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Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year (1). In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities (2). Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza (3,4). One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users., Traditional surveillance systems, including those used by the US Centers for Disease Control and Prevention (CDC) and the European Influenza Surveillance Scheme (EISS), rely on both virological and clinical data, [...]

Objective: The objective of openFDA is to facilitate access and use of big important Food and Drug Administration public datasets by developers, researchers, and the public through harmonization of data across disparate FDA datasets provided via application programming interfaces (APIs).Materials and Methods: Using cutting-edge technologies deployed on FDA's new public cloud computing infrastructure, openFDA provides open data for easier, faster (over 300 requests per second per process), and better access to FDA datasets; open source code and documentation shared on GitHub for open community contributions of examples, apps and ideas; and infrastructure that can be adopted for other public health big data challenges.Results: Since its launch on June 2, 2014, openFDA has developed four APIs for drug and device adverse events, recall information for all FDA-regulated products, and drug labeling. There have been more than 20 million API calls (more than half from outside the United States), 6000 registered users, 20,000 connected Internet Protocol addresses, and dozens of new software (mobile or web) apps developed. A case study demonstrates a use of openFDA data to understand an apparent association of a drug with an adverse event.Conclusion: With easier and faster access to these datasets, consumers worldwide can learn more about FDA-regulated products. [ABSTRACT FROM AUTHOR]

We describe a walking microrobot that is propelled by cilialike thermal bimorph actuator arrays. The robot consists of two actuator array chips, each having an 8 x 8 array of "motion pixels," which are composed of four orthogonally oriented cilia. Each group of unidirectional cilia is controlled independently for each chip, which provides planar motion with three degrees of freedom (x, y, θ). The robot is approximately 3 cm in length, 1 cm in width, and 0.9 mm in height and has a mass of 0.5 g. By varying the actuation frequency and motion gait strategy, the direction and velocity of the motion can be controlled. In this paper, we present the system architecture, control mechanism, and modeling of the robot, as well as experimental results, during linear and rotary motion. The robot can carry loads up to seven times its own mass, and it can operate at speeds up to 250 µm/s with step sizes from 1 to 4 µm. [ABSTRACT FROM AUTHOR]

Background: Due to recent advances in artificial intelligence, large language models (LLMs) have emerged as a powerful tool for a variety of language-related tasks, including sentiment analysis, and summarization of provider-patient interactions. However, there is limited research on these models in the area of crisis prediction., Objective: This study aimed to evaluate the performance of LLMs, specifically OpenAI's generative pretrained transformer 4 (GPT-4), in predicting current and future mental health crisis episodes using patient-provided information at intake among users of a national telemental health platform., Methods: Deidentified patient-provided data were pulled from specific intake questions of the Brightside telehealth platform, including the chief complaint, for 140 patients who indicated suicidal ideation (SI), and another 120 patients who later indicated SI with a plan during the course of treatment. Similar data were pulled for 200 randomly selected patients, treated during the same time period, who never endorsed SI. In total, 6 senior Brightside clinicians (3 psychologists and 3 psychiatrists) were shown patients' self-reported chief complaint and self-reported suicide attempt history but were blinded to the future course of treatment and other reported symptoms, including SI. They were asked a simple yes or no question regarding their prediction of endorsement of SI with plan, along with their confidence level about the prediction. GPT-4 was provided with similar information and asked to answer the same questions, enabling us to directly compare the performance of artificial intelligence and clinicians., Results: Overall, the clinicians' average precision (0.7) was higher than that of GPT-4 (0.6) in identifying the SI with plan at intake (n=140) versus no SI (n=200) when using the chief complaint alone, while sensitivity was higher for the GPT-4 (0.62) than the clinicians' average (0.53). The addition of suicide attempt history increased the clinicians' average sensitivity (0.59) and precision (0.77) while increasing the GPT-4 sensitivity (0.59) but decreasing the GPT-4 precision (0.54). Performance decreased comparatively when predicting future SI with plan (n=120) versus no SI (n=200) with a chief complaint only for the clinicians (average sensitivity=0.4; average precision=0.59) and the GPT-4 (sensitivity=0.46; precision=0.48). The addition of suicide attempt history increased performance comparatively for the clinicians (average sensitivity=0.46; average precision=0.69) and the GPT-4 (sensitivity=0.74; precision=0.48)., Conclusions: GPT-4, with a simple prompt design, produced results on some metrics that approached those of a trained clinician. Additional work must be done before such a model can be piloted in a clinical setting. The model should undergo safety checks for bias, given evidence that LLMs can perpetuate the biases of the underlying data on which they are trained. We believe that LLMs hold promise for augmenting the identification of higher-risk patients at intake and potentially delivering more timely care to patients., (©Christine Lee, Matthew Mohebbi, Erin O'Callaghan, Mirène Winsberg. Originally published in JMIR Mental Health (https://mental.jmir.org), 02.08.2024.)