相关论文: FRAMR-EMR: Framework for Prognostic Predictive Mod…
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor…
Dynamic predictive modelling using electronic health record (EHR) data has gained significant attention in recent years. The reliability and trustworthiness of such models depend heavily on the quality of the underlying data, which is, in…
The present research tackles the difficulty of predicting osteoporosis risk via machine learning (ML) approaches, emphasizing the use of explainable artificial intelligence (XAI) to improve model transparency. Osteoporosis is a significant…
Longitudinal data in electronic health records (EHRs) represent an individual`s clinical history through a sequence of codified concepts, including diagnoses, procedures, medications, and laboratory tests. Generative pre-trained…
During the growing popularity of electronic medical records, electronic medical record (EMR) data has exploded increasingly. It is very meaningful to retrieve high quality EMR in mass data. In this paper, an EMR value network with retrieval…
Predicting the health risks of patients using Electronic Health Records (EHR) has attracted considerable attention in recent years, especially with the development of deep learning techniques. Health risk refers to the probability of the…
Most of the existing medicine recommendation systems that are mainly based on electronic medical records (EMRs) are significantly assisting doctors to make better clinical decisions benefiting both patients and caregivers. Even though the…
Accurate prediction of clinical outcomes using Electronic Health Records (EHRs) is critical for early intervention, efficient resource allocation, and improved patient care. EHRs contain multimodal data, including both structured data and…
Referral workflow inefficiencies, including misaligned referrals and delays, contribute to suboptimal patient outcomes and higher healthcare costs. In this study, we investigated the possibility of predicting procedural needs based on…
Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data…
In this study, we propose a novel framework that utilizes deep learning (DL) and attention mechanisms to predict the radiographic progression of patellofemoral osteoarthritis (PFOA) over a period of seven years. This study included subjects…
Disease-modifying management aims to prevent deterioration and progression of the disease, not just relieve symptoms. Unfortunately, the development of necessary therapies is often hampered by the failure to recognize the presymptomatic…
Knee osteoarthritis (KOA) is a disease that impairs knee function and causes pain. A radiologist reviews knee X-ray images and grades the severity level of the impairments according to the Kellgren and Lawrence grading scheme; a five-point…
This study addresses a critical gap in the healthcare system by developing a clinically meaningful, practical, and explainable disease surveillance system for multiple chronic diseases, utilizing routine EHR data from multiple U.S.…
The use of Electronic Health Records (EHRs) has increased dramatically in the past 15 years, as, it is considered an important source of managing data od patients. The EHRs are primary sources of disease diagnosis and demographic data of…
Patient similarity assessment, which identifies patients similar to a given patient, can help improve medical care. The assessment can be performed using Electronic Medical Records (EMRs). Patient similarity measurement requires converting…
Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease…
Osteoporosis is a common condition that increases fracture risk, especially in older adults. Early diagnosis is vital for preventing fractures, reducing treatment costs, and preserving mobility. However, healthcare providers face challenges…
Electronic health record (EHR) data are becoming an increasingly common data source for understanding clinical risk of acute events. While their longitudinal nature presents opportunities to observe changing risk over time, these analyses…
Hospitals struggle to predict critical outcomes. Traditional early warning systems, like NEWS and MEWS, rely on static variables and fixed thresholds, limiting their adaptability, accuracy, and personalization. We previously developed the…