A GPT-based EHR modeling system for unsupervised novel disease detection.

Objective: To develop an Artificial Intelligence (AI)-based anomaly detection model as a complement of an "astute physician" in detecting novel disease cases in a hospital and preventing emerging outbreaks.

Methods: Data included hospitalized patients (n = 120,714) at a safety-net hospital in Massachusetts. A novel Generative Pre-trained Transformer (GPT)-based clinical anomaly detection system was designed and further trained using Empirical Risk Minimization (ERM), which can model a hospitalized patient's Electronic Health Records (EHR) and detect atypical patients.

A New Paradigm for Pandemic Preparedness.

Purpose Of Review: Preparing for pandemics requires a degree of interdisciplinary work that is challenging under the current paradigm. This review summarizes the challenges faced by the field of pandemic science and proposes how to address them.

Recent Findings: The structure of current siloed systems of research organizations hinders effective interdisciplinary pandemic research.

Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models.

Introduction: Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials.

Methods: We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases.

Automating biomedical literature review for rapid drug discovery: Leveraging GPT-4 to expedite pandemic response.

Objective: The rapid expansion of the biomedical literature challenges traditional review methods, especially during outbreaks of emerging infectious diseases when quick action is critical. Our study aims to explore the potential of ChatGPT to automate the biomedical literature review for rapid drug discovery.

Materials And Methods: We introduce a novel automated pipeline helping to identify drugs for a given virus in response to a potential future global health threat.

Predictive models of miscarriage on the basis of data from a preconception cohort study.

Objective: To use self-reported preconception data to derive models that predict the risk of miscarriage.

Design: Prospective preconception cohort study.

Setting: Not applicable.

Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations.

The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.

ITNR: Inversion Transformer-based Neural Ranking for cancer drug recommendations.

Personalized drug response prediction is an approach for tailoring effective therapeutic strategies for patients based on their tumors' genomic characterization. While machine learning methods are widely employed in the literature, they often struggle to capture drug-cell line relations across various cell lines. In addressing this challenge, our study introduces a novel listwise Learning-to-Rank (LTR) model named Inversion Transformer-based Neural Ranking (ITNR).

Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records.

Introduction: Predictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis.

Methods: This is a retrospective cohort study from a SafetyNet hospital's electronic health records (EHR) from 2003-2016.

Fusion of Low-Level Descriptors of Digital Voice Recordings for Dementia Assessment.

Digital voice recordings can offer affordable, accessible ways to evaluate behavior and function. We assessed how combining different low-level voice descriptors can evaluate cognitive status. Using voice recordings from neuropsychological exams at the Framingham Heart Study, we developed a machine learning framework fusing spectral, prosodic, and sound quality measures early in the training cycle.

Large Language Models in Neurology Research and Future Practice.

Recent advancements in generative artificial intelligence, particularly using large language models (LLMs), are gaining increased public attention. We provide a perspective on the potential of LLMs to analyze enormous amounts of data from medical records and gain insights on specific topics in neurology. In addition, we explore use cases for LLMs, such as early diagnosis, supporting patient and caregivers, and acting as an assistant for clinicians.

Improved prediction of MHC-peptide binding using protein language models.

Major histocompatibility complex Class I (MHC-I) molecules bind to peptides derived from intracellular antigens and present them on the surface of cells, allowing the immune system (T cells) to detect them. Elucidating the process of this presentation is essential for regulation and potential manipulation of the cellular immune system. Predicting whether a given peptide binds to an MHC molecule is an important step in the above process and has motivated the introduction of many computational approaches to address this problem.

Predicting polycystic ovary syndrome (PCOS) with machine learning algorithms from electronic health records.

Introduction: Predictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis.

Methods: This is a retrospective cohort study from a SafetyNet hospital's electronic health records (EHR) from 2003-2016.

A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent.

This paper is concerned with minimizing the average of cost functions over a network in which agents may communicate and exchange information with each other. We consider the setting where only noisy gradient information is available. To solve the problem, we study the distributed stochastic gradient descent (DSGD) method and perform a non-asymptotic convergence analysis.

Routing and Rebalancing Intermodal Autonomous Mobility-on-Demand Systems in Mixed Traffic.

This paper studies congestion-aware route-planning policies for intermodal Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on-demand mobility jointly with public transit under mixed traffic conditions (consisting of AMoD and private vehicles). First, we devise a network flow model to jointly optimize the AMoD routing and rebalancing strategies in a congestion-aware fashion by accounting for the endogenous impact of AMoD flows on travel time. Second, we capture the effect of exogenous traffic stemming from private vehicles adapting to the AMoD flows in a user-centric fashion by leveraging a sequential approach.

