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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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Insights on the Side Effects of Female Contraceptive Products From Online Drug Reviews: Natural Language Processing–Based Content Analysis

Insights on the Side Effects of Female Contraceptive Products From Online Drug Reviews: Natural Language Processing–Based Content Analysis

To analyze information shared and consumed on social media, natural language processing (NLP) is used due to its capacity to analyze nonstructured, textual data. For example, Pleasants et al [28] used NLP to study posts related to birth control on the US platform Reddit and found that “Side Effects!?” is the most common flair, a tag that users can attach to their post to categorize the content. Furthermore, “Experience” and “Side Effects?!”

Nicole Groene, Audrey Nickel, Amanda E Rohn

JMIR AI 2025;4:e68809

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