Search Articles

View query in Help articles search

Search Results (1 to 10 of 10 Results)

Download search results: CSV END BibTex RIS


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

We reviewed 102 surgical pathology reports from 102 patients and excluded reports if they were from other organ sites (n=10), benign (n=2), cytopathology (n=5), or outside review (n=1). We included 84 reports for analysis. The study flowchart is shown in Figure 2. Flowchart of the study design and analysis. *The concordance rate was calculated as the total number of concordant answers/total number of answers for each of the 12 medical question answering (MQA).

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

JMIR Form Res 2025;9:e64544

Leveraging Artificial Intelligence and Data Science for Integration of Social Determinants of Health in Emergency Medicine: Scoping Review

Leveraging Artificial Intelligence and Data Science for Integration of Social Determinants of Health in Emergency Medicine: Scoping Review

Most studies were published after 2020 (Figure 2) and included patient populations focusing exclusively on the ED (n=9), pediatric patients (n=2), patients treated by emergency medical services (n=2), and US veterans (n=2), among other examples.

Ethan E Abbott, Donald Apakama, Lynne D Richardson, Lili Chan, Girish N Nadkarni

JMIR Med Inform 2024;12:e57124

Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis

Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis

Overall, the cohort was 40.1% (n=10,831) female; 15% (n=4055) identified as Black, 79.2% (n=21,407) as White, and 5.8% (n=1566) as “other” beneficiaries (Table 1). For age, 15.4% (n=4156) of the cohort included beneficiaries older than 85 years. Among area-level SDOH, the median HHI by zip code was US $49,720.50 (IQR US $39,893.25-$64,233.25), and the median percentage living below the poverty level at the zip code level was 10.4% (IQR 6.0%-16.4%). Overall mortality at 1 year was 45.1% (n=12,191).

Ethan E Abbott, Wonsuk Oh, Yang Dai, Cole Feuer, Lili Chan, Brendan G Carr, Girish N Nadkarni

JMIR Aging 2023;6:e51844

Development of the ehive Digital Health App: Protocol for a Centralized Research Platform

Development of the ehive Digital Health App: Protocol for a Centralized Research Platform

The demographic profile of the participants enrolled in one ehive study, the Warrior Watch Study, is described to highlight participant diversity (n=297). The number of individuals participating in ehive-related studies in each state in the United States. A trend line of the total weekly number of ehive participants from April 2021 through January 2023, demonstrating high rates of use and engagement. An example of engagement in the ehive app on 3 sequential days.

Robert P Hirten, Matteo Danieletto, Kyle Landell, Micol Zweig, Eddye Golden, Georgy Orlov, Jovita Rodrigues, Eugenia Alleva, Ipek Ensari, Erwin Bottinger, Girish N Nadkarni, Thomas J Fuchs, Zahi A Fayad

JMIR Res Protoc 2023;12:e49204

StudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials

StudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials

Historically, there have been local implementations of N-of-1 trials in hospitals in the United States, Canada, and Australia [4,10]; series of articles on N-of-1 trials have been published in medical and epidemiological journals [11,12]; and networks on N-of-1 studies have been formed [13]. The advancements in digital technologies provide the potential to initiate a new era of N-of-1 trials in terms of scale and scope and have opened up new avenues to offer remote health care.

Stefan Konigorski, Sarah Wernicke, Tamara Slosarek, Alexander M Zenner, Nils Strelow, Darius F Ruether, Florian Henschel, Manisha Manaswini, Fabian Pottbäcker, Jonathan A Edelman, Babajide Owoyele, Matteo Danieletto, Eddye Golden, Micol Zweig, Girish N Nadkarni, Erwin Böttinger

J Med Internet Res 2022;24(7):e35884

Factors Associated With Longitudinal Psychological and Physiological Stress in Health Care Workers During the COVID-19 Pandemic: Observational Study Using Apple Watch Data

Factors Associated With Longitudinal Psychological and Physiological Stress in Health Care Workers During the COVID-19 Pandemic: Observational Study Using Apple Watch Data

Participants had a mean age of 37 years, were 69.3% (n=246) female, and were followed for a mean of 60 days (IQR 21-98 days). Clinical trainees had higher baseline resilience compared to clinical nontrainees (P=.03) and staff (P=.01), higher optimism (P=.04) and emotional support (P=.01) compared to staff, and higher emotional support compared to clinical nontrainees (P=.01) (Table S2, Multimedia Appendix 1).

