Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200–300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.

Side effects of COVID-19 or other vaccinations may affect an individual’s safety, ability to work or care for self or others, and/or willingness to be vaccinated. Identifying modifiable factors that influence these side effects may increase the number of people vaccinated. In this observational study, data were from individuals who received an mRNA COVID-19 vaccine between December 2020 and April 2021 and responded to at least one post-vaccination symptoms survey that was sent daily for three days after each vaccination. We excluded those with a COVID-19 diagnosis or positive SARS-CoV2 test within one week after their vaccination because of the overlap of symptoms. We used machine learning techniques to analyze the data after the first vaccination. Data from 50,484 individuals (73% female, 18 to 95 years old) were included in the primary analysis. Demographics, history of an epinephrine autoinjector prescription, allergy history category (e.g., food, vaccine, medication, insect sting, seasonal), prior COVID-19 diagnosis or positive test, and vaccine manufacturer were identified as factors associated with allergic and non-allergic side effects; vaccination time 6:00–10:59 was associated with more non-allergic side effects. Randomized controlled trials should be conducted to quantify the relative effect of modifiable factors, such as time of vaccination.

“What time should I take my medicine?” is an increasingly important question. Current knowledge of time-of-day effects for specific medications in hospitalized patients with cardiovascular disease is very limited. In such patients, increased medication efficiency could potentially reduce dose use and/or the length of time in the Intensive Care Unit (ICU) and/or hospital – potentially improving patient outcomes and patient and family quality of life, and reducing financial costs. We studied whether the time of day or night a patient is given a diuretic affects urine volume response. In this observational study, data were from 7,704 patients (63% male, 18 to 98 years old) admitted to one hospital’s acute care cardiac units, cardiac ICUs, cardiac surgery ICUs, and/or non-cardiac ICUs, who received intravenous furosemide (a diuretic), had measurements of urine volume, were hospitalized for ≥ 3 days between January 2016 to July 2021 and were older than 18 years. We used machine learning (ML) techniques to analyze the data. The ML technique identified factors that were expected to predict urine volume response to the diuretic: sex, age, medication dose and time, creatinine concentration, diagnosis, and hospital unit. The ML technique also identified medication administration time 00:00–06:00 as a predictor of higher urine volume response. Randomized controlled trials should be conducted to quantify the relative effect of modifiable factors, such as time of medication administration.