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BACKGROUND: Malnutrition is a serious health risk facing older people living in residential aged care facilities. Aged care staff record observations and concerns about older people in electronic health records (EHR), including free-text progress notes. These insights are yet to be unleashed. OBJECTIVE: This study explored the risk factors for malnutrition in structured and unstructured electronic health data. METHODS: Data of weight loss and malnutrition were extracted from the de-identified EHR records of a large aged care organization in Australia. A literature review was conducted to identify causative factors for malnutrition. Natural language processing (NLP) techniques were applied to progress notes to extract these causative factors. The NLP performance was evaluated by the parameters of sensitivity, specificity and F1-Score. RESULTS: The NLP methods were highly accurate in extracting the key data, values for 46 causative variables, from the free-text client progress notes. Thirty three percent (1,469 out of 4,405) of the clients were malnourished. The structured, tabulated data only recorded 48% of these malnourished clients, far less than that (82%) identified from the progress notes, suggesting the importance of using NLP technology to uncover the information from nursing notes to fully understand the health status of the vulnerable older people in residential aged care. CONCLUSION: This study identified 33% of older people suffered from malnutrition, lower than those reported in the similar setting in previous studies. Our study demonstrates that NLP technology is important for uncovering the key information about health risks for older people in residential aged care. Future research can apply NLP to predict other health risks for older people in this setting. [ABSTRACT FROM AUTHOR]

• High sensitivity interferometric microdisplacement optical fiber sensor. • High extinction ratio microfiber Sagnac loop. • Online Vernier with tunable sensitivity. • Virtual reference arms are used to reduce costs and volume. • The virtual reference arm has high stability and is not subject to external interference. A virtual reference arm-based microfiber Sagnac ring (MFSL) microdisplacement sensor with tunable sensitivity is proposed and demonstrated in this study. We abandon the traditional physical reference arm and build a virtual reference arm to overcome the disadvantage of the physical reference arm being susceptible to environmental influences and to achieve tunable sensitivity. A MFSL with a diameter of 4 μm was fabricated via the winding and tapering process, and a microdisplacement sensitivity of −0.34 nm/μm was obtained in the range of 0–5 μm. Experimental results show that the microdisplacement sensitivity can reach −1.40 nm/μm after cascading the simulated reference arm, which is 4.12 times that of single MFSL. In addition, by changing the parameters of the virtual reference arm without remaking the reference arm, we obtained sensors with sensitivities of −1.74 and −2.30 nm/μm. The sensor has advantages of tunable sensitivity and low cost and has potential applications in microdisplacement measurement. [ABSTRACT FROM AUTHOR]

A novel temperature (T) and relative humidity (RH) sensor based on a graphene oxide (GO)-coated Loop-Fiber that incorporates a fiber Bragg grating (FBG) is proposed in this work. The uniform waist area of the tapered area was coated with GO for RH measurement. At the same time, FBG was used to overcome the T cross-sensitivity, thereby making a T and RH sensor. Experimental results show that the structure is sensitive to T and RH, and the sensitivity in the T range of 35 ° C to 60 ° C and humidity range of 37.7–65.5 %RH is − 201.1 pm/°C and − 141.7 pm/%RH. Corresponding resolutions are 0.14 %RH and 0.1 ° C. FBG is insensitive to RH but sensitive to T and its T sensitivity is 5.2 pm/°C. Many experiments showed the excellent reversibility and repeatability of the sensor. This sensor has the advantages of high sensitivity, simple structure, easy production and low cost. It can also be used for measuring T and RH simultaneously. • A high sensitivity temperature and relative humidity sensor based on graphene oxide and FBG is demonstrated. • The measured relative humidity and temperature sensitivity are − 141.7 pm/%RH and − 201.1 pm/°C, respectively. • The sensor has high resolution. • The sensor is simple to manufacture, high sensitivity and low cost. [ABSTRACT FROM AUTHOR]