2,685 research outputs found
Design and implementation of a deep recurrent model for prediction of readmission in urgent care using electronic health records
There has been a steady growth in machine learning research in healthcare, however, progress is difficult to measure because of the use of different cohorts, task definitions and
input variables. To take the advantage of the availability and value of digital health data, we aim to predict unplanned
readmissions to the intensive care unit (ICU) from a publicly
available Critical Care dataset called Medical Information Mart for Intensive Care (MIMIC-III). In this research, we formulate a heterogeneous LSTM and CNN architecture specifically to create a model of readmission risk. Our proposed predictive framework outperformed all the benchmark classifiers such as support vector machine, random forest and logistic regression models on all performance measures (AUC, accuracy and precision) except on recall where random forest performed slightly better. Predictions from these models will help in resource planning and decrease mortality or length of stay in clinical care settings
Outcome-Oriented Predictive Process Monitoring to Predict Unplanned ICU Readmission in MIMIC-IV Database
Unplanned readmission entails unnecessary risks for patients and avoidable waste of medical resources, especially intensive care unit (ICU) readmissions, which increases likelihood of length of stay and more severely mortality. Identifying patients who are likely to suffer unplanned ICU readmission can benefit both patients and hospitals. Readmission is typically predicted by statistical features extracted from completed ICU stays. The development of predictive process monitoring (PPM) technique aims to learn from complete traces and predict the outcome of ongoing ones. In this paper, we adopt PPM to view ICU stay from electronic health record (EHR) as a continuous process trace to enable us to discover clinical and also process features to predict likelihood of readmission. Using events logs extracted from MIMIC-IV database, the results show that our approach can achieve up to 65% accuracy during the early stage of each ICU stay and demonstrate the feasibility of applying PPM to unplanned ICU readmission prediction
Machine learning model to predict mental health crises from electronic health records
The timely identification of patients who are at risk of a mental health crisis can lead to improved outcomes and to the mitigation of burdens and costs. However, the high prevalence of mental health problems means that the manual review of complex patient records to make proactive care decisions is not feasible in practice. Therefore, we developed a machine learning model that uses electronic health records to continuously monitor patients for risk of a mental health crisis over a period of 28 days. The model achieves an area under the receiver operating characteristic curve of 0.797 and an area under the precision-recall curve of 0.159, predicting crises with a sensitivity of 58% at a specificity of 85%. A follow-up 6-month prospective study evaluated our algorithm's use in clinical practice and observed predictions to be clinically valuable in terms of either managing caseloads or mitigating the risk of crisis in 64% of cases. To our knowledge, this study is the first to continuously predict the risk of a wide range of mental health crises and to explore the added value of such predictions in clinical practice
From Plate to Prevention: A Dietary Nutrient-aided Platform for Health Promotion in Singapore
Singapore has been striving to improve the provision of healthcare services
to her people. In this course, the government has taken note of the deficiency
in regulating and supervising people's nutrient intake, which is identified as
a contributing factor to the development of chronic diseases. Consequently,
this issue has garnered significant attention. In this paper, we share our
experience in addressing this issue and attaining medical-grade nutrient intake
information to benefit Singaporeans in different aspects. To this end, we
develop the FoodSG platform to incubate diverse healthcare-oriented
applications as a service in Singapore, taking into account their shared
requirements. We further identify the profound meaning of localized food
datasets and systematically clean and curate a localized Singaporean food
dataset FoodSG-233. To overcome the hurdle in recognition performance brought
by Singaporean multifarious food dishes, we propose to integrate supervised
contrastive learning into our food recognition model FoodSG-SCL for the
intrinsic capability to mine hard positive/negative samples and therefore boost
the accuracy. Through a comprehensive evaluation, we present performance
results of the proposed model and insights on food-related healthcare
applications. The FoodSG-233 dataset has been released in
https://foodlg.comp.nus.edu.sg/
Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML
Medical applications of machine learning (ML) have experienced a surge in
popularity in recent years. The intensive care unit (ICU) is a natural habitat
for ML given the abundance of available data from electronic health records.
Models have been proposed to address numerous ICU prediction tasks like the
early detection of complications. While authors frequently report
state-of-the-art performance, it is challenging to verify claims of
superiority. Datasets and code are not always published, and cohort
definitions, preprocessing pipelines, and training setups are difficult to
reproduce. This work introduces Yet Another ICU Benchmark (YAIB), a modular
framework that allows researchers to define reproducible and comparable
clinical ML experiments; we offer an end-to-end solution from cohort definition
to model evaluation. The framework natively supports most open-access ICU
datasets (MIMIC III/IV, eICU, HiRID, AUMCdb) and is easily adaptable to future
ICU datasets. Combined with a transparent preprocessing pipeline and extensible
training code for multiple ML and deep learning models, YAIB enables unified
model development. Our benchmark comes with five predefined established
prediction tasks (mortality, acute kidney injury, sepsis, kidney function, and
length of stay) developed in collaboration with clinicians. Adding further
tasks is straightforward by design. Using YAIB, we demonstrate that the choice
of dataset, cohort definition, and preprocessing have a major impact on the
prediction performance - often more so than model class - indicating an urgent
need for YAIB as a holistic benchmarking tool. We provide our work to the
clinical ML community to accelerate method development and enable real-world
clinical implementations. Software Repository:
https://github.com/rvandewater/YAIB.Comment: Main benchmark: https://github.com/rvandewater/YAIB, Cohort
generation: https://github.com/rvandewater/YAIB-cohorts, Models:
https://github.com/rvandewater/YAIB-model
Unmasking Bias and Inequities: A Systematic Review of Bias Detection and Mitigation in Healthcare Artificial Intelligence Using Electronic Health Records
Objectives: Artificial intelligence (AI) applications utilizing electronic
health records (EHRs) have gained popularity, but they also introduce various
types of bias. This study aims to systematically review the literature that
address bias in AI research utilizing EHR data. Methods: A systematic review
was conducted following the Preferred Reporting Items for Systematic Reviews
and Meta-analyses (PRISMA) guideline. We retrieved articles published between
January 1, 2010, and October 31, 2022, from PubMed, Web of Science, and the
Institute of Electrical and Electronics Engineers. We defined six major types
of bias and summarized the existing approaches in bias handling. Results: Out
of the 252 retrieved articles, 20 met the inclusion criteria for the final
review. Five out of six bias were covered in this review: eight studies
analyzed selection bias; six on implicit bias; five on confounding bias; four
on measurement bias; two on algorithmic bias. For bias handling approaches, ten
studies identified bias during model development, while seventeen presented
methods to mitigate the bias. Discussion: Bias may infiltrate the AI
application development process at various stages. Although this review
discusses methods for addressing bias at different development stages, there is
room for implementing additional effective approaches. Conclusion: Despite
growing attention to bias in healthcare AI, research using EHR data on this
topic is still limited. Detecting and mitigating AI bias with EHR data
continues to pose challenges. Further research is needed to raise a
standardized method that is generalizable and interpretable to detect, mitigate
and evaluate bias in medical AI.Comment: 29 pages, 2 figures, 2 tables, 2 supplementary files, 66 reference
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