AIMC Journal:
Journal of the American Medical Informatics Association : JAMIA

Showing 231 to 240 of 493 articles

A framework for the oversight and local deployment of safe and high-quality prediction models.

Journal of the American Medical Informatics Association : JAMIA
Artificial intelligence/machine learning models are being rapidly developed and used in clinical practice. However, many models are deployed without a clear understanding of clinical or operational impact and frequently lack monitoring plans that can...

Automated medical literature screening using artificial intelligence: a systematic review and meta-analysis.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We aim to investigate the application and accuracy of artificial intelligence (AI) methods for automated medical literature screening for systematic reviews.

Identifying daily activities of patient work for type 2 diabetes and co-morbidities: a deep learning and wearable camera approach.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: People are increasingly encouraged to self-manage their chronic conditions; however, many struggle to practise it effectively. Most studies that investigate patient work (ie, tasks involved in self-management and contexts influencing such ...

Use of unstructured text in prognostic clinical prediction models: a systematic review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This systematic review aims to assess how information from unstructured text is used to develop and validate clinical prognostic prediction models. We summarize the prediction problems and methodological landscape and determine whether usi...

Assessing socioeconomic bias in machine learning algorithms in health care: a case study of the HOUSES index.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Artificial intelligence (AI) models may propagate harmful biases in performance and hence negatively affect the underserved. We aimed to assess the degree to which data quality of electronic health records (EHRs) affected by inequities rel...

Extracting seizure frequency from epilepsy clinic notes: a machine reading approach to natural language processing.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Seizure frequency and seizure freedom are among the most important outcome measures for patients with epilepsy. In this study, we aimed to automatically extract this clinical information from unstructured text in clinical notes. If success...

Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This study aims to establish an informative dynamic prediction model of treatment outcomes using follow-up records of tuberculosis (TB) patients, which can timely detect cases when the current treatment plan may not be effective.

A web-based tool for automatically linking clinical trials to their publications.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Evidence synthesis teams, physicians, policy makers, and patients and their families all have an interest in following the outcomes of clinical trials and would benefit from being able to evaluate both the results posted in trial registrie...

Defining AMIA's artificial intelligence principles.

Journal of the American Medical Informatics Association : JAMIA
Recent advances in the science and technology of artificial intelligence (AI) and growing numbers of deployed AI systems in healthcare and other services have called attention to the need for ethical principles and governance. We define and provide a...

Gender-sensitive word embeddings for healthcare.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To analyze gender bias in clinical trials, to design an algorithm that mitigates the effects of biases of gender representation on natural-language (NLP) systems trained on text drawn from clinical trials, and to evaluate its performance.