BMI Journal Club

Overview

Welcome to the journal club for the Department of Biomedical Informatics at UCSD.

We present and critique literature on machine learning, signal processing, and computation in biomedicine.

Sessions are moderated by Professor Shamim Nemati.

Where

Altman Clinical and Translational Research Institute (ACTRI)
9452 Medical Center Drive
La Jolla, CA - 92093
(For room number, see below)
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Schedule & Papers

Date Time Room Presenter PDFs of Papers & Slides ( )
08/26 01:00pm - Irvin The silent trial - the bridge between bench-to-bedside clinical AI application
07/27 01:00pm - Nicole Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging
07/11 01:00pm - Nathan Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients
07/08 01:00pm - Gabe Sepsis subphenotyping based on organ dysfunction trajectory
07/01 11:00am - Aaron Machine Learning Prediction of Clinical Trial Operational Efciency
05/20 11:00am - Thesath Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment
05/27 11:00am - Jonathan Concept-based model explanations for electronic health records
05/20 11:00am - Archil Literature-Augmented Clinical Outcome Prediction
05/06 11:00am - Gabe Implementation approaches and barriers for rule-based and machine learning-based sepsis risk prediction tools: a qualitative study
03/24 10:30am - Supreeth A Pragmatic Stepped-wedge, Cluster-controlled Trial of Real-time Pneumonia Clinical Decision Support
03/10 10:30am - Gabe Development and Verification of a Digital Twin Patient Model to Predict Specific Treatment Response During the First 24 Hours of Sepsis
02/23 10:30am - Jonathan Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations
01/20 10:30am - Dr. Benjamin Glicksberg Contrastive learning improves critical event prediction in COVID-19 patients
01/13 10:30am - Rahul Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records
12/15 10:30am - Jonathan Identification of driver genes for critical forms of COVID-19 in a deeply phenotyped young patient cohort
11/18 10:30am - Archil Training deep neural-networks using a noise adaptation layer
10/08 10:30am - Aaron Personalized Antibiograms: Machine Learning for Precision Selection of Empiric Antibiotics
09/23 10:30am - Supreeth Improving Timeliness of Antibiotic Administration Using a Provider and Pharmacist Facing Sepsis Early Warning System in the Emergency Department Setting: A Randomized Controlled Quality Improvement Initiative
08/12 10:30am - Nancy Clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health
07/29 10:30am - Jonathan Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning
07/15 10:30am - Dr. Gabe Wardi Design and Implementation of a Real-time Monitoring Platform for Optimal Sepsis Care in an Emergency Department: Observational Cohort Study
07/01 10:30am - Dr. Kit Curtius Multicentre derivation and validation of a colitis-associated colorectal cancer risk prediction web tool
06/17 10:30am - Jonathan Swarm Learning for decentralized and confidential clinical machine learning
06/03 10:30am - Severine The Value of Artificial Intelligence in Laboratory Medicine: Current Opinions and Barriers to Implementation
05/20 10:30am - Aaron Towards understanding the effective use of antibiotics for sepsis
05/06 10:30am - Jonathan Early detection of sepsis utilizing deep learning on electronic health record event sequences
04/22 10:30am - Nancy Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case‑study in pulmonary embolism detection
04/08 10:30am - Severine Directions for Explainable Knowledge-Enabled Systems
03/24 10:30am - Supreeth Assessment of Machine Learning to Estimate the Individual Treatment Effect of Corticosteroids in Septic Shock
03/04 10:30am - Aaron Improving the delivery of palliative care through predictive modeling and healthcare informatics
02/19 10:30am - - From Data to Optimal Decision Making: A Data-Driven, Probabilistic Machine Learning Approach to Decision Support for Patients With Sepsis
02/11 10:30am - Dr. Sally Baxter Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19: a prospective cohort study
01/28 10:30am - Supreeth Learning Representations of Missing Data for Predicting Patient Outcomes
01/14 10:30am - Supreeth Second opinion needed: communicating uncertainty in medical machine learning
01/07 10:30am - Fatemeh Deep Learning for Improved Risk Prediction in Surgical Outcomes
10/29 10:30am - - Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance
10/15 10:30am - Supreeth Controlling Level of Unconsciousness by Titrating Propofol with Deep Reinforcement Learning
10/15 10:30am - - Reinforcement Learning for Closed-Loop Propofol Anesthesia: A Study in Human Volunteers
2020 - - - Contrastive Learning of Medical Visual Representations from Paired Images and Text
2020 - - - A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections
2020 - - Supreeth Predictive modeling of bacterial infections and antibiotic therapy needs in critically ill adults
2020 - - Nancy Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study
2020 - - Nancy A multidimensional precision medicine approach identifies an autism subtype characterized by dyslipidemia
2020 - - Nancy Predicting Severe Chronic Obstructive Pulmonary Disease Exacerbations. Developing a Population Surveillance Approach with Administrative Data
2020 - - Nancy Machine Learning and Prediction of All-Cause Mortality in COPD
2020 - - Heqi Explainable artificial intelligence model to predict acute critical illness from electronic health records

