Predictive modelling of hospital readmissions in diabetic patients clusters

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceDiabetes is a global public health problem with increasing incidence over the past 10 years. This disease's social and economic impacts are widely assessed worldwide, showing a direct and gradual decrease in the individual's ability to work, a gradual loss in the scale of quality of life and a burden on personal finances. The recurrence of hospitalisation is one of the most significant indexes in measuring the quality of care and the opportunity to optimise resources. Numerous techniques identify the patient who will need to be readmitted, such as LACE and HOSPITAL. The purpose of this study was to use a dataset related to the risk of hospital readmission in patients with Diabetes first to apply a clustering of subgroups by similarity. Then structures a predictive analysis with the main algorithms to identify the methodology of best performance. Numerous approaches were performed to prepare the dataset for these two interventions. The results found in the first phase were two clusters based on the total number of hospital recurrences and others on total administrative costs, with K=3. In the second phase, the best algorithm found was Neural Network 3, with a ROC of 0.68 and a misclassification rate of 0.37. When applied the same algorithm in the clusters, there were no gains in the confidence of the indexes, suggesting that there are no substantial gains in the division of subpopulations since the disease has the same behaviour and needs throughout its development

Contributions from computational intelligence to healthcare data processing

80 p.The increasing ability to gather, store and process health care information, through the electronic health records and improved communication methods opens the door for new applications intended to improve health care in many different ways. Crucial to this evolution is the development of new computational intelligence tools, related to machine learning and statistics. In this thesis we have dealt with two case studies involving health data. The first is the monitoring of children with respiratory diseases in the pediatric intensive care unit of a hospital. The alarm detection is stated as a classification problem predicting the triage selected by the nurse or medical doctor. The second is the prediction of readmissions leading to hospitalization in an emergency department of a hospital. Both problems have great impact in economic and personal well being. We have tackled them with a rigorous methodological approach, obtaining results that may lead to a real life implementation. We have taken special care in the treatment of the data imbalance. Finally we make propositions to bring these techniques to the clinical environment

Development and validation of a knowledge-based score to predict Fried's frailty phenotype across multiple settings using one-year hospital discharge data: The electronic frailty score

Background: Most claims-based frailty instruments have been designed for group stratification of older populations according to the risk of adverse health outcomes and not frailty itself. We aimed to develop and validate a tool based on one-year hospital discharge data for stratification on Fried's frailty phenotype (FP). Methods: We used a three-stage development/validation approach. First, we created a clinical knowledge-driven electronic frailty score (eFS) calculated as the number of deficient organs/systems among 18 critical ones identified from the International Statistical Classification of Diseases and Related Problems, 10th Revision (ICD-10) diagnoses coded in the year before FP assessment. Second, for eFS development and internal validation, we linked individual records from the Lc65+ cohort database to inpatient discharge data from Lausanne University Hospital (CHUV) for the period 2004-2015. The development/internal validation sample included community-dwelling, non-institutionalised residents of Lausanne (Switzerland) recruited in the Lc65+ cohort in three waves (2004, 2009, and 2014), aged 65-70 years at enrolment, and hospitalised at the CHUV at least once in the year preceding the FP assessment. Using this sample, we selected the best performing model for predicting the dichotomised FP, with the eFS or ICD-10-based variables as predictors. Third, we conducted an external validation using 2016 Swiss nationwide hospital discharge data and compared the performance of the eFS model in predicting 13 adverse outcomes to three models relying on well-designed and validated claims-based scores (Claims-based Frailty Index, Hospital Frailty Risk Score, Dr Foster Global Frailty Score). Findings: In the development/internal validation sample (n = 469), 14·3% of participants (n = 67) were frail. Among 34 models tested, the best-subsets logistic regression model with four predictors (age and sex at FP assessment, time since last hospital discharge, eFS) performed best in predicting the dichotomised FP (area under the curve=0·71; F1 score=0·39) and one-year adverse health outcomes. On the external validation sample (n = 54,815; 153 acute care hospitals), the eFS model demonstrated a similar performance to the three other claims-based scoring models. According to the eFS model, the external validation sample showed an estimated prevalence of 56·8% (n = 31,135) of frail older inpatients at admission. Interpretation: The eFS model is an inexpensive, transportable and valid tool allowing reliable group stratification and individual prioritisation for comprehensive frailty assessment and may be applied to both hospitalised and community-dwelling older adults. Funding: The study received no external funding

