Bhattacharya, Moumita Jurkovitz, Claudine Shatkay, Hagit
Published in
Journal of biomedical informatics
Patients associated with multiple co-occurring health conditions often face aggravated complications and less favorable outcomes. Co-occurring conditions are especially prevalent among individuals suffering from kidney disease, an increasingly widespread condition affecting 13% of the general population in the US. This study aims to identify and ch...
Ma, Handong Sheng, Wenbo Li, Jiyu Hou, Lengchen Yang, Jiafang Cai, Junjie Xu, Wenxiang Zhang, Shaodian
Published in
Journal of biomedical informatics
Venous thromboembolism (VTE) is a common vascular disease and potentially fatal complication during hospitalization, and so the early identification of VTE risk is of significant importance. Compared with traditional scale assessments, machine learning methods provide new opportunities for precise early warning of VTE from clinical medical records....
Narayanan, Sankaran Achan, Pradeep Rangan, P Venkat Rajan, Sreeranga P
Published in
Journal of biomedical informatics
Assertions, such as negation and speculation, alter the meaning of clinical findings ('concepts') in Electronic Health Records. Accurate assertion detection is vital to the identification of target findings in clinical decision support systems. Diverse clinical concepts and assertion modifiers embedded within longer sentences add to the challenge o...
Kersloot, Martijn G Jacobsen, Annika Groenen, Karlijn H J Dos Santos Vieira, Bruna Kaliyaperumal, Rajaram Abu-Hanna, Ameen Cornet, Ronald 't Hoen, Peter A C Roos, Marco Schultze Kool, Leo
...
Published in
Journal of biomedical informatics
Existing methods to make data Findable, Accessible, Interoperable, and Reusable (FAIR) are usually carried out in a post hoc manner: after the research project is conducted and data are collected. De-novo FAIRification, on the other hand, incorporates the FAIRification steps in the process of a research project. In medical research, data is often c...
Li, Liang Jiang, Yuewen Huang, Biqing
Published in
Journal of biomedical informatics
Influenza is one of the most common infectious diseases worldwide, which causes a considerable economic burden on hospitals and other healthcare costs. Predicting new and urgent trends in epidemiological data is an effective way to prevent influenza outbreaks and protect public health. Traditional autoregressive(AR) methods and new deep learning mo...
Shivers, Jennifer Amlung, Joseph Ratanaprayul, Natschja Rhodes, Bryn Biondich, Paul
Published in
Journal of biomedical informatics
Narrative clinical guidelines often contain assumptions, knowledge gaps, and ambiguities that make translation into an electronic computable format difficult. This can lead to divergences in electronic implementation, reducing the usefulness of collected data outside of that implementation setting. This work set out to evolve guidelines-based data ...
Kopylov, Arthur T Petrovsky, Denis V Stepanov, Alexander A Rudnev, Vladimir R Malsagova, Kristina A Butkova, Tatyana V Zakharova, Natalya V Kostyuk, Georgy P Kulikova, Liudmila I Enikeev, Dmitry V
...
Published in
Journal of biomedical informatics
The association between cancer risk and schizophrenia is widely debated. Despite many epidemiological studies, there is still no strong evidence regarding the molecular basis for the comorbidity between these two pathological conditions. The vast majority of assays have been performed using clinical records of schizophrenic patients or those underg...
Swaraj, Aman Verma, Karan Kaur, Arshpreet Singh, Ghanshyam Kumar, Ashok Melo de Sales, Leandro
Published in
Journal of biomedical informatics
Time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease. The novel coronavirus disease, COVID-19, is spreading rapidly all over the world. The countries with dense populations, in particular, such as India, await imminent risk in tackling the epidem...
El-Gayar, Omar Ofori, Martinson Nawar, Nevine
Published in
Journal of biomedical informatics
Diabetes prevalence has become a global crisis. Due to the substantial rise in smartphone use, a variety of mobile interventions have been developed to help improve the clinical outcomes of diabetes patients. This study seeks to examine specific behavior change theories and techniques used in the design of self-management mobile app-based intervent...
Xiao, Xueli Wei, Guanhao Zhou, Li Pan, Yi Jing, Huan Zhao, Emily Yuan, Yilian
Published in
Journal of biomedical informatics
Electronic health records contain patient's information that can be used for health analytics tasks such as disease detection, disease progression prediction, patient profiling, etc. Traditional machine learning or deep learning methods treat EHR entities as individual features, and no relationships between them are taken into consideration. We pro...
