Shahriar Noroozizadeh
ML+ISM PhD @ Carnegie Mellon Unviersity
snoroozi [AT] cs.cmu.edu

Temporal-Supervised Contrastive Learning: Modeling Patient Risk Progression [Paper: ML4H] [Paper: AAAI - R2HCAI Workshop]

Abstract: We consider the problem of predicting how the likelihood of an outcome of interest for a patient changes over time as we observe more of the patient’s data. To solve this problem, we propose a supervised contrastive learning framework that learns an embedding representation for each time step of a patient time series. Our framework learns the embedding space to have the following properties: (1) nearby points in the embedding space have similar predicted class probabilities, (2) adjacent time steps of the same time series map to nearby points in the embedding space, and (3) time steps with very different raw feature vectors map to far apart regions of the embedding space. To achieve property (3), we employ a nearest neighbor pairing mechanism in the raw feature space. This mechanism also serves as an alternative to “data augmentation”, a key ingredient of contrastive learning, which lacks a standard procedure that is adequately realistic for clinical tabular data, to our knowledge. We demonstrate that our approach outperforms state-of-the-art baselines in predicting mortality of septic patients (MIMIC-III dataset) and tracking progression of cognitive impairment (ADNI dataset). Our method also consistently recovers the correct synthetic dataset embedding structure across experiments, a feat not achieved by baselines. Our ablation experiments show the pivotal role of our nearest neighbor pairing.

Contrastive Learning Based Interpretable Hospital Discharge Delay Prediction

Abstract: We addressed the significant challenge of delays in patient discharge across hospitals. Over an 11-month period, more than 63% of discharges at four UPMC hospitals were delayed, leading to costs of an estimated $6.6 million in the sampled hospital units. These delays adversely affect patient experience and health outcomes, exacerbated by issues like the lack of post-discharge patient transportation and ineffective capacity management in the health system. Throughout the CMLH fellowship, we aimed to mitigate these issues by developing a discharge delay prediction module. This initiative was divided into two phases: (1) Length of Stay Prediction: Various regression models were benchmarked using prehospital data. Predicting longer lengths of stays posed challenges, mainly due to their infrequent occurrence in the dataset. (2) Predictability Analysis: Building on initial insights, the prediction task was refined based on length of stay percentiles, identifying patients with more predictable stays versus those harder to forecast. A key innovation in this study was the application of a contrastive learning approach. This methodology significantly outperformed traditional models, including Random Forest, XGBoost, Support Vector Machines, Logistic Regression, and Fully-Connected Neural Networks. By leveraging the contrastive learning paradigm, the study offers a robust solution to predict patient discharge times, providing valuable guidance for hospital management and optimizing patient flow.

Pre-trained CLIP Encoder for Embodied Instruction Following in ALFRED [Paper]

Abstract: We introduce a method employing pre-trained CLIP encoders to enhance model generalization in the ALFRED task. In contrast to previous literature where CLIP replaces the visual encoder, we suggest using CLIP as an additional module through an auxiliary object detection objective. We validate our method on the recently proposed Episodic Transformer architecture and demonstrate that incorporating CLIP improves task performance on the unseen validation set. Additionally, our analysis results support that CLIP especially helps with leveraging object descriptions, detecting small objects, and interpreting rare words.

Automatic Brain Pathology Analysis for Traumatic Brain Injury [Paper]

Abstract: Traumatic brain injury (TBI) is one of the leading causes of death and disability worldwide. Detailed studies of the microglial response after TBI require high throughput quantification of changes in microglial count and morphology in histological sections throughout the brain. In this paper, we present a fully automated end-to-end system that is capable of assessing microglial activation in white matter regions on whole slide images of Iba1 stained sections. Our approach involves the division of the full brain slides into smaller image patches that are subsequently automatically classified into white and grey matter sections. On the patches classified as white matter, we jointly apply functional minimization methods and deep learning classification to identify Iba1-immunopositive microglia. Detected cells are then automatically traced to preserve their complex branching structure after which fractal analysis is applied to determine the activation states of the cells. The resulting system detects white matter regions with 84% accuracy, detects microglia with a performance level of 0.70 (F1 score, the harmonic mean of precision and sensitivity) and performs binary microglia morphology classification with a 70% accuracy. This automated pipeline performs these analyses at a 20-fold increase in speed when compared to a human pathologist. Moreover, we have demonstrated robustness to variations in stain intensity common for Iba1 immunostaining. A preliminary analysis was conducted that indicated that this pipeline can identify differences in microglia response due to TBI. An automated solution to microglia cell analysis can greatly increase standardized analysis of brain slides, allowing pathologists and neuroscientists to focus on characterizing the associated underlying diseases and injuries.

Software Development Engineering Intern [2015]: Main focus areas researched and worked on during this internship included: Windows 10 Universal Application Platform (UAP), Windows 10 NFL Application, Development of a Key Performance Indicator (KPI) System, Mocking Framework Development, Coded User Interface (UI) Automation and Build Machine Automation Development.

Research and Development Intern [2016]: Developed an electronic nose sensor that is capable of selectively and sensitively detect biomarkers in exhaled breath to improve the emergency diagnosis of lung infections for patients with respiratory diseases including Acute Respiratory Distress Syndrome (ARDS). I designed a standalone signal processing algorithm and application tailored for gas chromatography data, which effectively isolated the presence of octane—a critical biomarker of ARDS in exhaled breath. This application was instrumental in enhancing the accuracy and reliability of the electronic nose sensor, especially given the challenges of non-real-time data processing.