Prediction Of Heart Failure Decompensations Using Artificial Intelligence - Machine Learning Techniques

Los apartados 4.41, 4.4.2 y 4.4.3 del capítulo 4 están sujetos a confidencialidad por la autora. 203 p.Heart failure (HF) is a major concern in public health. Its total impact is increased by its high incidence and prevalence and its unfavourable medium-term prognosis. In addition, HF leads to huge health care resource consumption. Moreover, efforts to develop a deterministic understanding of rehospitalization have been difficult, as no specific patient or hospital factors have been shown to consistently predict 30-day readmission after hospitalization for HF.Taking all these facts into account, we wanted to develop a project to improve the assistance care of patients with HF. Up to know, we were using telemonitoring with a codification system that generated alarms depending on the received values. However, these simple rules generated large number of false alerts being, hence, not trustworthy. The final aims of this work are: (i) asses the benefits of remote patient telemonitoring (RPT), (ii) improve the results obtained with RPT using ML techniques, detecting which parameters measured by telemonitoring best predict HF decompensations and creating predictive models that will reduce false alerts and detect early decompensations that otherwise will lead to hospital admissions and (iii) determine the influence of environmental factors on HF decompensations.All in all, the conclusions of this study are:1. Asses the benefits of RPT: Telemonitoring has not shown a statistically significant reduction in the number of HF-related hospital admissions. Nevertheless, we have observed a statistically significant reduction in mortality in the intervention group with a considerable percentage of deaths from non-cardiovascular causes. Moreover, patients have considered the RPT programme as a tool that can help them in the control of their chronic disease and in the relationship with health professionals.2. Improve the results obtained with RPT using machine learning techniques: Significant weight increases, desaturation below 90%, perception of clinical worsening, including development of oedema, worsening of functional class and orthopnoea are good predictors of heart failure decompensation. In addition, machine learning techniques have improved the current alerts system implemented in our hospital. The system reduces the number of false alerts notably although it entails a decrement on sensitivity values. The best results are achieved with the predictive model built by applying NB with Bernoulli to the combination of telemonitoring alerts and questionnaire alerts (Weight + Ankle + well-being plus the yellow alerts of systolic blood pressure, diastolic blood pressure, O2Sat and heart rate). 3. Determine the influence of environmental factors on HF decompensations: Air temperature is the most significant environmental factor (negative correlation) in our study, although some other attributes, such as precipitation, are also relevant. This work also shows a consistent association between increasing levels SO2 and NOX air and HF hospitalizations

Prediction Of Heart Failure Decompensations Using Artificial Intelligence - Machine Learning Techniques

