Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI.

Artificial intelligence (AI) systems hold great promise as decision-support tools, but we must be able to identify and understand their inevitable mistakes if they are to fulfill this potential. This is particularly true in domains where the decisions are high-stakes, such as law, medicine, and the military. In this Perspective, we describe the particular challenges for AI decision support posed in military coalition operations.

A genetic programming approach to development of clinical prediction models: A case study in symptomatic cardiovascular disease.

Background: Genetic programming (GP) is an evolutionary computing methodology capable of identifying complex, non-linear patterns in large data sets. Despite the potential advantages of GP over more typical, frequentist statistical approach methods, its applications to survival analyses are rare, at best. The aim of this study was to determine the utility of GP for the automatic development of clinical prediction models.

TRAK App Suite: A Web-Based Intervention for Delivering Standard Care for the Rehabilitation of Knee Conditions.

Background: Standard care for the rehabilitation of knee conditions involves exercise programs and information provision. Current methods of rehabilitation delivery struggle to keep up with large volumes of patients and the length of treatment required to maximize the recovery. Therefore, the development of novel interventions to support self-management is strongly recommended.

FlexiTerm: a flexible term recognition method.

Background: The increasing amount of textual information in biomedicine requires effective term recognition methods to identify textual representations of domain-specific concepts as the first step toward automating its semantic interpretation. The dictionary look-up approaches may not always be suitable for dynamic domains such as biomedicine or the newly emerging types of media such as patient blogs, the main obstacles being the use of non-standardised terminology and high degree of term variation.

Results: In this paper, we describe FlexiTerm, a method for automatic term recognition from a domain-specific corpus, and evaluate its performance against five manually annotated corpora.

Designing and implementing telemonitoring for early detection of deterioration in chronic disease: defining the requirements.

Patients with chronic disease may suffer frequent acute deteriorations and associated increased risk of hospitalisation. Earlier detection of these could enable successful intervention, improving patients' well-being and reducing costs; however, current telemonitoring systems do not achieve this effectively. We conducted a qualitative study using stakeholder interviews to define current standards of care and user requirements for improved early detection telemonitoring.

Bridging two translation gaps: a new informatics research agenda for telemonitoring of chronic disease.

Objective: To propose a research agenda that addresses technological and other knowledge gaps in developing telemonitoring solutions for patients with chronic diseases, with particular focus on detecting deterioration early enough to intervene effectively.

Design: A mixed methods approach incorporating literature review, key informant, and focus group interviews to gain an in-depth, multidisciplinary understanding of current approaches, and a roadmapping process to synthesise a research agenda.

Results: Counter to intuition, the research agenda for early detection of deterioration in patients with chronic diseases is not only primarily about advances in sensor technology but also much more about the problems of clinical specification, translation, and interfacing.

Detecting deterioration in patients with chronic disease using telemonitoring: navigating the 'trough of disillusionment'.

Objectives: To examine the evidence base for telemonitoring designed for patients who have chronic obstructive pulmonary disease and heart failure, and to assess whether telemonitoring fulfils the principles of monitoring and is ready for implementation into routine settings.

Design: Qualitative data collection using interviews and participation in a multi-path mapping process.

Participants: Twenty-six purposively selected informants completed semi-structured interviews and 24 individuals with expertise in the relevant clinical and informatics domains from academia, industry, policy and provider organizations and participated in a multi-path mapping workshop.

Information quality in proteomics.

Proteomics, the study of the protein complement of a biological system, is generating increasing quantities of data from rapidly developing technologies employed in a variety of different experimental workflows. Experimental processes, e.g.

Universal metrics for quality assessment of protein identifications by mass spectrometry.

Increasing numbers of large proteomic datasets are becoming available. As attempts are made to interpret these datasets and integrate them with other forms of genomic data, researchers are becoming more aware of the importance of data quality with respect to protein identification. We present three simple and universal metrics that describe different aspects of the quality of protein identifications by peptide mass fingerprinting.