Effective hospital readmission prediction models using machine-learned features

Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data

Depression and anxiety are common comorbidities of stroke. Research has shown that about 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with such adverse mental outcomes are often attributed to poorer health outcomes, such as higher mortality rates. The objective of this study is to use deep learning (DL) methods to predict the risk of a stroke survivor experiencing post-stroke depression and/or post-stroke anxiety, which is collectively known as post-stroke adverse mental outcomes (PSAMO). This study studied 179 patients with stroke, who were further classified into PSAMO versus no PSAMO group based on the results of validated depression and anxiety questionnaires, which are the industry's gold standard. This study collected demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. In addition, sequential data such as daily lab results taken seven consecutive days after admission are also collected. The combination of using DL algorithms, such as multi-layer perceptron (MLP) and long short-term memory (LSTM), which can process complex patterns in the data, and the inclusion of new data types, such as sequential data, helped to improve model performance. Accurate prediction of PSAMO helps clinicians make early intervention care plans and potentially reduce the incidence of PSAMO.

A Qualitative Study Examining the Unintended Consequences from Implementing a Case Management Team to Reduce Avoidable Hospital Readmission in Singapore

Background

Countries are implementing interventions to reduce avoidable hospital readmissions. However, evaluating such interventions are potentially complex. These interventions can cause unintended consequences, and they are among the most common causes of the intervention's failure. The objective of this study was to identify the unintended consequences from implementing a pilot case management team to reduce avoidable hospital readmissions at a tertiary hospital in Singapore.

Methods

We conducted five in-depth semi-structured interviews with stakeholders who were involved in the planning, development, and implementation of the intervention in addition to analysing 12 intervention documents. Deductive thematic analysis using Rogers' diffusion of innovation theory was conducted.

Results

Data analysis generated seven subthemes: ineffective targeting of patient population, fund constraints, lack of patient ownership, limited post discharge follow up, comprehensive care approaches, role overlap and patient confusion. The absence of a readmission risk assessment tool resulted in care plan needs assessments being conducted for all admitted patients, rather than targeting those who would benefit most. This broad approach overwhelmed care coordination efforts. The initial plan to form a specialised intervention team responsible for care plan needs assessments could not be fully established due to funding constraints. As a result, the intervention team functioned more as a consulting service, providing recommendations to the primary team, which retained decision-making authority. Overlapping roles with existing case managers caused patient confusion, prompting the intervention team to step back and support care plan needs assessment remotely.

Conclusion

Overall, results suggest that intervention team recognised a problem and participated in the intervention. This became the foundation for implementing change. However, the unintended consequences undermined the intervention from achieving its objectives and as a result the intervention was stopped. Decision-makers should pay attention to these unintended consequences to inform effective implementation and refine future interventions.

Explainable machine learning to identify risk factors for unplanned hospital readmissions in Nova Scotian hospitals

OBJECTIVE

A report from the Canadian Institute for Health Information found unplanned hospital readmissions (UHR) common, costly, and potentially avoidable, estimating a $1.8 billion cost to the Canadian healthcare system associated with inpatient readmissions within 30 days of discharge for the studied period (11 months). The first step towards addressing this costly problem is enabling early detection of patients at risk through detecting UHR risk factors.

METHODOLOGY

We utilized Machine Learning and explainability tools to examine risk factors for UHR within 30 days of discharge, utilizing data from Nova Scotian (Canada) healthcare institutions (2015-2022). To the best of our knowledge, our research constitutes the most comprehensive study on UHR risk factors for the province.

RESULTS

We found that predicting UHR solely from healthcare data has limitations, as discharge information often falls short of accurately predicting readmission occurrences. However, despite this inherent limitation, integrating explainability tools offers insights into the underlying factors contributing to readmission risk, empowering medical personnel with information to improve patient care and outcomes. As part of this work, we identify and report risk factors for UHR and build a guideline to support medical personnel's decision-making regarding targeted post-discharge follow-ups. We found that conditions such as heart failure and Chronic Obstructive Pulmonary Disease (COPD) are associated with a higher likelihood of readmission. Patients admitted for procedures related to childbirth have a lower probability of readmission. We studied the impact of the admission type, patient characteristics, and patient stay characteristics on UHR. For example, we found that new and elective admission patients are less likely to be readmitted, while patients who received a transfusion are more likely to be readmitted.

