Digital Therapeutics for COPD Patient Self-Management: Needs Analysis and Design Study.

Timely management of Chronic Obstructive Pulmonary Disease (COPD) exacerbations can improve recovery and reduce the risk of hospitalization. Digital therapeutics are digital interventions, based on best evidence, designed to provide home-based, patient-centered and pervasive self-management support to patients. Digital therapeutics can be effectively used to offer personalized and explainable self-management and behaviour modification resources to patients to reduce the burden of COPD, especially the prevention of acute COPD exacerbations.

A community-of-practice-based evaluation methodology for knowledge intensive computational methods and its application to multimorbidity decision support.

Objective: The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings.

Towards an Adaptive Clinical Transcription System for In-Situ Transcribing of Patient Encounter Information.

Electronic patient charts are essential for follow-up and multi-disciplinary care, but either take up an exorbitant amount of time during the patient encounter using a key-stroke entry system, or suffer from poor recall when made long after the encounter. Transcribing in-situ, natural dictations by the clinician, recorded during the encounter, with minimal workflow impact, is a promising solution. However, human transcription requires significant manual resources, whereas automated transcription currently lacks the accuracy for specialized clinical language.

Towards a framework for comparing functionalities of multimorbidity clinical decision support: A literature-based feature set and benchmark cases.

Multimorbidity, the coexistence of two or more health conditions, has become more prevalent as mortality rates in many countries have declined and their populations have aged. Multimorbidity presents significant difficulties for Clinical Decision Support Systems (CDSS), particularly in cases where recommendations from relevant clinical guidelines offer conflicting advice. A number of research groups are developing computer-interpretable guideline (CIG) modeling formalisms that integrate recommendations from multiple Clinical Practice Guidelines (CPGs) for knowledge-based multimorbidity decision support.

Semantic knowledge modeling and evaluation of argument Theory to develop dialogue based patient education systems for chronic disease Self-Management.

Background: To improve understanding as well as uptake of health educational material, it should be tailored to informational needs, offer intuitive modes of interaction, and present credible evidence for health claims. Dialogue systems go some way in meeting these requirements, as they emulate interactive and intuitive person-to-person communication. However, most works do not offer a formal model nor modelling process to structure dialogue content, and do not focus on ensuring credibility.

Staged reflexive artificial intelligence driven testing algorithms for early diagnosis of pituitary disorders.

Background: Sellar masses (SM) frequently present with insidious hormonal dysfunction. We previously showed that, by utilizing a combined reflex/reflecting approach involving a laboratory clinician (LC) on common endocrine test results requested by non-specialists, and subsequently adding further warranted tests, previously undiagnosed pituitary disorders can be identified. However, manually employing these strategies by an LC is not feasible for wider screening of pituitary disorders.

Decision support for comorbid conditions via execution-time integration of clinical guidelines using transaction-based semantics and temporal planning.

In case of comorbidity, i.e., multiple medical conditions, Clinical Decision Support Systems (CDSS) should issue recommendations based on all relevant disease-related Clinical Practice Guidelines (CPG).

Using Knowledge Graphs to Plausibly Infer Missing Associations in EMR Data.

Electronic Medical Records (EMRs) are increasingly being deployed at primary points of care and clinics for digital record keeping, increasing productivity and improving communication. In practice, however, there still exists an often incomplete picture of patient profiles, not only because of disconnected EMR systems but also due to incomplete EMR data entry - often caused by clinician time constraints and lack of data entry restrictions. To complete a patient's partial EMR data, we plausibly infer missing causal associations between medical EMR concepts, such as diagnoses and treatments, for situations that lack sufficient raw data to enable machine learning methods.

Ontology-Based Personalized Cognitive Behavioural Plans for Patients with Mild Depression.

Cognitive Behavioural Therapy (CBT) is an action-oriented psychotherapy that combines cognitive and behavioural techniques for psychosocial treatment for depression, and is considered by many to be the golden standard in psychotherapy. More recently, computerized CBT (CCBT) has been deployed to help increase availability and access to this evidence-based therapy. In this vein, a CBT ontology, as a shared common understanding of the domain, can facilitate the aggregation, verification, and operationalization of computerized CBT knowledge.

Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods.

In a digitally enabled healthcare setting, we posit that an individual's current location is pivotal for supporting many virtual care services-such as tailoring educational content towards an individual's current location, and, hence, current stage in an acute care process; improving activity recognition for supporting self-management in a home-based setting; and guiding individuals with cognitive decline through daily activities in their home. However, unobtrusively estimating an individual's indoor location in real-world care settings is still a challenging problem. Moreover, the needs of location-specific care interventions go beyond absolute coordinates and require the individual's discrete semantic location; i.

Smartphone Apps for the Treatment of Mental Disorders: Systematic Review.

Background: Smartphone apps are an increasingly popular means for delivering psychological interventions to patients suffering from a mental disorder. In line with this popularity, there is a need to analyze and summarize the state of the art, both from a psychological and technical perspective.