Los apartados 4.41, 4.4.2 y 4.4.3 del capítulo 4 están sujetos a confidencialidad por la autora. 203 p.Heart failure (HF) is a major concern in public health. Its total impact is increased by its high incidence and prevalence and its unfavourable medium-term prognosis. In addition, HF leads to huge health care resource consumption. Moreover, efforts to develop a deterministic understanding of rehospitalization have been difficult, as no specific patient or hospital factors have been shown to consistently predict 30-day readmission after hospitalization for HF.Taking all these facts into account, we wanted to develop a project to improve the assistance care of patients with HF. Up to know, we were using telemonitoring with a codification system that generated alarms depending on the received values. However, these simple rules generated large number of false alerts being, hence, not trustworthy. The final aims of this work are: (i) asses the benefits of remote patient telemonitoring (RPT), (ii) improve the results obtained with RPT using ML techniques, detecting which parameters measured by telemonitoring best predict HF decompensations and creating predictive models that will reduce false alerts and detect early decompensations that otherwise will lead to hospital admissions and (iii) determine the influence of environmental factors on HF decompensations.All in all, the conclusions of this study are:1. Asses the benefits of RPT: Telemonitoring has not shown a statistically significant reduction in the number of HF-related hospital admissions. Nevertheless, we have observed a statistically significant reduction in mortality in the intervention group with a considerable percentage of deaths from non-cardiovascular causes. Moreover, patients have considered the RPT programme as a tool that can help them in the control of their chronic disease and in the relationship with health professionals.2. Improve the results obtained with RPT using machine learning techniques: Significant weight increases, desaturation below 90%, perception of clinical worsening, including development of oedema, worsening of functional class and orthopnoea are good predictors of heart failure decompensation. In addition, machine learning techniques have improved the current alerts system implemented in our hospital. The system reduces the number of false alerts notably although it entails a decrement on sensitivity values. The best results are achieved with the predictive model built by applying NB with Bernoulli to the combination of telemonitoring alerts and questionnaire alerts (Weight + Ankle + well-being plus the yellow alerts of systolic blood pressure, diastolic blood pressure, O2Sat and heart rate). 3. Determine the influence of environmental factors on HF decompensations: Air temperature is the most significant environmental factor (negative correlation) in our study, although some other attributes, such as precipitation, are also relevant. This work also shows a consistent association between increasing levels SO2 and NOX air and HF hospitalizations

Developing a Tool to Support Decisions on Patient Prioritization at Admission to Home Health Care

Background and aims: Millions of Americans are discharged from hospitals to home health every year and about third of them return to hospitals. A significant number of rehospitalizations (up to 60%) happen within the first two weeks of services. Early targeted allocation of services for patients who need them the most, have the potential to decrease readmissions. Unfortunately, there is only fragmented evidence on factors that should be used to identify high-risk patients in home health. This dissertation study aimed to (1) identify factors associated with priority for the first home health nursing visit and (2) to construct and validate a decision support tool for patient prioritization. I recruited a geographically diverse convenience sample of nurses with expertise in care transitions and care coordination to identify factors supporting home health care prioritization. Methods: This was a predictive study of home health visit priority decisions made by 20 nurses for 519 older adults referred to home health. Variables included sociodemographics, diagnosis, comorbid conditions, adverse events, medications, hospitalization in last 6 months, length of stay, learning ability, self-rated health, depression, functional status, living arrangement, caregiver availability and ability and first home health visit priority decision. A combination of data mining and logistic regression models was used to construct and validate the final model. Results: The final model identified five factors associated with first home health visit priority. A cutpoint for decisions on low/medium versus high priority was derived with a sensitivity of 80% and specificity of 57.9%, area under receiver operator curve (ROC) 75.9%. Nurses were more likely to prioritize patients who had wounds (odds ratio [OR]=1.88), comorbid condition of depression (OR=1.73), limitation in current toileting status (OR= 2.02), higher numbers of medications (increase in OR for each medication =1.04) and comorbid conditions (increase in OR for each condition =1.04). Discussion: This dissertation study developed one of the first clinical decision support tools for home health, the PREVENT - Priority for Home Health Visit Tool. Further work is needed to increase the specificity and generalizability of the tool and to test its effects on patient outcomes

Using machine learning to predict individual severity estimates of alcohol withdrawal syndrome in patients with alcohol dependence