CONCLUSIONS

We validated the risk factors and the guidelines using real-world data. Our results suggested that our proposal correctly identifies risk factors and effectively produces valuable guidelines for medical personnel. The guideline evaluation suggests we can screen half the patients while capturing more than 72% of the readmission episodes. Our study contributes insights into the challenge of identifying risk factors for UHR while providing a practical guideline for healthcare professionals to identify factors influencing patient readmission, particularly within Nova Scotia.

Advancements in Managing Choledocholithiasis and Acute Cholangitis in the Elderly: A Comprehensive Review

The increasing elderly population has led to a rising prevalence of choledocholithiasis and acute cholangitis, presenting unique diagnostic and therapeutic challenges owing to age-related physiological changes and multiple comorbidities. This comprehensive review synthesizes current evidence and recent advances in managing these conditions in elderly patients, with a particular focus on diagnostic innovations, therapeutic strategies, and perioperative optimization. Recent advances in diagnostic modalities, including enhanced imaging techniques and AI-assisted systems, have improved early detection accuracy, whereas minimally invasive interventions, particularly endoscopic retrograde cholangiopancreatography (ERCP) and laparoscopic common bile duct exploration (LCBDE), have demonstrated superior outcomes when combined with comprehensive perioperative care. The implementation of multidisciplinary approaches and personalized treatment strategies has significantly improved patient outcomes, with evidence supporting the critical role of early antibiotic intervention, careful surgical selection, and enhanced recovery protocols in reducing morbidity and mortality. The optimal management of elderly patients with choledocholithiasis and acute cholangitis requires a systematic, individualized approach incorporating advanced diagnostic techniques, minimally invasive interventions, and comprehensive perioperative care, while future research should focus on developing age-specific treatment algorithms and validating novel therapeutic approaches.

Barriers to Implementing Effective Healthcare Practices for the Aging Population: Approaches to Identification and Management

The aging population presents a growing challenge to healthcare systems, necessitating urgent adaptations to meet the complex needs of older adults. Existing healthcare models often lack integration and fail to provide patient-centered care, leading to fragmented services, suboptimal outcomes, increased hospitalizations, and escalating healthcare costs. This narrative review aims to systematically identify and categorize the key barriers to effective healthcare implementation for the elderly, evaluate current healthcare models and their limitations, and explore evidence-based strategies to improve care delivery. A comprehensive literature search was conducted in PubMed, MEDLINE, Scopus, and Web of Science for studies published from 2000 to October 2024. The identified barriers span multiple domains, including patient-related challenges such as low health literacy and socioeconomic disparities, disease-specific factors like frailty and multimorbidity, provider-related constraints such as inadequate geriatric training, and system-wide deficiencies in primary care infrastructure and policy support. To address these challenges, this review explores emerging solutions, including risk stratification tools, integrated healthcare models, digital health innovations, and artificial intelligence-driven interventions. By providing a structured analysis of barriers and solutions, this review aims to inform policy and healthcare practices that enhance elderly care, reduce hospital readmissions, and optimize resource utilization in aging populations.

Support Vector Machines: A Literature Review on Their Application in Analyzing Mass Data for Public Health

This study considers the literature on support vector machines (SVMs) in the area of public health data analysis, particularly evaluating their ability to harness big data for disease classification and health predictions. SVMs have been remarkably embraced for two decades in clinical diagnosis, patient management, and prediction of health trends owing to their high precision and robustness. This review suggests the ability of the method to support spatially relevant health system responses through the assessment of SVM advantages and disadvantages in public health and future research agendas, including improving scalability, integrating SVMs with emerging data sources like the Internet of Things (IoT) and genomic data, and enhancing model transparency to support real-world public health decision-making.

Survival models and longitudinal medical events for hospital readmission forecasting

BACKGROUND

The rate of 30-day all-cause hospital readmissions can affect the funding a hospital receives. An accurate and reliable readmission prediction model could save money and increase quality-of-care. Few projects have explored formulating this task as a survival prediction problem, where models can exploit a real-valued time-to-readmission target. This paper demonstrates the effectiveness of a survival-inspired readmission model, especially when paired with a longitudinal patient representation that is agnostic to disease-cohort and predictive task.

