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.
Enhances detection of adverse drug events using diverse healthcare record data.
TPSQRs model longitudinal event data, detecting ADRs from EHRs.
The increased adoption of Electronic Health Records(EHRs) has brought changes to the way the patient care is carried out. The rich heterogeneous and temporal data space stored in EHRs can be leveraged by machine learning models to capture the underlying information and make clinically relevant predictions. This can be …
Deep Rule Forests identifies drug-drug and drug-disease interactions causing AKI.
Background: The problem of predicting whether a drug combination of arbitrary orders is likely to induce adverse drug reactions is considered in this manuscript. Methods: Novel kernels over drug combinations of arbitrary orders are developed within support vector machines for the prediction. Graph matching methods are …
Paper proposes a new method for predicting drug interactions using adversarial autoencoders.
Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statistical relational learning is a natural fit for analyzing EMRs but is less adept at handling their inherent latent structure, such as connecti…
CASTER predicts drug interactions using chemical substructures.
Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects. The detection of polypharmacy side effects is usually done in Phase IV clinical trials, but there are still plenty which remain undiscovered when the drugs are put on the market. Such acc…
With the increased availability of large databases of electronic health records (EHRs) comes the chance of enhancing health risks screening. Most post-marketing detections of adverse drug reaction (ADR) rely on physicians' spontaneous reports, leading to under reporting. To take up this challenge, we develop a scalable…
Machine learning predicts biologic therapy outcomes in psoriasis patients.
This paper uses MIL and MHCNN-RNN to predict precursors to aviation safety events.
New method identifies sepsis-related patient features in EMR data.
Study finds non-adherence to schizophrenia meds leads to earlier adverse events.
Graph-based framework predicts ADR signals from clinical data.
Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existi…
Study predicts adverse events in Afghanistan using time series data.
Detecting real-time price impact in algo trading
Monitoring the biomedical literature for cases of Adverse Drug Reactions (ADRs) is a critically important and time consuming task in pharmacovigilance. The development of computer assisted approaches to aid this process in different forms has been the subject of many recent works. One particular area that has shown pro…
PHASE predicts surgical complications from physiological signals.
The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases and co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions, in which activi…
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…
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…
Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that ha…
Survey on risk-aware multi-armed bandits for better decision-making.
Method detects surgical deviations during laparoscopic rectopexy.
Study shows SEC crypto classification led to significant market reactions.
Predicts stock price changes based on clinical trial announcements.
Modern treatments for Type 1 diabetes (T1D) use devices known as artificial pancreata (APs), which combine an insulin pump with a continuous glucose monitor (CGM) operating in a closed-loop manner to control blood glucose levels. In practice, poor performance of APs (frequent hyper- or hypoglycemic events) is common en…
Model predicts drug overdose hotspots using EMS and toxicology data.
Survival analysis in the presence of multiple possible adverse events, i.e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.). Since only one event is typically observed, the incidence of an event of interest is often obscured by other related competing events. This nonidentifiabil…
This paper reviews radar-based nowcasting techniques for short-term weather predictions.
The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according…
AI traders learn to exploit meta-orders from slower traders, increasing their profits.
auton-survival simplifies survival analysis for healthcare data.
The paper proposes a method to identify subgroups with different treatment effects in time-to-event data.
Study examines tech stocks' reactions to Facebook data leak scandal.
Warfarin dosing remains challenging due to narrow therapeutic index and highly individual variability. Incorrect warfarin dosing is associated with devastating adverse events. Remarkable efforts have been made to develop the machine learning based warfarin dosing algorithms incorporating clinical factors and genetic va…
A new drug embedding method using hierarchical drug relations and chemical structures.
Unified framework for counterfactual survival analysis improves treatment effect estimation.
New method predicts drug interactions from drug images.
Graph-augmented CNN predicts drug interactions with high accuracy.
Selecting the right drugs for the right patients is a primary goal of precision medicine. In this manuscript, we consider the problem of cancer drug selection in a learning-to-rank framework. We have formulated the cancer drug selection problem as to accurately predicting 1). the ranking positions of sensitive drugs an…
This study evaluates subgroup analysis methods for time-to-event outcomes in randomized controlled trials.
With the horrific Jamal Khashoggi killing, Mohammed Bin Salman's image in the international community has been damaged. This study seeks to test whether Khashoggi murder discourage businesses from investing in Saudi Arabia. We use an event-study methodology and asset pricing model to assess, at sectoral level, the dyna…
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…
CardiGraphormer uses SSL and GNNs to improve drug discovery.
Method learns drug-disease representations for repositioning opportunities.