Despite its high prevalence in diverse clinical settings, treatment of alcohol withdrawal syndrome (AWS) is mainly based on subjective clinical opinion. Without reliable predictors of potential harmful AWS outcomes at the individual patient’s level, decisions like provision of pharmacotherapy rely on resource-intensive in-patient monitoring. By contrast, an accurate risk prognosis would enable timely preemptive treatment, open up possibilities for safe out-patient care and lead to a more efficient use of health care resources. The aim of this project was to develop such tools using clinical and patient-reported information easily attainable at patient’s admission. To this end, a machine learning framework incorporating nested cross-validation, ensemble learning, and external validation was developed to retrieve accurate, generalizable prediction models for three meaningful AWS outcomes: (1) Separating mild and more severe AWS as defined by the established AWS scale, and directly identifying patients at risk of (2) delirium tremens as well as (3) withdrawal seizures. Based on 121 sociodemographic, clinical and laboratory-based variables, that were retrieved retrospectively from the patients’ charts, this classification paradigm was used to build predictive models in two cohorts of AWS patients at major detoxification wards in Munich (Ludwig-Maximilian-Universität München, n=389; Technische Universität München, n=805). Moderate to severe AWS cases were predicted with significant balanced accuracy (BAC) in both cohorts (LMU, BAC = 69.4%; TU, BAC = 55.9%). A post-hoc association between the models’ poor outcome predictions and higher clomethiazole doses further added to their clinical validity. While delirium tremens cases were accurately identified in the TU cohort (BAC = 75%), the framework yielded no significant model for withdrawal seizures. Variable importance analyses revealed that predictive patterns highly varied between both treatment sites and withdrawal outcomes. Besides several previously described variables (most notably, low platelet count and cerebral brain lesions), several new predictors were identified (history of blood pressure abnormalities, positive urine-based benzodiazepine screening and years of schooling), emphasizing the utility of data-driven, hypothesis-free prediction approaches. Due to limitations of the datasets as well as site-specific patient characteristics, the models did not generalize across treatment sites, highlighting the need to conduct strict validation procedures before implementing prediction tools in clinical care. In conclusion, this dissertation provides evidence on the utility of machine learning methods to enable personalized risk predictions for AWS severity. More specifically, nested-cross validation and ensemble learning could be used to ensure generalizable, clinically applicable predictions in future prospective research based on multi-center collaboration.Die prädiktive Einschätzung der Ausprägung von Entzugssymptomen bei Patient*innen mit Alkoholabhängigkeit beruht trotz jahrzehntelanger wissenschaftlicher Bemühungen weiterhin auf subjektiver klinischer Einschätzung. Entgiftungsbehandlungen werden daher weltweit vorwiegend im stationären Rahmen durchgeführt, um eine engmaschige klinische Überwachung zu gewährleisten. Da über 90 % der Entzugssyndrome mit lediglich milder vegetativer Symptomatik verlaufen, bindet dieses Vorgehen wertvolle Ressourcen. Datenbasierte Prädiktionstools könnten einen wichtigen Beitrag in Richtung