SEPSIS Grand Rounds

Date Time Presenter PDFs of Papers & Slides ( )
09/23 11am Chris Control of Confounding and Reporting of Results in Causal Inference Studies Examples of DAG Strobe Statement Strobe Paper

Join our JC

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ML method papers

Similarity of Neural Network Representations Revisited

Overcoming catastrophic forgetting in neural networks Blog post

Exponentially Weighted Imitation Learning for Batched Historical Data

Previous presentations

Date Presenter PDFs of Papers & Slides ( )
04/13 - 1) PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration 2) A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data
09/12 Supreeth On the interpretability of machine learningbased model for predicting hypertension Extra: Blog post on LIME, Blog post on Shapley Values
08/15 Jejo Koola A clinically applicable approach to continuous prediction of future acute kidney injury
07/12 Gabe Wardi Personal clinical history predicts antibiotic resistance of urinary tract infections
07/02 Supreeth Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis - Continued
06/20 Supreeth Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis and Supplementary Material
05/17 Supreeth Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation and Objecting to experiments that compare two unobjectionable policies or treatments
02/25 Matt Temporal Convolutional Networks and Dynamic Time Warping can Drastically Improve the Early Prediction of Sepsis
02/11 Russell Sepsis as a model for improving diagnosis and Evaluating the impact of a computerized surveillance algorithm and decision support system on sepsis mortality
01/09 Russell Evaluating a New Marker for Risk Prediction Using the Test Tradeoff: An Update Decision Analysis for the Evaluation of Diagnostic Tests, Prediction Models, and Molecular Markers Decision Analysis
12/05/18 Russell The challenge of implementing AI models in the ICU To Trust Or Not To Trust A Classifier Supplement
11/16/18 Russell/Supreeth Relational inductive biases, deep learning, and graph networks
10/24/18 Russell The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care Supplementary Materials
10/03/18 Supreeth Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation
09/28/18 Supreeth Estimating attributable fraction of mortality from sepsis to inform clinical trials Supplementary Materials
09/12/18 Russell The reusable holdout: Preserving validity in adaptive data analysis Supplementary Materials
03/07/18 Supreeth Scalable and accurate deep learning for Electronic Health Records
01/31/19 Supreeth Learning to cluster in order to transfer across domains and tasks
01/24/18 Azade Maximum Likelihood Estimation, Weibull cox proportional hazards model
12/14/17 Supreeth Neural Network based clustering using pair-wise constraints
07/13/17 Erik Reinertsen Transfer Entropy Estimation and Directional Coupling Change Detection in Biomedical Time Series
07/06/17 Supreeth Doubly Robust Policy Evaluation and Learning
06/29/17 Supreeth Doubly Robust Estimation of Causal Effects
06/22/17 Supreeth Nonlinear Inverse Reinforcement Learning with Gaussian Processes
06/07/17 Supreeth Doctor AI: Predicting Clinical Events via Recurrent Neural Networks
06/22/16 Mark Connolly The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI
05/18/16 Sahar Harati Speech Emotion Recognition Using Deep Neural Network and Extreme Learning Machine
04/27/16 Jitesh Punjabi Using Anchors to Estimate Clinical State without Labelee Data
04/13/16 Supreeth Prajwal OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records
03/30/16 Ziyi Li Using generalized estimating equations for longitudinal data analysis
03/02/16 Erik Reinertsen Major depressive disorder subtypes to predict long-term course
02/17/16 Myles McCrary Real-time prediction of mortality, readmission, and length of stay