Distributionally robust learning-to-rank under the Wasserstein metric.

Despite their satisfactory performance, most existing listwise Learning-To-Rank (LTR) models do not consider the crucial issue of robustness. A data set can be contaminated in various ways, including human error in labeling or annotation, distributional data shift, and malicious adversaries who wish to degrade the algorithm's performance. It has been shown that Distributionally Robust Optimization (DRO) is resilient against various types of noise and perturbations.

Personalized hypertension treatment recommendations by a data-driven model.

Background: Hypertension is a prevalent cardiovascular disease with severe longer-term implications. Conventional management based on clinical guidelines does not facilitate personalized treatment that accounts for a richer set of patient characteristics.

Methods: Records from 1/1/2012 to 1/1/2020 at the Boston Medical Center were used, selecting patients with either a hypertension diagnosis or meeting diagnostic criteria (≥ 130 mmHg systolic or ≥ 90 mmHg diastolic, n = 42,752).

Social determinants of health and the prediction of missed breast imaging appointments.

Background: Predictive models utilizing social determinants of health (SDH), demographic data, and local weather data were trained to predict missed imaging appointments (MIA) among breast imaging patients at the Boston Medical Center (BMC). Patients were characterized by many different variables, including social needs, demographics, imaging utilization, appointment features, and weather conditions on the date of the appointment.

Methods: This HIPAA compliant retrospective cohort study was IRB approved.

Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach.

Introduction: Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia.

Methods: A novel natural language processing approach is developed and validated to identify different stages of dementia based on automated transcription of digital voice recordings of subjects' neuropsychological tests conducted by the Framingham Heart Study (n = 1084). Transcribed sentences from the test were encoded into quantitative data and several models were trained and tested using these data and the participants' demographic characteristics.

Development and validation of predictive models for COVID-19 outcomes in a safety-net hospital population.

Objective: To develop predictive models of coronavirus disease 2019 (COVID-19) outcomes, elucidate the influence of socioeconomic factors, and assess algorithmic racial fairness using a racially diverse patient population with high social needs.

Materials And Methods: Data included 7,102 patients with positive (RT-PCR) severe acute respiratory syndrome coronavirus 2 test at a safety-net system in Massachusetts. Linear and nonlinear classification methods were applied.

Redefining and Validating Digital Biomarkers as Fluid, Dynamic Multi-Dimensional Digital Signal Patterns.

"Digital biomarker" is a term broadly and indiscriminately applied and often limited in its conceptualization to mimic well-established biomarkers as defined and approved by regulatory agencies such as the United States Food and Drug Administration (FDA). There is a practical urgency to revisit the definition of a digital biomarker and expand it beyond current methods of identification and validation. Restricting the promise of digital technologies within the realm of currently defined biomarkers creates a missed opportunity.

Informative predictors of pregnancy after first IVF cycle using eIVF practice highway electronic health records.

The aim of this study is to determine the most informative pre- and in-cycle variables for predicting success for a first autologous oocyte in-vitro fertilization (IVF) cycle. This is a retrospective study using 22,413 first autologous oocyte IVF cycles from 2001 to 2018. Models were developed to predict pregnancy following an IVF cycle with a fresh embryo transfer.

Predictive models of pregnancy based on data from a preconception cohort study.

Study Question: Can we derive adequate models to predict the probability of conception among couples actively trying to conceive?

Summary Answer: Leveraging data collected from female participants in a North American preconception cohort study, we developed models to predict pregnancy with performance of ∼70% in the area under the receiver operating characteristic curve (AUC).

What Is Known Already: Earlier work has focused primarily on identifying individual risk factors for infertility. Several predictive models have been developed in subfertile populations, with relatively low discrimination (AUC: 59-64%).

HPC Data Center Participation in Demand Response: an Adaptive Policy with QoS Assurance.

Demand response programs help stabilize the electricity grid by providing monetary stimulus to consumers if they regulate their power consumption following market requirements. Regulation service, a market that requires participants to regulate power by following a signal updated every few seconds, is particularly beneficial to HPC data centers since data centers are capable of increasing/decreasing power consumption owing to the flexibility in running workloads and the availability of power control mechanisms. While prior works have explored how data centers can provide regulation service reserves, Quality-of-Service (QoS) provisioning for the jobs running at the data centers has not been considered.