Predicting Comorbid Conditions and Trajectories using Social Health Records

A dynamic probabilistic model of the onset and interaction of cardio-metabolic comorbidities on an ageing adult population

Comorbidity is widespread in the ageing population, implying multiple and complex medical needs for individuals and a public health burden. Determining risk factors and predicting comorbidity development can help identify at-risk subjects and design prevention strategies. Using socio-demographic and clinical data from approximately 11,000 subjects monitored over 11 years in the English Longitudinal Study of Ageing, we develop a dynamic Bayesian network (DBN) to model the onset and interaction of three cardio-metabolic comorbidities, namely type 2 diabetes (T2D), hypertension, and heart problems. The DBN allows us to identify risk factors for developing each morbidity, simulate ageing progression over time, and stratify the population based on the risk of outcome occurrence. By applying hierarchical agglomerative clustering to the simulated, dynamic risk of experiencing morbidities, we identified patients with similar risk patterns and the variables contributing to their discrimination. The network reveals a direct joint effect of biomarkers and lifestyle on outcomes over time, such as the impact of fasting glucose, HbA1c, and BMI on T2D development. Mediated cross-relationships between comorbidities also emerge, showcasing the interconnected nature of these health issues. The model presents good calibration and discrimination ability, particularly in predicting the onset of T2D (iAUC-ROC = 0.828, iAUC-PR = 0.294) and survival (iAUC-ROC = 0.827, iAUC-PR = 0.311). Stratification analysis unveils two distinct clusters for all comorbidities, effectively discriminated by variables like HbA1c for T2D and age at baseline for heart problems. The developed DBN constitutes an effective, highly-explainable predictive risk tool for simulating and stratifying the dynamic risk of developing cardio-metabolic comorbidities. Its use could help identify the effects of risk factors and develop health policies that prevent the occurrence of comorbidities.

Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study

Background

Diabetic mellitus (DM) and cardiovascular diseases (CVD) cause significant healthcare burden globally and often co-exists. Current approaches often fail to identify many people with co-occurrence of DM and CVD, leading to delay in healthcare seeking, increased complications and morbidity. In this paper, we aimed to develop and evaluate a two-stage machine learning (ML) model to predict the co-occurrence of DM and CVD.

Methods

We used the diabetes complications screening research initiative (DiScRi) dataset containing >200 variables from >2000 participants. In the first stage, we used two ML models (logistic regression and Evimp functions) implemented in multivariate adaptive regression splines model to infer the significant common risk factors for DM and CVD and applied the correlation matrix to reduce redundancy. In the second stage, we used classification and regression algorithm to develop our model. We evaluated the prediction models using prediction accuracy, sensitivity and specificity as performance metrics.

Results

Common risk factors for DM and CVD co-occurrence was family history of the diseases, gender, deep breathing heart rate change, lying to standing blood pressure change, HbA1c, HDL and TC\HDL ratio. The predictive model showed that the participants with HbA1c >6.45 and TC\HDL ratio > 5.5 were at risk of developing both diseases (97.9% probability). In contrast, participants with HbA1c >6.45 and TC\HDL ratio ≤ 5.5 were more likely to have only DM (84.5% probability) and those with HbA1c ≤5.45 and HDL >1.45 were likely to be healthy (82.4%. probability). Further, participants with HbA1c ≤5.45 and HDL <1.45 were at risk of only CVD (100% probability). The predictive accuracy of the ML model to detect co-occurrence of DM and CVD is 94.09%, sensitivity 93.5%, and specificity 95.8%.

Conclusions

Our ML model can significantly predict with high accuracy the co-occurrence of DM and CVD in people attending a screening program. This might help in early detection of patients with DM and CVD who could benefit from preventive treatment and reduce future healthcare burden.

