Development of machine learning models predicting mortality using routinely collected observational health data from 0-59 months old children admitted to an intensive care unit in Bangladesh: critical role of biochemistry and haematology data.

Introduction: Treatment in the intensive care unit (ICU) generates complex data where machine learning (ML) modelling could be beneficial. Using routine hospital data, we evaluated the ability of multiple ML models to predict inpatient mortality in a paediatric population in a low/middle-income country.

Method: We retrospectively analysed hospital record data from 0-59 months old children admitted to the ICU of Dhaka hospital of International Centre for Diarrhoeal Disease Research, Bangladesh.

The potential and paradoxes of eHealth research for digitally marginalised groups: A qualitative meta-review.

Whilst the transformation towards digital healthcare is accelerating, there is still a substantial risk of excluding people with a distance to the online world. Groups like people with a low socioeconomic position, people with a migrant background or the elderly, who are already most at risk of experiencing health inequalities, are simultaneously experiencing increased digital exclusion. Researchers play a role in determining how eHealth access is framed and can thus impact how the barriers to its use are addressed.

Kaleidoscopic integration: Advancing the integration of incommensurable knowledge in healthcare guidelines.

The integration of different types of knowledge in epistemically hierarchical settings remains one of the greatest challenges when developing standards for healthcare practices. In this article, we open up the notion of knowledge integration and empirically examine the various ways in which different types of knowledge interact and can be integrated. To allow us to focus on the diverse forms of knowledge as well as their interaction and integration, we combine Moreira's work on repertoires of evaluation with that of Dewulf and Bouwen on frame interactions.

Capturing Emerging Experiential Knowledge for Vaccination Guidelines Through Natural Language Processing: Proof-of-Concept Study.

Background: Experience-based knowledge and value considerations of health professionals, citizens, and patients are essential to formulate public health and clinical guidelines that are relevant and applicable to medical practice. Conventional methods for incorporating such knowledge into guideline development often involve a limited number of representatives and are considered to be time-consuming. Including experiential knowledge can be crucial during rapid guidance production in response to a pandemic but it is difficult to accomplish.

A HTA of what? Reframing through including patient perspectives in health technology assessment processes.

Objectives: Despite increasing emphasis on the inclusion of patient input in health technology assessment (HTA) in Europe in particular, questions remain as to the integration of patient insight alongside other HTA inputs. This paper aims to explore how HTA processes, while ensuring the scientific quality of assessments, "make do" with patient knowledge elicited through patients' involvement mechanisms.

Methods: The qualitative study analyzed institutional HTA and patient involvement in four European country contexts.