METHODS

We forecast readmissions for a population-level cohort of 421,088 patients discharged in 2015 and 2016 from hospitals in Alberta, Canada. Clinical features and sequences of historical medical codes (calculated from at least four full years prior to discharge) from linked administrative sources serve as model inputs. We trained binary 30-day readmission models (XGBoost and a Deep Neural Network) and time-to-event readmission models (CoxPH and N-MTLR) with and without machine-learned medical knowledge at initialization, then compared against the popular LACE-based model using the AUROC score at 30 days (AUROC@30). Survival models are additionally evaluated using concordance, Integrated Brier, and L1-loss scores.

RESULTS

All models that utilize sequence features markedly out-perform even the best models trained on only clinical features. Further, a time-to-event target improves predictive performance at 30 days, given the same model inputs and architecture. N-MTLR, using solely sequence inputs and initialized with pre-learned medical knowledge, achieves an average AUROC@30 of 0.8460 over five folds with a standard deviation of 0.003. All trained models match or out-perform the LACE baseline of 0.6587±0.003.

CONCLUSION

Sequences of administrative medical codes contain rich predictive information for forecasting readmissions, and embedding medical knowledge a priori using machine learning provides readmission models an advantageous foundation for training. When combined with a model that can leverage a time-to-event target, excellent performance is possible on the 30-day all-cause readmission task using only administrative data.

Impact of wearable device data and multi-scale entropy analysis on improving hospital readmission prediction

OBJECTIVE

Unplanned readmissions following a hospitalization remain common despite significant efforts to curtail these. Wearable devices may offer help identify patients at high risk for an unplanned readmission.

MATERIALS AND METHODS

We conducted a multi-center retrospective cohort study using data from the All of Us data repository. We included subjects with wearable data and developed a baseline Feedforward Neural Network (FNN) model and a Long Short-Term Memory (LSTM) time-series deep learning model to predict daily, unplanned rehospitalizations up to 90 days from discharge. In addition to demographic and laboratory data from subjects, post-discharge data input features include wearable data and multiscale entropy features based on intraday wearable time series. The most significant features in the LSTM model were determined by permutation feature importance testing.

RESULTS

In sum, 612 patients met inclusion criteria. The complete LSTM model had a higher area under the receiver operating characteristic curve than the FNN model (0.83 vs 0.795). The 5 most important input features included variables from multiscale entropy (steps) and number of active steps per day.

DISCUSSION

Data available from wearable devices can improve ability to predict readmissions. Prior work has focused on predictors available up to discharge or on additional data abstracted from wearable devices. Our results from 35 institutions highlight how multiscale entropy can improve readmission prediction and may impact future work in this domain.

CONCLUSION

Wearable data and multiscale entropy can improve prediction of a deep-learning model to predict unplanned 90-day readmissions. Prospective studies are needed to validate these findings.

Forecasting Patient Early Readmission from Irish Hospital Discharge Records Using Conventional Machine Learning Models

BACKGROUND/OBJECTIVES

Predicting patient readmission is an important task for healthcare risk management, as it can help prevent adverse events, reduce costs, and improve patient outcomes. In this paper, we compare various conventional machine learning models and deep learning models on a multimodal dataset of electronic discharge records from an Irish acute hospital.

METHODS

We evaluate the effectiveness of several widely used machine learning models that leverage patient demographics, historical hospitalization records, and clinical diagnosis codes to forecast future clinical risks. Our work focuses on addressing two key challenges in the medical fields, data imbalance and the variety of data types, in order to boost the performance of machine learning algorithms. Furthermore, we also employ SHapley Additive Explanations (SHAP) value visualization to interpret the model predictions and identify both the key data features and disease codes associated with readmission risks, identifying a specific set of diagnosis codes that are significant predictors of readmission within 30 days.

