Predicting diabetes-related hospitalizations based on electronic health records

OBJECTIVE: To derive a predictive model to identify patients likely to be hospitalized during the following year due to complications attributed to Type II diabetes. METHODS: A variety of supervised machine learning classification methods were tested and a new method that discovers hidden patient clusters in the positive class (hospitalized) was developed while, at the same time, sparse linear support vector machine classifiers were derived to separate positive samples from the negative ones (non-hospitalized). The convergence of the new method was established and theoretical guarantees were proved on how the classifiers it produces generalize to a test set not seen during training. RESULTS: The methods were tested on a large set of patients from the Boston Medical Center - the largest safety net hospital in New England. It is found that our new joint clustering/classification method achieves an accuracy of 89% (measured in terms of area under the ROC Curve) and yields informative clusters which can help interpret the classification results, thus increasing the trust of physicians to the algorithmic output and providing some guidance towards preventive measures. While it is possible to increase accuracy to 92% with other methods, this comes with increased computational cost and lack of interpretability. The analysis shows that even a modest probability of preventive actions being effective (more than 19%) suffices to generate significant hospital care savings. CONCLUSIONS: Predictive models are proposed that can help avert hospitalizations, improve health outcomes and drastically reduce hospital expenditures. The scope for savings is significant as it has been estimated that in the USA alone, about $5.8 billion are spent each year on diabetes-related hospitalizations that could be prevented.Accepted manuscrip

Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach

Urban living in modern large cities has significant adverse effects on health, increasing the risk of several chronic diseases. We focus on the two leading clusters of chronic disease, heart disease and diabetes, and develop data-driven methods to predict hospitalizations due to these conditions. We base these predictions on the patients' medical history, recent and more distant, as described in their Electronic Health Records (EHR). We formulate the prediction problem as a binary classification problem and consider a variety of machine learning methods, including kernelized and sparse Support Vector Machines (SVM), sparse logistic regression, and random forests. To strike a balance between accuracy and interpretability of the prediction, which is important in a medical setting, we propose two novel methods: K-LRT, a likelihood ratio test-based method, and a Joint Clustering and Classification (JCC) method which identifies hidden patient clusters and adapts classifiers to each cluster. We develop theoretical out-of-sample guarantees for the latter method. We validate our algorithms on large datasets from the Boston Medical Center, the largest safety-net hospital system in New England

Performance measurement with multiple interrelated variables and threshold target levels:evidence from retail firms in the US

In this study, we developed a DEA-based performance measurement methodology that is consistent with performance assessment frameworks such as the Balanced Scorecard. The methodology developed in this paper takes into account the direct or inverse relationships that may exist among the dimensions of performance to construct appropriate production frontiers. The production frontiers we obtained are deemed appropriate as they consist solely of firms with desirable levels for all dimensions of performance. These levels should be at least equal to the critical values set by decision makers. The properties and advantages of our methodology against competing methodologies are presented through an application to a real-world case study from retail firms operating in the US. A comparative analysis between the new methodology and existing methodologies explains the failure of the existing approaches to define appropriate production frontiers when directly or inversely related dimensions of performance are present and to express the interrelationships between the dimensions of performance

Multiple access in BPL networks according to IEEE 1901 standard- Simulation of TDMA operation

