no code implementations • 23 Dec 2022 • Yuting Guo, Swati Rajwal, Sahithi Lakamana, Chia-Chun Chiang, Paul C. Menell, Adnan H. Shahid, Yi-Chieh Chen, Nikita Chhabra, Wan-Ju Chao, Chieh-Ju Chao, Todd J. Schwedt, Imon Banerjee, Abeed Sarker
In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by migraine sufferers; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem.
Controlled Markov chains (CMCs) form the bedrock for model-based reinforcement learning.
We assume each task to be a different random Principal Component (PC) matrix with a possibly different support and that the support union of the PC matrices is small.
Language modality within the vision language pretraining framework is innately discretized, endowing each word in the language vocabulary a semantic meaning.
We developed a bi-directional Long Short Term Memory (LSTM) Network that is able to use readily available insurance data (inpatient visits, outpatient visits, and drug prescriptions) to predict 30 day re-admission for any admitted patient, regardless of reason.
Pathology text mining is a challenging task given the reporting variability and constant new findings in cancer sub-type definitions.
Measures to predict 30-day readmission are considered an important quality factor for hospitals as accurate predictions can reduce the overall cost of care by identifying high risk patients before they are discharged.
While existing methods can be applied for class-wise retrieval (aka.
no code implementations • 8 Feb 2022 • Jiwoong J. Jeong, Brianna L. Vey, Ananth Reddy, Thomas Kim, Thiago Santos, Ramon Correa, Raman Dutt, Marina Mosunjac, Gabriela Oprea-Ilies, Geoffrey Smith, Minjae Woo, Christopher R. McAdams, Mary S. Newell, Imon Banerjee, Judy Gichoya, Hari Trivedi
Developing and validating artificial intelligence models in medical imaging requires datasets that are large, granular, and diverse.
Given an internal dataset A as the base source, we first train anomaly detectors for each class of dataset A to learn internal distributions in an unsupervised way.
no code implementations • 23 Nov 2021 • Yuyin Zhou, Shih-Cheng Huang, Jason Alan Fries, Alaa Youssef, Timothy J. Amrhein, Marcello Chang, Imon Banerjee, Daniel Rubin, Lei Xing, Nigam Shah, Matthew P. Lungren
Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimodal, i. e., they only learn features from pixel-level information.
The use of artificial intelligence (AI) in healthcare has become a very active research area in the last few years.
The key issue is the granularity of OOD data in the medical domain, where intra-class OOD samples are predominant.
Traditional anomaly detection methods focus on detecting inter-class variations while medical image novelty identification is inherently an intra-class detection problem.
no code implementations • 21 Jul 2021 • Imon Banerjee, Ananth Reddy Bhimireddy, John L. Burns, Leo Anthony Celi, Li-Ching Chen, Ramon Correa, Natalie Dullerud, Marzyeh Ghassemi, Shih-Cheng Huang, Po-Chih Kuo, Matthew P Lungren, Lyle Palmer, Brandon J Price, Saptarshi Purkayastha, Ayis Pyrros, Luke Oakden-Rayner, Chima Okechukwu, Laleh Seyyed-Kalantari, Hari Trivedi, Ryan Wang, Zachary Zaiman, Haoran Zhang, Judy W Gichoya
Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race.
We present a PAC-Bayesian analysis of variational Bayes (VB) approximations to tempered Bayesian posterior distributions, bounding the model risk of the VB approximations.
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The results show the internal performance of each of the 5 pathologies outperformed external performance on both of the models.
To test the performance of the two top models for CXR COVID-19 diagnosis on external datasets to assess model generalizability.
1 code implementation • 16 Apr 2020 • Pradeeban Kathiravelu, Puneet Sharma, ASHISH SHARMA, Imon Banerjee, Hari Trivedi, Saptarshi Purkayastha, Priyanshu Sinha, Alexandre Cadrin-Chenevert, Nabile Safdar, Judy Wawira Gichoya
Executing machine learning (ML) pipelines in real-time on radiology images is hard due to the limited computing resources in clinical environments and the lack of efficient data transfer capabilities to run them on research clusters.
We propose a hybrid sequential deep learning model to predict the risk of AMD progression in non-exudative AMD eyes at multiple timepoints, starting from short-term progression (3-months) up to long-term progression (21-months).
We propose a scalable computerized approach for large-scale inference of Liver Imaging Reporting and Data System (LI-RADS) final assessment categories in narrative ultrasound (US) reports.
We propose a deep learning model - Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term life expectancy (3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence.
Radiology reports are a rich resource for advancing deep learning applications in medicine by leveraging the large volume of data continuously being updated, integrated, and shared.
Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline.
We propose an automated method for detecting aggressive prostate cancer(CaP) (Gleason score >=7) based on a comprehensive analysis of the lesion and the surrounding normal prostate tissue which has been simultaneously captured in T2-weighted MR images, diffusion-weighted images (DWI) and apparent diffusion coefficient maps (ADC).