RESULTS

Through extensive benchmarking and the application of a variety of feature engineering techniques, we successfully improved the area under the curve (AUROC) score from 0.628 to 0.7 across our models on the test dataset. We also revealed that specific diagnoses, including cancer, COPD, and certain social factors, are significant predictors of 30-day readmission risk. Conversely, bacterial carrier status appeared to have minimal impact due to lower case frequencies.

CONCLUSIONS

Our study demonstrates how we effectively utilize routinely collected hospital data to forecast patient readmission through the use of conventional machine learning while applying explainable AI techniques to explore the correlation between data features and patient readmission rate.

Multimodal Machine Learning for Prediction of 30-Day Readmission Risk in Elderly Population

BACKGROUND

Readmission within 30 days is a prevalent issue among elderly patients, linked to unfavorable health outcomes. Our objective was to develop and validate multimodal machine learning models for predicting 30-day readmission risk in elderly patients discharged from internal medicine departments.

METHODS

This was a retrospective cohort study which included elderly patients aged 75 or older, who were hospitalized at the Hadassah Medical Center internal medicine departments between 2014 and 2020. Three machine learning algorithms were developed and employed to predict 30-day readmission risk. The primary measures were predictive model performance scores, specifically area under the receiver operator curve (AUROC), and average precision.

RESULTS

This study included 19,569 admissions. Of them, 3258 (16.65%) resulted in 30-day readmission. Our 3 proposed models demonstrated high accuracy and precision on an unseen test set, with AUROC values of 0.87, 0.89, and 0.93, respectively, and average precision values of 0.76, 0.78, and 0.81. Feature importance analysis revealed that the number of admissions in the past year, history of 30-day readmission, Charlson score, and admission length were the most influential variables. Notably, the natural language processing score, representing the probability of readmission according to a textual-based model trained on social workers' assessment letters during hospitalization, ranked among the top 10 contributing factors.

CONCLUSIONS

Leveraging multimodal machine learning offers a promising strategy for identifying elderly patients who are at high risk for 30-day readmission. By identifying these patients, machine learning models may facilitate the effective execution of preventive actions to reduce avoidable readmission incidents.

Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques

BACKGROUND

The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet mental health needs. Accurate ED revisit prediction could aid early intervention and ensure efficient healthcare resource allocation. We examine the potential increased accuracy and performance of graph neural network (GNN) machine learning models compared to recurrent neural network (RNN), and baseline conventional machine learning and regression models for predicting ED revisit in electronic health record (EHR) data.

METHODS

This study used EHR data for children and youth aged 4-17 seeking services at McMaster Children's Hospital's Child and Youth Mental Health Program outpatient service to develop and evaluate GNN and RNN models to predict whether a child/youth with an ED visit had an ED revisit within 30 days. GNN and RNN models were developed and compared against conventional baseline models. Model performance for GNN, RNN, XGBoost, decision tree and logistic regression models was evaluated using F1 scores.

RESULTS

The GNN model outperformed the RNN model by an F1-score increase of 0.0511 and the best performing conventional machine learning model by an F1-score increase of 0.0470. Precision, recall, receiver operating characteristic (ROC) curves, and positive and negative predictive values showed that the GNN model performed the best, and the RNN model performed similarly to the XGBoost model. Performance increases were most noticeable for recall and negative predictive value than for precision and positive predictive value.

CONCLUSIONS

This study demonstrates the improved accuracy and potential utility of GNN models in predicting ED revisits among children and youth, although model performance may not be sufficient for clinical implementation. Given the improvements in recall and negative predictive value, GNN models should be further explored to develop algorithms that can inform clinical decision-making in ways that facilitate targeted interventions, optimize resource allocation, and improve outcomes for children and youth.

Risk Prediction of Diabetic Foot Amputation Using Machine Learning and Explainable Artificial Intelligence

BACKGROUND

Diabetic foot ulcers (DFUs) are serious complications of diabetes which can lead to lower extremity amputations (LEAs). Risk prediction models can identify high-risk patients who can benefit from early intervention. Machine learning (ML) methods have shown promising utility in medical applications. Explainable modeling can help its integration and acceptance. This study aims to develop a risk prediction model using ML algorithms with explainability for LEA in DFU patients.