Robert P Hirten, Matteo Danieletto, Lewis Tomalin, Katie Hyewon Choi, Micol Zweig, Eddye Golden, Sparshdeep Kaur, Drew Helmus, Anthony Biello, Renata Pyzik, Claudia Calcagno, Robert Freeman, Bruce E Sands, Dennis Charney, Erwin P Bottinger, James W Murrough, Laurie Keefer, Mayte Suarez-Farinas, Girish N Nadkarni, Zahi A Fayad

J Med Internet Res 2021;23(9):e31295

Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

To do this, we used a mixed-effect cosinor model, where the HRV measure of participant i at time t can be written as follows: where M, β, and γ are the population parameters (fixed effects) and θi is a vector of random effects that is assumed to follow a multivariate normal distribution θi~N(0,Σ). In this context, the introduction of random effects intrinsically models the correlation due to the longitudinal sampling.

Robert P Hirten, Matteo Danieletto, Lewis Tomalin, Katie Hyewon Choi, Micol Zweig, Eddye Golden, Sparshdeep Kaur, Drew Helmus, Anthony Biello, Renata Pyzik, Alexander Charney, Riccardo Miotto, Benjamin S Glicksberg, Matthew Levin, Ismail Nabeel, Judith Aberg, David Reich, Dennis Charney, Erwin P Bottinger, Laurie Keefer, Mayte Suarez-Farinas, Girish N Nadkarni, Zahi A Fayad

J Med Internet Res 2021;23(2):e26107

Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

Data from patients who tested positive for COVID-19 (N=4029) were derived from the EHRs of 5 Mount Sinai Health System (MSHS) hospitals in New York City. Study inclusion criteria are shown in Figure 1. Further details, as well as cross-hospital demographic and clinical comparisons, are in Multimedia Appendices 1-8. Study design and model workflow. (A) Criteria for patient inclusion in this study. (B) An overview of the local and pooled models.

Akhil Vaid, Suraj K Jaladanki, Jie Xu, Shelly Teng, Arvind Kumar, Samuel Lee, Sulaiman Somani, Ishan Paranjpe, Jessica K De Freitas, Tingyi Wanyan, Kipp W Johnson, Mesude Bicak, Eyal Klang, Young Joon Kwon, Anthony Costa, Shan Zhao, Riccardo Miotto, Alexander W Charney, Erwin Böttinger, Zahi A Fayad, Girish N Nadkarni, Fei Wang, Benjamin S Glicksberg

JMIR Med Inform 2021;9(1):e24207

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation

Demographic characteristics, clinical history, and vital signs of hospitalized patients with COVID-19 at baseline (N=4098). a MSH: Mount Sinai Hospital. b OH: other hospitals. c—: Values with fewer than 10 patients per field are censored to protect patient privacy. Admission laboratory parameters of hospitalized patients with COVID-19 at baseline (N=4098), median (IQR). a MSH: Mount Sinai Hospital. b OH: other hospitals. c PCO2: partial pressure of carbon dioxide.

Akhil Vaid, Sulaiman Somani, Adam J Russak, Jessica K De Freitas, Fayzan F Chaudhry, Ishan Paranjpe, Kipp W Johnson, Samuel J Lee, Riccardo Miotto, Felix Richter, Shan Zhao, Noam D Beckmann, Nidhi Naik, Arash Kia, Prem Timsina, Anuradha Lala, Manish Paranjpe, Eddye Golden, Matteo Danieletto, Manbir Singh, Dara Meyer, Paul F O'Reilly, Laura Huckins, Patricia Kovatch, Joseph Finkelstein, Robert M. Freeman, Edgar Argulian, Andrew Kasarskis, Bethany Percha, Judith A Aberg, Emilia Bagiella, Carol R Horowitz, Barbara Murphy, Eric J Nestler, Eric E Schadt, Judy H Cho, Carlos Cordon-Cardo, Valentin Fuster, Dennis S Charney, David L Reich, Erwin P Bottinger, Matthew A Levin, Jagat Narula, Zahi A Fayad, Allan C Just, Alexander W Charney, Girish N Nadkarni, Benjamin S Glicksberg

J Med Internet Res 2020;22(11):e24018