einer individualisierten, akkuraten und verlässlichen Verlaufsbeurteilung leisten. Diese würde sichere ambulante Behandlungskonzepte, prophylaktische medikamentöse Behandlungen von Risikopatient*innen, sowie innovative Behandlungsforschung basierend auf stratifizierten Risikogruppen ermöglichen. Das Ziel dieser Arbeit war die Entwicklung solcher prädiktiven Tools für Patient*innen mit Alkoholentzugssyndrom (AES). Hierfür wurde ein innovatives Machine Learning Paradigma unter Verwendung von strikten Validierungsmethoden (Nested Cross-Validation und Out-of-Sample External Validation) verwendet, um generalisierbare, akkurate Prädiktionsmodelle für drei bedeutsame klinische Endpunkte des AES zu entwickeln: (1) die Klassifikation von milden in Abgrenzung zu moderat bis schwer ausgeprägten AES Verläufen, definiert nach einer hierfür etablierten klinischen Skala (AES Skala), sowie die direkte Identifikation der Komplikationen (2) Delirium tremens (DT) sowie von (3) zerebralen Entzugsanfällen (WS). Dieses Paradigma wurde unter Verwendung von 121 retrospektiv erfassten klinischen, laborbasierten, sowie soziodemographischen Variablen auf 1194 Patient*innen mit Alkoholabhängigkeit an zwei großen Entgiftungsstationen in München angewandt (Ludwig-Maximilian-Universität München, n=389; Technische Universität München, n=805). Moderate bis schwere AES Verläufe konnten an beiden Behandlungszentren mit einer signifikanten Genauigkeit (balanced accuracy [BAC]) prädiziert werden (LMU, BAC = 69.4%; TU, BAC = 55.9%). In einer post-hoc Analyse war die Prädiktion moderater bis schwerer Verläufe zudem mit höheren kumulativen Clomethiazol-Dosen assoziiert, was für die klinische Validität der Modelle spricht. Während DT in der TU Kohorte mit einer hohen Genauigkeit (BAC = 75%) identifiziert werden konnte, war die Prädiktion von Entzugsanfällen nicht erfolgreich. Eine explorative Analyse konnte zeigen, dass die prädiktive Bedeutsamkeit einzelner Variable sowohl zwischen den Behandlungszentren als auch den einzelnen Endpunkten deutlich variierte. Neben mehreren bereits in früheren wissenschaftlichen Arbeiten beschriebenen prädiktiv wertvollen Variablen (insbesondere einer durchschnittlich niedrigeren Thrombozytenzahl im Blut sowie von strukturellen zerebralen Läsionen) konnten hierbei mehrere neue Prädiktoren identifiziert werden (Blutdruckauffälligkeiten in der Vorgeschichte; positives Urinscreening auf Benzodiazepine; Anzahl der Schuljahre). Diese Ergebnisse unterstreichen den Wert von datenbasierten, hypothesen-freien Prädiktionsansätzen. Aufgrund von Limitationen des retrospektiven Datensatzes, wie der fehlenden zentrumsübergreifenden Verfügbarkeit einiger Variablen, sowie klinischen Besonderheiten der beiden Kohorten, ließen sich die Modelle am jeweils anderen Behandlungszentrum nicht validieren. Dieses Ergebnis unterstreicht die Notwendigkeit, die Generalisierbarkeit von Prädiktionsergebnissen adäquat zu testen, bevor hierauf basierende Tools für die klinische Praxis empfohlen werden. Solche Methoden wurden im Rahmen dieser Arbeit erstmalig in einem Forschungsprojekt zum AES verwendet. Zusammenfassend, zeigen die Ergebnisse dieser Dissertation erstmalig einen Nutzen von Machine Learning Ansätzen zur individualisierten Risikoprädiktion schwerer AES Verläufe an. Das hierbei verwendete cross-validierte Machine Learning Paradigma wäre ein mögliches Analyseverfahren, um in künftigen prospektiven Multi-Center-Studien verlässliche Prädikationsergebnisse mit hohem klinischen Anwendungspotential zu erreichen