METHODS

This study is a retrospective review of 2559 inpatient DFU episodes in a tertiary institution from 2012 to 2017. Fifty-one features including patient demographics, comorbidities, medication, wound characteristics, and laboratory results were reviewed. Outcome measures were the risk of major LEA, minor LEA and any LEA. Machine learning models were developed for each outcome, with model performance evaluated using receiver operating characteristic (ROC) curves, balanced-accuracy and F1-score. SHapley Additive exPlanations (SHAP) was applied to interpret the model for explainability.

RESULTS

Model performance for prediction of major, minor, and any LEA event achieved ROC of 0.820, 0.637, and 0.756, respectively, with XGBoost, XGBoost, and Gradient Boosted Trees algorithms demonstrating best results for each model, respectively. Using SHAP, key features that contributed to the predictions were identified for explainability. Total white cell (TWC) count, comorbidity score and red blood cell count contributed highest weightage to major LEA event. Total white cell, eosinophils, and necrotic eschar in the wound contributed most to any LEA event.

CONCLUSIONS

Machine learning algorithms performed well in predicting the risk of LEA in a patient with DFU. Explainability can help provide clinical insights and identify at-risk patients for early intervention.

Deep Clustering of Electronic Health Records Tabular Data for Clinical Interpretation

Machine learning applications are widespread due to straightforward supervised learning of known data labels. Many data samples in real-world scenarios, including medicine, are unlabeled because data annotation can be time-consuming and error-prone. The application and evaluation of unsupervised clustering methods are not trivial and are limited to traditional methods (e.g., k-means) when clinicians demand deeper insights into patient data beyond classification accuracy. The contribution of this paper is three-fold: 1) to introduce a patient stratification strategy based on a clinical variable instead of a diagnostic label, 2) to evaluate clustering performance using within-cluster homogeneity and between-cluster statistical difference, and 3) to compare widely used traditional clustering algorithms (e.g., k-means) with a state-of-the-art deep learning solution for clustering tabular data. The deep clustering method achieves superior within-cluster homogeneity and between-cluster separation compared to k-means and identifies three statistically distinct and clinically interpretable high blood pressure patient clusters. The proposed clustering strategy and evaluation metrics will facilitate the stratification of large patient cohorts in health science research without requiring explicit diagnostic labels.

Single-label machine learning classification revealed some hidden but inter-related causes of five psychotic disorder diseases

Psychotic disorder diseases (PDD) or mental illnesses are group of illnesses that affect the minds and impair the cognitive ability, retard emotional ability and obstruct the process of communication and relationship with others and are characterized by delusions, hallucinations and disoriented or disordered pattern of thinking. Prognosis of PDD is not sufficient because of the nature of the diseases and as such adequate form of diagnosis is required to detect, manage and treat the illness. This paper applied the single-label classification (SLC) machine learning approach in mining of electronic health records of people with PDD in Nigeria using eleven independent (demographic) variables and five PDD as target variables. The five PDDs are Insomnia, Schizophrenia, Minimal Brain dysfunction (MBD), which is also known as Attention-Deficit/Hyperactivity Disorder (ADHD), Vascular Dementia (VD) and Bipolar Disorder (BD). The aim of using SLC is that it would be easier to detect some PDDs that are related to each other without the loss of information, which is a plus over multi-label classification (MLC). ReliefF algorithm was used at each experiment to precipitate the order of importance of the independent variables and redundant variables were excluded from the analysis. The order of the variables in feature selection was matched with feature importance after the classifications and quantified using the Spearman rank correlation coefficient. The data was divided into: 70% for training and 30% for testing. Four new performance metrics adapted from the root mean square (RMSE) were proposed and used to measure the differences between the performance results of the 10 Machine learning models in terms of the training and testing and secondly, feature and without feature selection. The new metrics are close to zero which is an indication that the use of feature selection and cross validation may not greatly affects the accuracy of the SLC. When the PDDs are included as predictors for classifying others, there was a tremendous improvement as revealed by the four new metrics for classification accuracy (CA), precision and recall. Analysis of variance showed the four different metrics differs significantly for classification accuracy (CA) and precision. However, there were no significant difference between the CA and precision when the duo are compared together across the four evaluation metrics at p value less than 0.05.