Objective: This study aimed to systematically review the literature on the use of smartphones for psychological interventions.

Using an Artificial Intelligence-Based Argument Theory to Generate Automated Patient Education Dialogues for Families of Children with Juvenile Idiopathic Arthritis.

Juvenile Idiopathic Arthritis (JIA) is the most common chronic rheumatic disease of childhood, with outcomes including pain, prolonged dependence on medications, and disability. Parents of children with JIA report being overwhelmed by the volume of information in the patient education materials that are available to them. This paper addresses this educational gap by applying an artificial intelligence method, based on an extended model of argument, to design and implement a dialogue system that allows users get the educational material they need, when they need it.

Proactively Guiding Patients Through ADL via Knowledge-Based and Context-Driven Activity Recognition.

Assisted Ambient Living (AAL) focuses on self-sufficiency, assisting disabled people to perform activities of daily living (ADL) by automating assistive actions in smart environments. Importantly, AAL provides opportunities for dynamically guiding patients with a cognitive decline through an ADL. Activity recognition is a pivotal task since it allows detecting when an ADL is started by recognizing its constituent activities.

Providing Comorbid Decision Support via the Integration of Clinical Practice Guidelines at Execution-Time by Leveraging Medical Linked Open Datasets.

Clinical Decision Support Systems (CDSS) utilize computerized Clinical Practice Guidelines (CPG) to deliver evidence-based care recommendations. However, when dealing with comorbidity (i.e.

A Digital Health Platform to Deliver Tailored Early Stimulation Programs for Children with Developmental Delays.

Developmental delay is a deviation from the regular development of normative milestones during childhood. Early stimulation is a standardized and straightforward technique to support children with developmental delays (aged 0-3 years) in reaching basic motor skills, which are essential for the execution of everyday activities, such as playing, feeding and locomotion. In doing so, early stimulation reduces the chances of permanent motor impairment, thus allowing the child to live a more functional life.

Execution-time integration of clinical practice guidelines to provide decision support for comorbid conditions.

Patients with multiple medical conditions (comorbidity) pose major challenges to clinical decision support systems, since the different Clinical Practice Guidelines (CPG) often involve adverse interactions, such as drug-drug or drug-disease interactions. Moreover, opportunities often exist for optimizing care and resources across multiple CPG. These challenges have been taken up in the state of the art, with many approaches focusing on the static integration of comorbid CIG.

Semantics-based plausible reasoning to extend the knowledge coverage of medical knowledge bases for improved clinical decision support.

Background: Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians' experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge.

The Shared Decision Making Frontier: a Feasibility and Usability Study for Managing Non-Critical Chronic Illness by Combining Behavioural & Decision Theory with Online Technology.

The objective of this study is to determine if shared decisions for managing non-critical chronic illness, made through an online biomedical technology intervention, us feasible and usable. The technology intervention incorporates behavioural and decision theories to increase patient engagement, and ultimately long term adherence to health behaviour change. We devised the iheart web intervention as a "proof of concept" in five phases.

A Mobile and Intelligent Patient Diary for Chronic Disease Self-Management.

By involving patients in their own long-term care, patient self-management approaches aim to increase self-sufficiency and reduce healthcare costs. For example, electronic patient diaries enable patients to collect health data autonomously, increasing self-reliance and reducing strain on health professionals. By deploying patient diaries on mobile platforms, health data collection can occur at any time and place, increasing the mobility of chronic patients who typically need to enter health data frequently.

Effect of different arm loads on the position of the scapula in abduction postures.

Objective: The objective of this study is to determine the relation between arm load and the three-dimensional shoulder orientations.

Background: Analysis of a musculo-skeletal system by means of an inverse dynamical simulation requires postural data of the bony elements involved. For the shoulder, the positions of the clavicle and the scapula are difficult to record due to the skin displacement.

Electromyography of shoulder muscles in relation to force direction.

In a static force task the electromyographic level of 14 shoulder muscles including 3 rotator cuff muscles was related to force direction. Surface and wire electrodes were used. The force direction of maximal electromyography (principal action) was identified for every muscle.

Evaluation of a sitting aid: the back-up.

The effects of a portable back support, the Back-Up, were tested in 28 variables. Both subjective and objective physical load measures were recorded during sitting with and without Back-Up, most of them during VDU work. The main result was that the posture of the upper back and neck/head was improved by the Back-Up.

Effects of external load and abduction angle on EMG level of shoulder muscles during isometric action.

Goal of the study was to investigate the effect of muscle length and load on EMG of shoulder muscles. Surface EMG's were taken of seven subjects during isometric abduction. One maximal and three submaximal tasks were performed.

The constrained control of force and position in multi-joint movements.

In many arm or leg movements the hand or foot has to exert an external force on the environment. Based on an inverse dynamical analysis of cycling, it is shown that the distribution of net moments in the joints needed to control the direction of the external force is often opposite to the direction of joint displacements associated with this task. Kinetic and kinematic data were obtained from five experienced cyclists during ergometer cycling by means of film analysis and pedal force measurement.