Integrating multimodal data through interpretable heterogeneous ensembles.

Motivation: Integrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems. Results: We propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms and uses heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data and mortality due to coronavirus disease 2019 (COVID-19) from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling. Availability and implementation: Code and data are available at https://github.com/GauravPandeyLab/ensemble_integration. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Private Health Sector in India-Ready and Willing, Yet Underutilized in the Covid-19 Pandemic: A Cross-Sectional Study.

Background: The private medical sector is a resource that must be estimated for efficient inclusion into public healthcare during pandemics. Methods: A survey was conducted among private healthcare workers to ascertain their views on the potential resources that can be accessed from the private sector and methods to do the same. Results: There were 213 respondents, 80% of them being doctors. Nearly half (47.4%) felt that the contribution from the private medical sector has been suboptimal. Areas suggested for improved contributions by the private sector related to patient care (71.8%) and provision of equipment (62.4%), with fewer expectations (39.9%) on the research front. Another area of deemed support was maintaining continuity of care for non-COVID patients using virtual consultation services (77.4%), tele-consultation being the preferred option (60%). 58.2% felt that the Government had not involved the private sector adequately; and 45.1% felt they should be part of policy-making. Conclusion: A streamlined pathway to facilitate the private sector to join hands with the public sector for a national cause is the need of the hour. Through our study, we have identified gaps in the current contribution by the private sector and identified areas in which they could contribute, by their own admission.

Clinical features of COVID-19 mortality: development and validation of a clinical prediction model.

Background: The COVID-19 pandemic has affected millions of individuals and caused hundreds of thousands of deaths worldwide. Predicting mortality among patients with COVID-19 who present with a spectrum of complications is very difficult, hindering the prognostication and management of the disease. We aimed to develop an accurate prediction model of COVID-19 mortality using unbiased computational methods, and identify the clinical features most predictive of this outcome. Methods: In this prediction model development and validation study, we applied machine learning techniques to clinical data from a large cohort of patients with COVID-19 treated at the Mount Sinai Health System in New York City, NY, USA, to predict mortality. We analysed patient-level data captured in the Mount Sinai Data Warehouse database for individuals with a confirmed diagnosis of COVID-19 who had a health system encounter between March 9 and April 6, 2020. For initial analyses, we used patient data from March 9 to April 5, and randomly assigned (80:20) the patients to the development dataset or test dataset 1 (retrospective). Patient data for those with encounters on April 6, 2020, were used in test dataset 2 (prospective). We designed prediction models based on clinical features and patient characteristics during health system encounters to predict mortality using the development dataset. We assessed the resultant models in terms of the area under the receiver operating characteristic curve (AUC) score in the test datasets. Findings: Using the development dataset (n=3841) and a systematic machine learning framework, we developed a COVID-19 mortality prediction model that showed high accuracy (AUC=0·91) when applied to test datasets of retrospective (n=961) and prospective (n=249) patients. This model was based on three clinical features: patient's age, minimum oxygen saturation over the course of their medical encounter, and type of patient encounter (inpatient vs outpatient and telehealth visits). Interpretation: An accurate and parsimonious COVID-19 mortality prediction model based on three features might have utility in clinical settings to guide the management and prognostication of patients affected by this disease. External validation of this prediction model in other populations is needed. Funding: National Institutes of Health.