Co-Design, Development, and Evaluation of a Mobile Solution to Improve Medication Adherence in Cancer: Design Science Research Approach.

Background: Medication nonadherence negatively impacts the health outcomes of people with cancer as well as health care costs. Digital technologies present opportunities to address this health issue. However, there is limited evidence on how to develop digital interventions that meet the needs of people with cancer, are perceived as useful, and are potentially effective in improving medication adherence.

An AI based smart-phone system for asbestos identification.

Asbestos identification is a complex environmental and economic challenge. Typical commercial identification of asbestos involves sending samples to a laboratory where someone learned in the field uses light microscopy and specialized mounting to identify the morphologically distinct signatures of Asbestos. In this work we investigate the use of a portable (30x) microscope which works with a smart phone camera to develop an image recognition system.

Development of a person-centred digital platform for the long-term support of people living with an adult-onset genetic disease predisposition: a mixed-methods study protocol.

Introduction: Individuals at an inherited high-risk of developing adult-onset disease, such as breast cancer, are rare in the population. These individuals require lifelong clinical, psychological and reproductive assistance. After a positive germline test result, clinical genetic services provide support and care coordination.

Weed Detection Using Deep Learning: A Systematic Literature Review.

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds.

A Nudge-Inspired AI-Driven Health Platform for Self-Management of Diabetes.

Diabetes mellitus is a serious chronic disease that affects the blood sugar levels in individuals, with current predictions estimating that nearly 578 million people will be affected by diabetes by 2030. Patients with type II diabetes usually follow a self-management regime as directed by a clinician to help regulate their blood glucose levels. Today, various technology solutions exist to support self-management; however, these solutions tend to be independently built, with little to no research or clinical grounding, which has resulted in poor uptake.

Investigation of Intervention Solutions to Enhance Adherence to Oral Anticancer Medicines in Adults: Overview of Reviews.

Background: Adherence to anticancer medicines is critical for the success of cancer treatments; however, nonadherence remains challenging, and there is limited evidence of interventions to improve adherence to medicines in patients with cancer.

Objective: This overview of reviews aimed to identify and summarize available reviews of interventions to improve adherence to oral anticancer medicines in adult cancer survivors.

Methods: A comprehensive search of 7 electronic databases was conducted by 2 reviewers who independently conducted the study selection, quality assessment using the A Measurement Tool to Assess Systematic Reviews 2, and data extraction.

Patient clustering using dynamic partitioning on correlated and uncertain biomedical data.

Background and objectivesHealth professionals look for specific patterns by correlating multiple physiological data in the process of deciding treatments to remedy clinical abnormalities. Biomedical data exhibit some common patterns in the event of identical clinical illnesses. The primary interest of this work is automatic discovery of such patterns in vital sign data (e.

A clinical decision-making mechanism for context-aware and patient-specific remote monitoring systems using the correlations of multiple vital signs.

Background And Objectives: In home-based context-aware monitoring patient's real-time data of multiple vital signs (e.g. heart rate, blood pressure) are continuously generated from wearable sensors.