118 σ.Σκοπός της παρούσας διπλωματικής είναι η ανάλυση των σχημάτων πολλαπλής πρόσβασης σε δίκτυα ευρυζωνικής μετάδοσης πληροφορίας μέσω των γραμμών ηλεκτρικής ισχύος (τεχνολογία BPL- Broadband over Power Liners ή PLC- Power Line Communications) και η προσομοίωση δικτύου που λειτουργεί με βάση το σχήμα TDMA. Η BPL τεχνολογία είναι αναδυόμενη και αποτελεί μία επιχειρησιακή και επιχειρηματική δραστηριότητα που τώρα αρχίζει να αναπτύσσεται. Το ίδιο το δίκτυο ηλεκτρικής ενέργειας αποκτά τη δυνατότητα αυτοδιαχείρισης, ενώ παράλληλα οι εταιρίες ηλεκτρικής ενέργειας μπορούν να λειτουργήσουν και ως πάροχοι ευρυζωνικής πρόσβασης στο Διαδίκτυο σε κάθε είδους χρήστες με ό,τι αυτό συνεπάγεται. Σημαντικό βήμα ανάπτυξης της BPL τεχνολογίας ήταν η έκδοση του ΙΕΕΕ 1901 προτύπου το Δεκέμβριο του 2010, στη μελέτη του οποίου βασίζεται κυρίως η θεωρητική ανάλυση στη διπλωματική αυτή. Στο πρώτο κεφάλαιο γίνεται εισαγωγή στη BPL τεχνολογία και τις εφαρμογές της καθώς και αναφορά στο ερευνητικό πρόγραμμα υλοποίησης BPL δικτύου στη Λάρισα. Στο δεύτερο κεφάλαιο γίνεται περιγραφή του MAC στρώματος, λειτουργία του οποίου αποτελεί ο έλεγχος της πολλαπλής πρόσβασης των χρηστών στον κοινό BPL δίαυλο. Στο τρίτο κεφάλαιο εισάγονται οι όροι και τα στοιχεία οργάνωσης ενός BPL δικτύου σύμφωνα με το πρότυπο ΙΕΕΕ 1901, ενώ στο τέταρτο κεφάλαιο περιγράφεται αναλυτικά η διαδικασία πρόσβασης και ο έλεγχος αυτής καθώς και τα σχήματα πρόσβασης TDMA και CSMA/CA που χρησιμοποιούνται για πρόσβαση στο μέσο σε περιόδους χωρίς ανταγωνισμό (δεν συμβαίνουν συγκρούσεις, εξασφάλιση QoS) και με ανταγωνισμό (συμβαίνουν συγκρούσεις, υπηρεσίες βέλτιστης προσπάθειας) αντίστοιχα. Στο τελευταίο κεφάλαιο γίνεται με χρήση του δικτυακού προσομοιωτή OPNET προσομοίωση δικτύου που λειτουργεί με βάση το TDMA και συγκεκριμένα με χρήση σκυτάλης (token-based) και παρουσιάζονται τα αποτελέσματα των προσομοιώσεων τόσο για ένα βασικό σενάριο αναφοράς όσο και για σενάρια στα οποία μεταβάλλονται παράμετροι, όπως ο αριθμός των BPL μονάδων, ο αριθμός των χρηστών, η κατανομή των χρηστών, η τοποθεσία μίας κακής ζεύξης στο δίκτυο κ.α.The aim of this dissertation is to analyze multiple access schemes in broadband over power line transmission networks (BPL technology- Broadband over Power Liners or PLC technology- Power Line Communications) and simulate a networks which operates according to TDMA scheme. BPL is an emerginf technology and consists an operational and business activity that is starting to develop nowadays. The electric grid network itself can be self- managed, while at the same time the electric power providers can operate as Internet Service Providers for all type of users with what this may imply. An important step in the development of BPL technology was the issuance of ΙΕΕΕ 1901 standard in December 2010, on which the study in this dissertation was based. In the first section , BPL technology and its applications are introduced and the research program in BPL network implementation in Larissa is reviewed. In the second section, the MAC layer is described, whose operation constists of control of the medium access of users to the commol BPL channel. In the third section the terms and elements of the organization of a BPL network according to ΙΕΕΕ 1901 standard are introduced, while in the fourth section the access process and its control are described in detail. The TDMA and CSMA/CA multiple access schemes which are used for access to the medium in contention-free periods (no collisions occur, ensuring of QoS) and in contention periods (collisions occur, best effort services) respectively are also described. In the last section , with the use of OPNET network simulator, a TDMA and in particular a token-based network simulation is carried out and the results are presented for a benchmark reference scenario and also for scenario in which parameters such as the number of BPL units, the number of users, the distribution of users, the location of a bad link the network are changed.Θεοδώρα Σ. Μπρισίμ