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A Machine Learning Approach for Identifying People With Neuroinfectious Diseases in Electronic Health Records: Algorithm Development and Validation

A Machine Learning Approach for Identifying People With Neuroinfectious Diseases in Electronic Health Records: Algorithm Development and Validation

Many neuroinvasive pathogens, including viruses (eg, enterovirus, herpes simplex virus, West Nile virus, and HIV), bacteria (eg, S. pneumoniae, Mycobacterium tuberculosis), and fungi (eg, Cryptococcus), are linked to long-term cognitive sequelae [2,8]. Recent population-based studies suggest associations between pathogen exposures, especially neuroinvasive viruses causing encephalitis or meningitis, and the subsequent risk of developing dementia such as Alzheimer disease (AD) [9-12].

Arjun Singh, Shadi Sartipi, Haoqi Sun, Rebecca Milde, Niels Turley, Carson Quinn, G Kyle Harrold, Rebecca L Gillani, Sarah E Turbett, Sudeshna Das, Sahar Zafar, Marta Fernandes, M Brandon Westover, Shibani S Mukerji

JMIR Med Inform 2025;13:e63157

Exploring the Impact of Home-Based Serious Smartphone Resuscitation Gaming on Stress Among Nursing Students Practicing Simulated Adult Basic Life Support: Randomized Waitlist Controlled Trial

Exploring the Impact of Home-Based Serious Smartphone Resuscitation Gaming on Stress Among Nursing Students Practicing Simulated Adult Basic Life Support: Randomized Waitlist Controlled Trial

This classification was based on thresholds from the Affective Road dataset, where S≤0.65 indicated no stress, 0.65≤S≤1.3 indicated moderate stress, and S≥1.3 indicated high stress. The classifier extracted 48 signal features from a 10-second sliding window of EDA, BT, and HR data, along with contextual features from the preceding 10 windows.

Nino Fijačko, Benjamin S Abella, Špela Metličar, Leon Kopitar, Robert Greif, Gregor Štiglic, Pavel Skok, Matej Strnad

JMIR Serious Games 2025;13:e67623

A Conversational Agent (PracticePal) to Support the Delivery of a Brief Behavioral Activation Treatment for Depression in Rural India: Development and Pilot-Testing Study

A Conversational Agent (PracticePal) to Support the Delivery of a Brief Behavioral Activation Treatment for Depression in Rural India: Development and Pilot-Testing Study

S: counseling session. To evaluate the usability, feasibility, and acceptability of Practice Pal, we collected both quantitative and qualitative data. Quantitative data on engagement were extracted from the backend of the chatbot platform, which tracked the frequency of access, total time spent engaging with the chatbot (including watching the multimedia content), and the depth of engagement (measured in terms of completion of homework within the workflow).

Ravindra Agrawal, Kimberley Monteiro, Nityasri Sankha Narasimhamurti, Shreya Sharma, Amruta Suryawanshi, Aman Bariya, Shravani Narvekar, Lilianna Bagnoli, Mohit Saxena, Lauren Magoun, Shradha S Parsekar, Julia R Pozuelo, Neal Lesh, Mohit Sood, Tanushri Sharma, Harshita Yadav, Anant Bhan, Abhijit Nadkarni, Vikram Patel

JMIR Form Res 2025;9:e73563