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Developing a Scalable Annotation Method for Large Datasets That Enhances Alarms With Actionability Data to Increase Informativeness: Mixed Methods Approach

Developing a Scalable Annotation Method for Large Datasets That Enhances Alarms With Actionability Data to Increase Informativeness: Mixed Methods Approach

Alarm logs are present in the Philips Intelli Vue patient monitoring system (MX800 software version M.00.03; MMS X2 software version H.15.41-M.00.04). It includes bedside monitors, client monitors integrating data from 2 to 3 patient rooms, and a central station. By default, it measures oxygen saturation (Sp O2), BP (invasive or noninvasive), heart rate (HR), and temperature. In our study, we focused on a subset of patient alarms triggered by the electrocardiogram, invasive BP device, or pulse oximeter.

Sophie Anne Inès Klopfenstein, Anne Rike Flint, Patrick Heeren, Mona Prendke, Amin Chaoui, Thomas Ocker, Jonas Chromik, Bert Arnrich, Felix Balzer, Akira-Sebastian Poncette

J Med Internet Res 2025;27:e65961

Uncovering Social States in Healthy and Clinical Populations Using Digital Phenotyping and Hidden Markov Models: Observational Study

Uncovering Social States in Healthy and Clinical Populations Using Digital Phenotyping and Hidden Markov Models: Observational Study

The probability of transitioning into each hidden state at time t is dependent on the previous hidden state, and is given by: Where the elements of A are each of the transition probabilities such that Amn ≡ p(ztn = 1|zt-1,m = 1, ct) denotes the probability of transitioning from state m to state n at time t and we make it explicit that this can depend on a vector of time-varying covariates ct. In addition, other measures can be calculated from the hidden state sequence itself.

Imogen E Leaning, Andrea Costanzo, Raj Jagesar, Lianne M Reus, Pieter Jelle Visser, Martien J H Kas, Christian F Beckmann, Henricus G Ruhé, Andre F Marquand

J Med Internet Res 2025;27:e64007