Publications by authors named "Kikue Sato"

Article Synopsis
  • The study connects medical real-world data with geographic information to assess hospitalization impacts based on factors like patient location and medical history.* -
  • Air pollution, particularly exposure to NO2, SPM, and NOx, was identified as a significant risk factor, especially for respiratory diseases leading to long-term hospitalization.* -
  • Patient characteristics at the time of admission were important, but environmental factors, particularly pollutant exposure over the previous year, were found to be critical for predicting hospital stays.*
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Non-tuberculous mycobacterial infection (NTM) is rare in healthy children, with lymphadenitis being the most common presentation. Immunocompromised populations are known to be at high risk, but the clinical picture of NTM infection in pediatric hematology/oncology patients is unclear. In this nationwide retrospective analysis of patients under the age of 40 treated in Japanese pediatric hematology/oncology departments who developed NTM infection between January 2010 and December 2020, 36 patients were identified: 21 patients with hematopoietic stem cell transplantation (HSCT) and 15 non-transplant patients.

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It has been reported that the severity and lethality of Covid-19 are associated with coexisting underlying diseases (hypertension, diabetes, etc.) and cardiovascular diseases (coronary artery disease, atrial fibrillation, heart failure, etc.) that increase with age, but environmental exposure such as air pollutants may also be a risk factor for mortality.

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Article Synopsis
  • Predicting outcomes for patients with complex medical conditions using electronic medical records (EMRs) is challenging.
  • A machine learning model was developed to predict the prognosis of cancer patients by analyzing Japanese clinical text records along with other clinical data.
  • The study showed that including clinical text significantly improved the accuracy of predicting mortality in cancer patients, indicating a promising application for this approach.
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Purpose: Location visualization is essential for locating people/objects, improving efficiency, and preventing accidents. In hospitals, Wi-Fi, Bluetooth low energy (BLE) Beacon, indoor messaging system, and similar methods have generally been used for tracking, with Wi-Fi and BLE being the most common. Recently, nurses are increasingly using mobile devices, such as smartphones and tablets, while shifting.

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Recently, visualizing location of people and things in a hospital has become an issue particularly for improving work efficiency and incident prevention. Although radio frequency waves such as Wi-Fi and Bluetooth are commonly used in indoor positioning, they have several limitations owing to their physical characteristics. We proposed in-hospital hybrid positioning technique, involving a combination of radio waves and geomagnetic fingerprinting techniques.

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A context aware nursing support system to push right information to the right person at the right moment is the key to increase clinical safety under a computerized hospital. We prototyped a system which obtains context from positions of nurses and list of expected clinical procedures. A WoZ test showed that the proposed approach has potential to decrease incidents caused by information delivery error.

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We developed a tool that allows a medical facility to offer efficient nursing care with limited human resources by optimizing the distribution of hospital ward nursing tasks. The use of information and communications technology to visualize daily workloads and make use of quantified workload data is important for identifying management elements that allow the efficient allocation of personnel and tasks. The goal of this study was to utilize data from the ward management tool that we developed to consider workflow processes for nursing staff and the relationships between the nursing competence of the nursing staff and the patients' conditions and how these impact on workloads.

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