Studies on using data-driven decision support systems to improve personalized medicine processes

This dissertation looks at how new sources of information should be incorporated into medical decision-making processes to improve patient outcomes and reduce costs. There are three fundamental challenges that must be overcome to effectively use personalized medicine, we need to understand: 1) how best to appropriately designate which patients will receive the greatest value from these processes; 2) how physicians and caregivers interpret additional patient-specific information and how that affects their decision-making processes; and finally, (3) how to account for a patient’s ability to engage in their own healthcare decisions. The first study looks at how we can infer which patients will receive the most value from genomic testing. The difficult statistical problem is how to separate the distribution of patients, based on ex-ante factors, to identify the best candidates for personalized testing. A model was constructed to infer a healthcare provider’s decision on whether this test would provide beneficial information in selecting a patient’s medication. Model analysis shows that healthcare providers’ primary focus is to maximize patient health outcomes while considering the impact the patient’s economic welfare. The second study focuses on understanding how technology-enabled continuity of care (TECC) for Chronic Obstructive Pulmonary Disease (COPD) and Congestive Heart Failure (CHF) patients can be utilized to improve patient engagement, measured in terms of patient activation. We shed light on the fact that different types of patients garnered different levels of value from the use of TECC. The third study looks at how data-driven decision support systems can allow physicians to more accurately understand which patients are at high-risk of readmission. We look at how we can use available patient-specific information for patients admitted with CHF to more accurately identify which patients are most likely to be readmitted, and also why – whether for condition-related reasons versus for non- related reasons, allowing physicians to suggest different patient-specific readmission prevention strategies. Taken together, these three studies allow us to build a robust theory to tackle these challenges, both operational and policy-related, that need to be addressed for physicians to take advantage of the growing availability of patient-specific information to improve personalized medication processes

Improving Emergency Department Patient Flow Through Near Real-Time Analytics

ABSTRACT IMPROVING EMERGENCY DEPARTMENT PATIENT FLOW THROUGH NEAR REAL-TIME ANALYTICS This dissertation research investigates opportunities for developing effective decision support models that exploit near real-time (NRT) information to enhance the operational intelligence within hospital Emergency Departments (ED). Approaching from a systems engineering perspective, the study proposes a novel decision support framework for streamlining ED patient flow that employs machine learning, statistical and operations research methods to facilitate its operationalization. ED crowding has become the subject of significant public and academic attention, and it is known to cause a number of adverse outcomes to the patients, ED staff as well as hospital revenues. Despite many efforts to investigate the causes, consequences and interventions for ED overcrowding in the past two decades, scientific knowledge remains limited in regards to strategies and pragmatic approaches that actually improve patient flow in EDs. Motivated by the gaps in research, we develop a near real-time triage decision support system to reduce ED boarding and improve ED patient flow. The proposed system is a novel variant of a newsvendor modeling framework that integrates patient admission probability prediction within a proactive ward-bed reservation system to improve the effectiveness of bed coordination efforts and reduce boarding times for ED patients along with the resulting costs. Specifically, we propose a cost-sensitive bed reservation policy that recommends optimal bed reservation times for patients right during triage. The policy relies on classifiers that estimate the probability that the ED patient will be admitted using the patient information collected and readily available at triage or right after. The policy is cost-sensitive in that it accounts for costs associated with patient admission prediction misclassification as well as costs associated with incorrectly selecting the reservation time. To achieve the objective of this work, we also addressed two secondary objectives: first, development of models to predict the admission likelihood and target admission wards of ED patients; second, development of models to estimate length-of-stay (LOS) of ED patients. For the first secondary objective, we develop an algorithm that incorporates feature selection into a state-of-the-art and powerful probabilistic Bayesian classification method: multi-class relevance vector machine. For the second objective, we investigated the performance of hazard rate models (in particual, the non-parametric Cox proportional hazard model, parametric hazard rate models, as well as artificial neural networks for modeling the hazard rate) to estimate ED LOS by using the information that is available at triage or right after as the covariates in the models. The proposed models are tested using extensive historical data from several U.S. Department of Veterans Affairs Medical Centers (VAMCs) in the Mid-West. The Case Study using historical data from a VAMC demonstrates that applying the proposed framework leads to significant savings associated with reduced boarding times, in particular, for smaller wards with high levels of utilization. For theory, our primary contribution is the development of a cost sensitive ward-bed reservation model that effectively accounts for various costs and uncertainties. This work also contributes to the development of an integrated feature selection method for classification by developing and validating the mathematical derivation for feature selection during mRVM learning. Another contribution stems from investigating how much the ED LOS estimation can be improved by incorporating the information regarding ED orderable item lists. Overall, this work is a successful application of mixed methods of operation research, machine learning and statistics to the important domain of health care system efficiency improvement