Rom Gutman
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View article: PyDTS: A Python Package for Discrete-Time Survival Analysis with Competing Risks and Optional Penalization
PyDTS: A Python Package for Discrete-Time Survival Analysis with Competing Risks and Optional Penalization Open
PyDTS: A Python Package for Discrete-Time Survival Analysis with Competing Risks and Optional Penalization
View article: From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies
From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies Open
We propose a framework for building patient-specific treatment recommendation models, building on the large recent literature on learning patient-level causal models and inspired by the target trial paradigm of Hernan and Robins. We focus …
View article: Improving Inverse Probability Weighting by Post-calibrating Its Propensity Scores
Improving Inverse Probability Weighting by Post-calibrating Its Propensity Scores Open
Theoretical guarantees for causal inference using propensity scores are partially based on the scores behaving like conditional probabilities. However, prediction scores between zero and one do not necessarily behave like probabilities, es…
View article: Propensity score models are better when post-calibrated
Propensity score models are better when post-calibrated Open
Theoretical guarantees for causal inference using propensity scores are partly based on the scores behaving like conditional probabilities. However, scores between zero and one, especially when outputted by flexible statistical estimators,…
View article: What drives performance in machine learning models for predicting heart failure outcome?
What drives performance in machine learning models for predicting heart failure outcome? Open
Aims The development of acute heart failure (AHF) is a critical decision point in the natural history of the disease and carries a dismal prognosis. The lack of appropriate risk-stratification tools at hospital discharge of AHF patients si…
View article: PyDTS: A Python Package for Discrete Time Survival Analysis with Competing Risks
PyDTS: A Python Package for Discrete Time Survival Analysis with Competing Risks Open
A Python Package for Discrete Time Survival Analysis with Competing Risks
View article: PyDTS: A Python Package for Discrete-Time Survival Analysis with Competing Risks and Optional Penalization
PyDTS: A Python Package for Discrete-Time Survival Analysis with Competing Risks and Optional Penalization Open
Time-to-event (survival) analysis models the time until a pre-specified event occurs. When time is measured in discrete units or rounded into intervals, standard continuous-time models can yield biased estimators. In addition, the event of…
View article: Hospital load and increased COVID-19 related mortality in Israel
Hospital load and increased COVID-19 related mortality in Israel Open
The spread of Coronavirus disease 19 (COVID-19) has led to many healthcare systems being overwhelmed by the rapid emergence of new cases. Here, we study the ramifications of hospital load due to COVID-19 morbidity on in-hospital mortality …
View article: Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study
Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study Open
Objective The spread of coronavirus disease 2019 (COVID-19) has led to severe strain on hospital capacity in many countries. We aim to develop a model helping planners assess expected COVID-19 hospital resource utilization based on individ…
View article: Hospital load and increased COVID-19 related mortality - a nationwide study in Israel
Hospital load and increased COVID-19 related mortality - a nationwide study in Israel Open
The spread of Coronavirus disease 19 (COVID-19) has led to many healthcare systems being overwhelmed by the rapid emergence of new cases within a short period of time. We explore the ramifications of hospital load due to COVID-19 morbidity…
View article: What drives success in models predicting heart failure outcome?
What drives success in models predicting heart failure outcome? Open
Introduction The development of acute heart failure (AHF) is a critical decision-point in the natural history of heart failure and carries a dismal prognosis. The lack of appropriate risk-stratification tools for AHF patients limits physic…
View article: Development and validation of a machine learning model for predicting illness trajectory and hospital resource utilization of COVID-19 hospitalized patients – a nationwide study
Development and validation of a machine learning model for predicting illness trajectory and hospital resource utilization of COVID-19 hospitalized patients – a nationwide study Open
Background The spread of COVID-19 has led to a severe strain on hospital capacity in many countries. There is a need for a model to help planners assess expected COVID-19 hospital resource utilization. Methods Retrospective nationwide coho…