental health datasets associated with the Norwegian INTROducing Mental health through Adaptive Technology (INTROMAT) Project.

An HL7 FHIR and GraphQL approach for interoperability between heterogeneous Electronic Health Record systems.

Heterogeneities in data representation and care processes create interoperability complexity among Electronic Health Record systems (EHRs). We can resolve such data and process level heterogeneities by following consistent healthcare standards like Clinical Document Architecture (CDA), OpenEHR, and HL7 FHIR. However, these standards also differ at the structural and implementation level, making interoperability more complex. Hence, there is a need to investigate mechanisms that can resolve data level heterogeneity to achieve semantic data interoperability between heterogeneous systems. As a solution to this, we offer an architecture that utilizes a resource server based on GraphQL and HL7 FHIR that establishes communication between two heterogeneous EHRs. This paper describes how the proposed architecture is implemented to achieve interoperability between two heterogeneous EHRs, HL7 FHIR and OpenMRS. The presented approach establishes secure communication between the EHRs and provides accurate mappings that enable timely health information exchange between EHRs.

Attention-Based Deep Entropy Active Learning Using Lexical Algorithm for Mental Health Treatment.

With the increasing prevalence of Internet usage, Internet-Delivered Psychological Treatment (IDPT) has become a valuable tool to develop improved treatments of mental disorders. IDPT becomes complicated and labor intensive because of overlapping emotion in mental health. To create a usable learning application for IDPT requires diverse labeled datasets containing an adequate set of linguistic properties to extract word representations and segmentations of emotions. In medical applications, it is challenging to successfully refine such datasets since emotion-aware labeling is time consuming. Other known issues include vocabulary sizes per class, data source, method of creation, and baseline for the human performance level. This paper focuses on the application of personalized mental health interventions using Natural Language Processing (NLP) and attention-based in-depth entropy active learning. The objective of this research is to increase the trainable instances using a semantic clustering mechanism. For this purpose, we propose a method based on synonym expansion by semantic vectors. Semantic vectors based on semantic information derived from the context in which it appears are clustered. The resulting similarity metrics help to select the subset of unlabeled text by using semantic information. The proposed method separates unlabeled text and includes it in the next active learning mechanism cycle. Our method updates model training by using the new training points. The cycle continues until it reaches an optimal solution, and it converts all the unlabeled text into the training set. Our in-depth experimental results show that the synonym expansion semantic vectors help enhance training accuracy while not harming the results. The bidirectional Long Short-Term Memory (LSTM) architecture with an attention mechanism achieved 0.85 Receiver Operating Characteristic (ROC curve) on the blind test set. The learned embedding is then used to visualize the activated word's contribution to each symptom and find the psychiatrist's qualitative agreement. Our method improves the detection rate of depression symptoms from online forum text using the unlabeled forum texts.

Adaptive Elements in Internet-Delivered Psychological Treatment Systems: Systematic Review.

BACKGROUND: Internet-delivered psychological treatments (IDPTs) are built on evidence-based psychological treatment models, such as cognitive behavioral therapy, and are adjusted for internet use. The use of internet technologies has the potential to increase access to evidence-based mental health services for a larger proportion of the population with the use of fewer resources. However, despite extensive evidence that internet interventions can be effective in the treatment of mental health disorders, user adherence to such internet intervention is suboptimal. OBJECTIVE: This review aimed to (1) inspect and identify the adaptive elements of IDPT for mental health disorders, (2) examine how system adaptation influences the efficacy of IDPT on mental health treatments, (3) identify the information architecture, adaptive dimensions, and strategies for implementing these interventions for mental illness, and (4) use the findings to create a conceptual framework that provides better user adherence and adaptiveness in IDPT for mental health issues. METHODS: The review followed the guidelines from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The research databases Medline (PubMed), ACM Digital Library, PsycINFO, CINAHL, and Cochrane were searched for studies dating from January 2000 to January 2020. Based on predetermined selection criteria, data from eligible studies were analyzed. RESULTS: A total of 3341 studies were initially identified based on the inclusion criteria. Following a review of the title, abstract, and full text, 31 studies that fulfilled the inclusion criteria were selected, most of which described attempts to tailor interventions for mental health disorders. The most common adaptive elements were feedback messages to patients from therapists and intervention content. However, how these elements contribute to the efficacy of IDPT in mental health were not reported. The most common information architecture used by studies was tunnel-based, although a number of studies did not report the choice of information architecture used. Rule-based strategies were the most common adaptive strategies used by these studies. All of the studies were broadly grouped into two adaptive dimensions based on user preferences or using performance measures, such as psychometric tests. CONCLUSIONS: Several studies suggest that adaptive IDPT has the potential to enhance intervention outcomes and increase user adherence. There is a lack of studies reporting design elements, adaptive elements, and adaptive strategies in IDPT systems. Hence, focused research on adaptive IDPT systems and clinical trials to assess their effectiveness are needed.

Internet-delivered mental health treatment systems in Scandinavia - A usability evaluation.

Mental health problems are a major public health concern worldwide. Approximately 50% of the population will experience mental problems during their life. Traditional treatment is based on psychopharmacotherapy or psychotherapy, with face-to-face interaction between the patient and the therapist. New technologies such as Internet-delivered treatments are seen as an opportunity to offer more scalable and cost-efficient treatments in the field of mental health. Despite the growing interest and new evidence supporting the effect of Internet-delivered treatments is it remarkably little research on how the technology and the usability of Internet-delivered treatment programs affects the treatment. In this paper, we propose a set of evaluation criteria for evaluating the usability and the responsive design of Internet-delivered treatment systems. By our knowledge we are the first to include usability and universal design principles in the evaluation of Internet-delivered treatment systems. Our findings indicate that despite the good treatment results and proven clinical effects, the systems in general have several issues regarding usability, universal design and outdated technology. Based on our findings we propose that there should be established guidelines for testing the usability and technology of Internet-delivered treatment systems.

A Model Driven Approach to the Design of a Gamified e-Learning System for Clinical Guidelines.

Clinical practice guidelines (CPGs) are indispensable in the practice of evidence-based medicine. However, the cost of effective CPG dissemination strategies is prohibitive and not cost-effective. Therefore, scalable strategies using available technology are needed. We describe a formal model-driven approach to design a gamified e-learning system for clinical guidelines. We employ gamification to increase user motivation and engagement in the training of guideline content. Our approach involves the use of models for different aspects of the system, an entity model for the clinical domain, a workflow model for the clinical processes and a game model to manage the training sessions. A game engine instantiates a training session by coupling the workflow and entity models to automatically generate questions based on the data in the model instances. Our approach is flexible and adaptive as it allows for easy updates of the guidelines, integration with different device interfaces and representation of any guideline.