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Integrating Nurse Preferences Into AI-Based Scheduling Systems: Qualitative Study

Integrating Nurse Preferences Into AI-Based Scheduling Systems: Qualitative Study

Participatory staff scheduling can also be significantly enhanced by AI methodologies, especially by reinforcement learning (RL) and natural language processing (NLP). RL optimizes dynamic scheduling by adapting to evolving conditions through interaction and feedback, managing complex environments effectively [26-30].

Fabienne Josefine Renggli, Maisa Gerlach, Jannic Stefan Bieri, Christoph Golz, Murat Sariyar

JMIR Form Res 2025;9:e67747

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A Deep Learning–Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records

A Deep Learning–Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records

In the past decade, health care natural language processing (NLP) frameworks like Google’s Healthcare Natural Language application programming interface (API), Amazon Comprehend Medical, IBM Watson Health, and Microsoft Text Analytics for Health have emerged and shown promise in clinical concept recognition, entity linking, and sentiment analysis. However, these general-purpose NLP frameworks have shown varying degrees of performance on different data sources [11-13].

Gowtham Varma, Rohit Kumar Yenukoti, Praveen Kumar M, Bandlamudi Sai Ashrit, K Purushotham, C Subash, Sunil Kumar Ravi, Verghese Kurien, Avinash Aman, Mithun Manoharan, Shashank Jaiswal, Akash Anand, Rakesh Barve, Viswanathan Thiagarajan, Patrick Lenehan, Scott A Soefje, Venky Soundararajan

JMIR Cancer 2025;11:e64697

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Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study

Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study

Both the raw audio data and the transcribed text content were processed to extract acoustic and NLP-based features from speech outputs. NLP and acoustic signal models were embedded in the backend part of the mobile app. Consistent with recent evidence, we assumed speech as verbal behavior, the spoken output of the mental system underlying the language [39]. Through speech recognition, acoustic and linguistic features were extracted.

Cristina Crocamo, Riccardo Matteo Cioni, Aurelia Canestro, Christian Nasti, Dario Palpella, Susanna Piacenti, Alessandra Bartoccetti, Martina Re, Valentina Simonetti, Chiara Barattieri di San Pietro, Maria Bulgheroni, Francesco Bartoli, Giuseppe Carrà

JMIR Form Res 2025;9:e65555

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Using Large Language Models to Automate Data Extraction From Surgical Pathology Reports: Retrospective Cohort Study

Using Large Language Models to Automate Data Extraction From Surgical Pathology Reports: Retrospective Cohort Study

Advances in natural language processing (NLP) techniques have sought to address challenges in the efficiency and accuracy of data abstraction. Efforts have included applying NLP methods to extract pain scores in patients with cancer undergoing radiation [5], classify metastatic phenotypes from radiology reports of patients with colorectal cancer [6], and identify recurrence status in patients with hepatocellular carcinoma [7].

Denise Lee, Akhil Vaid, Kartikeya M Menon, Robert Freeman, David S Matteson, Michael L Marin, Girish N Nadkarni

JMIR Form Res 2025;9:e64544

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