Jiyoun Song
Orcid: 0000-0003-0362-0670
According to our database1,
Jiyoun Song
authored at least 20 papers
between 2020 and 2023.
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Bibliography
2023
Uncovering hidden trends: identifying time trajectories in risk factors documented in clinical notes and predicting hospitalizations and emergency department visits during home health care.
J. Am. Medical Informatics Assoc., October, 2023
Predicting emergency department visits and hospitalizations for patients with heart failure in home healthcare using a time series risk model.
J. Am. Medical Informatics Assoc., September, 2023
HomeADScreen: Developing Alzheimer's disease and related dementia risk identification model in home healthcare.
Int. J. Medical Informatics, September, 2023
Machine learning applied to electronic health record data in home healthcare: A scoping review.
Int. J. Medical Informatics, 2023
2022
2.45 e-RMS Low-Random-Noise, 598.5 mW Low-Power, and 1.2 kfps High-Speed 2-Mp Global Shutter CMOS Image Sensor With Pixel-Level ADC and Memory.
IEEE J. Solid State Circuits, 2022
Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care.
J. Biomed. Informatics, 2022
Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians.
J. Am. Medical Informatics Assoc., 2022
Design and analysis on low-power and low-noise single slope ADC for digital pixel sensors.
Proceedings of the Imaging Sensors and Systems 2022, online, January 15-26, 2022, 2022
Is Auto-generated Transcript of Patient-Nurse Communication Ready to Use for Identifying the Risk for Hospitalizations or Emergency Department Visits in Home Health Care? A Natural Language Processing Pilot Study.
Proceedings of the AMIA 2022, 2022
A Full and Approximated Model for Predicting Infection-Related Adverse Events in a Home Health Care Population.
Proceedings of the AMIA 2022, 2022
Capturing Concerns about Patient Deterioration in Narrative Documentation in Home Healthcare.
Proceedings of the AMIA 2022, 2022
Heart Failure Patient Characteristics and Symptoms Documented in Home Health Care Clinical Notes are Associated with Emergency Department Visits and Hospitalizations.
Proceedings of the AMIA 2022, 2022
2021
Predicting pressure injury using nursing assessment phenotypes and machine learning methods.
J. Am. Medical Informatics Assoc., 2021
"A catalyst for action": Factors for implementing clinical risk prediction models of infection in home care settings.
J. Am. Medical Informatics Assoc., 2021
A 2.6 e-rms Low-Random-Noise, 116.2 mW Low-Power 2-Mp Global Shutter CMOS Image Sensor with Pixel-Level ADC and In-Pixel Memory.
Proceedings of the 2021 Symposium on VLSI Circuits, Kyoto, Japan, June 13-19, 2021, 2021
Home Healthcare Clinicians' Perspectives on Electronic Health Records: A Qualitative Study.
Proceedings of the Nurses and Midwives in the Digital Age - Selected Papers, Posters and Panels from the 15th International Congress in Nursing Informatics, Virtual Event, 23 August, 2021
Identifying Heart Failure Symptoms and Poor Self-Management in Home Healthcare: A Natural Language Processing Study.
Proceedings of the Nurses and Midwives in the Digital Age - Selected Papers, Posters and Panels from the 15th International Congress in Nursing Informatics, Virtual Event, 23 August, 2021
Natural Language Processing Algorithm to Detect Terms Representing Risk of Hospitalization or Emergency Department Visits during Home Health Care.
Proceedings of the AMIA 2021, American Medical Informatics Association Annual Symposium, San Diego, CA, USA, October 30, 2021, 2021
Identifying Narrative Documentation of Clinician Concern about Patient Deterioration in Home Healthcare: A Text Mining Study.
Proceedings of the AMIA 2021, American Medical Informatics Association Annual Symposium, San Diego, CA, USA, October 30, 2021, 2021
2020
5.5 A 2.1e<sup>-</sup> Temporal Noise and -105dB Parasitic Light Sensitivity Backside-Illuminated 2.3µm-Pixel Voltage-Domain Global Shutter CMOS Image Sensor Using High-Capacity DRAM Capacitor Technology.
Proceedings of the 2020 IEEE International Solid- State Circuits Conference, 2020