International journal of medical informatics
Apr 18, 2020
OBJECTIVE: Mental or substance use disorders (M/SUD) are major contributors of disease burden with high risk for hospital readmissions. We sought to develop and evaluate a readmission model using a machine learning (ML) approach.
Diseases involve complex modifications to the cellular machinery. The gene expression profile of the affected cells contains characteristic patterns linked to a disease. Hence, new biological knowledge about a disease can be extracted from these prof...
Journal of child psychology and psychiatry, and allied disciplines
Apr 1, 2020
BACKGROUND: Children with attention-deficit/hyperactivity disorder (ADHD) have a high risk for substance use disorders (SUDs). Early identification of at-risk youth would help allocate scarce resources for prevention programs.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Mar 4, 2020
In this work, we utilize a combination of free-text and structured data to build Acute Respiratory Distress Syndrome(ARDS) prediction models and ARDS phenotype clusters. We derived 'Patient Context Vectors' representing patientspecific contextual ARD...
International journal of medical informatics
Mar 3, 2020
OBJECTIVE: Predicting the risk of falls in advance can benefit the quality of care and potentially reduce mortality and morbidity in the older population. The aim of this study was to construct and validate an electronic health record-based fall risk...
Patients with chronic obstructive pulmonary disease (COPD) repeat acute exacerbations (AE). Global Initiative for Chronic Obstructive Lung Disease (GOLD) is only available for patients in stable phase. Currently, there is a lack of assessment and pre...
OBJECTIVES: Machine Learning and Artificial Intelligence (AI) are rapidly growing in capability and increasingly applied to model outcomes and complications within medicine. In spinal surgery, post-operative surgical site infections (SSIs) are a rare...
OBJECTIVE: People with epilepsy are at increased risk for mental health comorbidities. Machine-learning methods based on spoken language can detect suicidality in adults. This study's purpose was to use spoken words to create machine-learning classif...
IMPORTANCE: Accurate risk stratification of patients with heart failure (HF) is critical to deploy targeted interventions aimed at improving patients' quality of life and outcomes.
BMC medical informatics and decision making
Dec 17, 2019
BACKGROUND: Electronic health records (EHRs) provide possibilities to improve patient care and facilitate clinical research. However, there are many challenges faced by the applications of EHRs, such as temporality, high dimensionality, sparseness, n...