A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study uses machine learning to analyze state drug policies and reduce overdose deaths.
A model learns symptom-drug relations for PD patients.
The study compares Euclidean and cosine distances in medical drug prescription prediction.
Deep learning predicts drug prescriptions across global health records.
Dr.S recommends cancer drugs based on genomic data.
Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources. Leveraging the patient-level temporal ordering information between numeric physiological measurements and various drug prescriptions provided in E…
The challenge in controlling stochastic systems in which low-probability events can set the system on catastrophic trajectories is to develop a robust ability to respond to such events without significantly compromising the optimality of the baseline control policy. This paper presents CelluDose, a stochastic simulatio…
We study the problem of detecting adverse drug events in electronic healthcare records. The challenge in this work is to aggregate heterogeneous data types involving diagnosis codes, drug codes, as well as lab measurements. An earlier framework proposed for the same problem demonstrated promising predictive performance…
Automated scoring engines are increasingly being used to score the free-form text responses that students give to questions. Such engines are not designed to appropriately deal with responses that a human reader would find alarming such as those that indicate an intention to self-harm or harm others, responses that all…
Drug-drug interactions are preventable causes of medical injuries and often result in doctor and emergency room visits. Computational techniques can be used to predict potential drug-drug interactions. We approach the drug-drug interaction prediction problem as a link prediction problem and present two novel methods fo…
PNNs improve personalized healthcare policies using mixed integer programming.
The opioid epidemic in the United States claims over 40,000 lives per year, and it is estimated that well over two million Americans have an opioid use disorder. Over-prescription and misuse of prescription opioids play an important role in the epidemic. Individuals who are prescribed opioids, and who are diagnosed wit…
Method learns optimal treatment policies from observational data.
The Prescriptive Canvas improves business outcomes by directly prescribing actions based on predictions.
A new neural network model for optimal treatment assignment.
Improves drug properties using a novel LLM and reinforcement learning.
Dynamic treatment recommendation systems based on large-scale electronic health records (EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant studies recommend treatments either use supervised learning (e.g. matching the indicator signal which denotes doctor prescriptions), or reinforc…
New approach makes deep reinforcement learning robust without assuming adversary knowledge.
TPSQRs model longitudinal event data, detecting ADRs from EHRs.
Product reviews and ratings on e-commerce websites provide customers with detailed insights about various aspects of the product such as quality, usefulness, etc. Since they influence customers' buying decisions, product reviews have become a fertile ground for abuse by sellers (colluding with reviewers) to promote the…
Designing a new drug is a lengthy and expensive process. As the space of potential molecules is very large (10^23-10^60), a common technique during drug discovery is to start from a molecule which already has some of the desired properties. An interdisciplinary team of scientists generates hypothesis about the required…
Graph-based framework predicts ADR signals from clinical data.
CPR models complex decision processes by breaking them into context-specific policies, improving interpretability and accuracy.
We report the first, to the best of our knowledge, hand-in-hand collaboration between human rights activists and machine learners, leveraging crowd-sourcing to study online abuse against women on Twitter. On a technical front, we carefully curate an unbiased yet low-variance dataset of labeled tweets, analyze it to acc…
Active search is a learning paradigm for actively identifying as many members of a given class as possible. A critical target scenario is high-throughput screening for scientific discovery, such as drug or materials discovery. In this paper, we approach this problem in Bayesian decision framework. We first derive the B…
The goal of personalized decision making is to map a unit's characteristics to an action tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic regime literature. In healthcare settings, optimizing policies with respect to a particular causal pat…
hyperSBINN improves drug cardiosafety assessment by efficiently modeling cardiac action potentials.
The occurrence of drug-drug-interactions (DDI) from multiple drug dispensations is a serious problem, both for individuals and health-care systems, since patients with complications due to DDI are likely to reenter the system at a costlier level. We present a large-scale longitudinal study (18 months) of the DDI phenom…
Method learns drug-disease representations for repositioning opportunities.
System automates identification of cancer drug repurposing from PubMed.
LLMs can identify tax strategies, potentially revolutionizing tax enforcement.
Model predicts anti-cancer drug responses using gene and molecular data.
Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.
PNNs improve treatment outcomes in TAVR and liver trauma.
Paper proposes an inductive RGCN for few-shot link prediction in drug-repurposing.
Automates infectious disease policy-making via inference in epidemiological models.
Deep neural network (DNN) based approaches hold significant potential for reinforcement learning (RL) and have already shown remarkable gains over state-of-art methods in a number of applications. The effectiveness of DNN methods can be attributed to leveraging the abundance of supervised data to learn value functions,…
Mitigates bias in text classification by weighting instances.
Generative model designs drug combinations for improved efficacy and reduced side effects.
We develop a prediction-based prescriptive model for learning optimal personalized treatments for patients based on their Electronic Health Records (EHRs). Our approach consists of: (i) predicting future outcomes under each possible therapy using a robustified nonlinear model, and (ii) adopting a randomized prescriptiv…
HMS-BERT detects cyberbullying in multiple languages and labels.
Paper integrates predictive and prescriptive tasks using bilevel optimization.
GeneDisco benchmarks experimental design for drug discovery.
Laboratory testing and medication prescription are two of the most important routines in daily clinical practice. Developing an artificial intelligence system that can automatically make lab test imputations and medication recommendations can save costs on potentially redundant lab tests and inform physicians of a more…
Study proposes a new approval policy for ML-based medical devices to prevent gradual performance degradation.
We describe two recently proposed machine learning approaches for discovering emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset Scan enables early detection of emerging patterns in spatio-temporal data, accounting for both the non-iid nature of the data and the fact that detecting subtle p…
One of the core tasks in multi-view learning is to capture relations among views. For sequential data, the relations not only span across views, but also extend throughout the view length to form long-term intra-view and inter-view interactions. In this paper, we present a new memory augmented neural network model that…
Project classifies Hinglish social content on platforms like Twitter, Reddit.