Sequential Pattern Mining to Predict Medical In-Hospital Mortality from Administrative Data: Application to Acute Coronary Syndrome

Prediction of a medical outcome based on a trajectory of care has generated a lot of interest in medical research. In sequence prediction modeling, models based on machine learning (ML) techniques have proven their efficiency compared to other models. In addition, reducing model complexity is a challenge. Solutions have been proposed by introducing pattern mining techniques. Based on these results, we developed a new method to extract sets of relevant event sequences for medical events' prediction, applied to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). From the French Hospital Discharge Database, we mined sequential patterns. They were further integrated into several predictive models using a text string distance to measure the similarity between patients' patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. We obtained good results in terms of discrimination with the receiver operating characteristic curve scores ranging from 0.71 to 0.99 with a good overall accuracy. We demonstrated the interest of sequential patterns for event prediction. This could be a first step to a decision-support tool for the prevention of in-hospital death by ACS.

Long Short-Term Memory Recurrent Neural Networks for Multiple Diseases Risk Prediction by Leveraging Longitudinal Medical Records

Individuals suffer from chronic diseases without being identified in time, which brings lots of burden of disease to the society. This paper presents a multiple disease risk prediction method to systematically assess future disease risks for patients based on their longitudinal medical records. In this study, medical diagnoses based on International Classification of Diseases (ICD) are aggregated into different levels for prediction to meet the needs of different stakeholders. The proposed approach gets validated using two independent hospital medical datasets, which includes 7105 patients with 18, 893 patients and 4170 patients with 13, 124 visits, respectively. The initial analysis reveals a high variation in patients' characteristics. The study demonstrates that recurrent neural network with long-short time memory units performs well in different levels of diagnosis aggregation. Especially, the results show that the developed model can be well applied to predicting future disease risks for patients, with the exact-match score of 98.90% and 95.12% using 3-digit ICD code aggregation, while 96.60% and 96.83% using 4-digit ICD code aggregation for these two datasets, respectively. Moreover, the approach can be developed as a reference tool for hospital information systems, enhancing patients' healthcare management over time.

Mining comorbidity patterns using retrospective analysis of big collection of outpatient records

BACKGROUND

Studying comorbidities of disorders is important for detection and prevention. For discovering frequent patterns of diseases we can use retrospective analysis of population data, by filtering events with common properties and similar significance. Most frequent pattern mining methods do not consider contextual information about extracted patterns. Further data mining developments might enable more efficient applications in specific tasks like comorbidities identification.

METHODS

We propose a cascade data mining approach for frequent pattern mining enriched with context information, including a new algorithm MIxCO for maximal frequent patterns mining. Text mining tools extract entities from free text and deliver additional context attributes beyond the structured information about the patients.

RESULTS

The proposed approach was tested using pseudonymised reimbursement requests (outpatient records) submitted to the Bulgarian National Health Insurance Fund in 2010-2016 for more than 5 million citizens yearly. Experiments were run on 3 data collections. Some known comorbidities of Schizophrenia, Hyperprolactinemia and Diabetes Mellitus Type 2 are confirmed; novel hypotheses about stable comorbidities are generated. The evaluation shows that MIxCO is efficient for big dense datasets.

CONCLUSION

Explicating maximal frequent itemsets enables to build hypotheses concerning the relationships between the exogeneous and endogeneous factors triggering the formation of these sets. MixCO will help to identify risk groups of patients with a predisposition to develop socially-significant disorders like diabetes. This will turn static archives like the Diabetes Register in Bulgaria to a powerful alerting and predictive framework.

Analysis of free text in electronic health records for identification of cancer patient trajectories

With an aging patient population and increasing complexity in patient disease trajectories, physicians are often met with complex patient histories from which clinical decisions must be made. Due to the increasing rate of adverse events and hospitals facing financial penalties for readmission, there has never been a greater need to enforce evidence-led medical decision-making using available health care data. In the present work, we studied a cohort of 7,741 patients, of whom 4,080 were diagnosed with cancer, surgically treated at a University Hospital in the years 2004-2012. We have developed a methodology that allows disease trajectories of the cancer patients to be estimated from free text in electronic health records (EHRs). By using these disease trajectories, we predict 80% of patient events ahead in time. By control of confounders from 8326 quantified events, we identified 557 events that constitute high subsequent risks (risk > 20%), including six events for cancer and seven events for metastasis. We believe that the presented methodology and findings could be used to improve clinical decision support and personalize trajectories, thereby decreasing adverse events and optimizing cancer treatment.