Correction to: From illness management to quality of life: rethinking consumer health informatics opportunities for progressive, potentially fatal illnesses.
Author(s):
DOI: 10.1093/jamia/ocae048
Author(s):
DOI: 10.1093/jamia/ocae048
This study focuses on refining temporal relation extraction within medical documents by introducing an innovative bimodal architecture. The overarching goal is to enhance our understanding of narrative processes in the medical domain, particularly through the analysis of extensive reports and notes concerning patient experiences.
Author(s): Knez, Timotej, Žitnik, Slavko
DOI: 10.1093/jamia/ocae059
We developed and externally validated a machine-learning model to predict postpartum depression (PPD) using data from electronic health records (EHRs). Effort is under way to implement the PPD prediction model within the EHR system for clinical decision support. We describe the pre-implementation evaluation process that considered model performance, fairness, and clinical appropriateness.
Author(s): Liu, Yifan, Joly, Rochelle, Reading Turchioe, Meghan, Benda, Natalie, Hermann, Alison, Beecy, Ashley, Pathak, Jyotishman, Zhang, Yiye
DOI: 10.1093/jamia/ocae056
To develop and validate a natural language processing (NLP) pipeline that detects 18 conditions in French clinical notes, including 16 comorbidities of the Charlson index, while exploring a collaborative and privacy-enhancing workflow.
Author(s): Petit-Jean, Thomas, Gérardin, Christel, Berthelot, Emmanuelle, Chatellier, Gilles, Frank, Marie, Tannier, Xavier, Kempf, Emmanuelle, Bey, Romain
DOI: 10.1093/jamia/ocae069
To investigate the consistency and reliability of medication recommendations provided by ChatGPT for common dermatological conditions, highlighting the potential for ChatGPT to offer second opinions in patient treatment while also delineating possible limitations.
Author(s): Iqbal, Usman, Lee, Leon Tsung-Ju, Rahmanti, Annisa Ristya, Celi, Leo Anthony, Li, Yu-Chuan Jack
DOI: 10.1093/jamia/ocae067
The timely stratification of trauma injury severity can enhance the quality of trauma care but it requires intense manual annotation from certified trauma coders. The objective of this study is to develop machine learning models for the stratification of trauma injury severity across various body regions using clinical text and structured electronic health records (EHRs) data.
Author(s): Gao, Jifan, Chen, Guanhua, O'Rourke, Ann P, Caskey, John, Carey, Kyle A, Oguss, Madeline, Stey, Anne, Dligach, Dmitriy, Miller, Timothy, Mayampurath, Anoop, Churpek, Matthew M, Afshar, Majid
DOI: 10.1093/jamia/ocae071
Author(s):
DOI: 10.1093/jamia/ocae083
Herbal prescription recommendation (HPR) is a hot topic and challenging issue in field of clinical decision support of traditional Chinese medicine (TCM). However, almost all previous HPR methods have not adhered to the clinical principles of syndrome differentiation and treatment planning of TCM, which has resulted in suboptimal performance and difficulties in application to real-world clinical scenarios.
Author(s): Dong, Xin, Zhao, Chenxi, Song, Xinpeng, Zhang, Lei, Liu, Yu, Wu, Jun, Xu, Yiran, Xu, Ning, Liu, Jialing, Yu, Haibin, Yang, Kuo, Zhou, Xuezhong
DOI: 10.1093/jamia/ocae066
Machine learning (ML) is increasingly employed to diagnose medical conditions, with algorithms trained to assign a single label using a black-box approach. We created an ML approach using deep learning that generates outcomes that are transparent and in line with clinical, diagnostic rules. We demonstrate our approach for autism spectrum disorders (ASD), a neurodevelopmental condition with increasing prevalence.
Author(s): Leroy, Gondy, Andrews, Jennifer G, KeAlohi-Preece, Madison, Jaswani, Ajay, Song, Hyunju, Galindo, Maureen Kelly, Rice, Sydney A
DOI: 10.1093/jamia/ocae080
To compare performances of a classifier that leverages language models when trained on synthetic versus authentic clinical notes.
Author(s): Litake, Onkar, Park, Brian H, Tully, Jeffrey L, Gabriel, Rodney A
DOI: 10.1093/jamia/ocae081