Domenico Conforti

Designing robust emergency medical service via stochastic programming

Abstract This paper addresses the problem of designing robust emergency medical services. Under this respect, the main issue to consider is the inherent uncertainty which characterizes real life situations. Several approaches can be used to design robust mathematical models which are able to hedge uncertain conditions. We are using here the stochastic programming framework and, in particular, the probabilistic paradigm. More specifically, we develop a stochastic programming model with probabilistic constraints aimed to solve both the location and the dimensioning problems, i.e. where service sites must be located and how many emergency vehicles must be assigned to each site, in order to achieve a reliable level of service and minimize the overall costs. In doing so, we consider the randomness of the system as far as the demand of emergency service is concerned. The numerical results, which have been collected on a large set of test problems, demonstrate the validity of the proposed model, particularly in dealing with the trade-off between quality of service and costs management.

Optimization models for radiotherapy patient scheduling

The efficient radiotherapy patient scheduling, within oncology departments, plays a crucial role in order to ensure the delivery of the right treatment at the right time. In this context, generating a high quality solution is a challenging task, since different goals (i.e., all the activities are scheduled as soon as possible, the patient waiting time is minimized, the device utilization is maximized) could be achieved and a large set of constraints (i.e., every device can be used by only one patient at time, the treatments have to be performed in an exact time order) should be taken into account. We propose novel optimization models dealing with the efficient outpatient scheduling within a radiotherapy department defined in such a way to represent different real-life situations. The effectiveness of the proposed models is evaluated on randomly generated problems and on a real case situation. The results are very encouraging since the developed optimization models allow to overcome the performance of human experts (i.e., the number of patients that begin the radiotherapy treatment is maximized).

Non-block scheduling with priority for radiotherapy treatments

In this paper, a quite challenging operational problem within health care delivery has been considered: the optimal management of patients waiting for radiotherapy treatments. Long waiting times for radiotherapy treatments of several cancers are largely documented all over the world. This problem is mainly due to an imbalance between supply and demand of radiotherapy services, which negatively affects the effectiveness and the efficiency of the health care delivered. Within this context, the paper presents an innovative solution approach for effectively scheduling a set of patients waiting to start the radiotherapy plan. The proposed approach is based on a well tailored integer linear optimization program, modelling a non-block scheduling strategy, with the aim to minimize the mean waiting time or maximize the number of new scheduled patients. The model has been tested and evaluated by carrying out some numerical experiments on suitable use-case scenarios, and the obtained results demonstrate the effectiveness and reliability of the proposed approach.

High frequency ultrasound measurement of digital dermal thickness in systemic sclerosis

Background Currently, assessment of dermal thickness in systemic sclerosis (SSc) is performed by palpation and assessment using the modified Rodnan skin score (mRSS). Objective To verify whether high frequency ultrasound (US) may be a reliable and a reproducible method to measure digital dermal thickness. Methods In 70 patients with SSc, skin thickness was evaluated with US by 2 observers at 2 different sites on the second digit of the dominant limb to determine the interobserver variability. Patients and controls were examined twice by the first observer for intraobserver variability. Patients were divided into three subgroups according to the phase of the disease (oedematous, fibrotic or atrophic). Results At both examined areas, US showed a significant dermal thickening (p<0.001) in the whole group of patients with SSc. A low intraobserver and interobserver variability was found. A highly significant correlation between the global mRSS and the local dermal thickness at the two examined sites (p=0.032, p=0.021) was detected. Skin thickness was significantly higher in the oedematous than in the fibrotic group (p<0.001) and significantly higher in the fibrotic and the oedematous group (p<0.001) than in the atrophic group (p<0.002). Conclusions US is a reliable tool giving reproducible results, and is able to detect digital dermal thickening in SSc.

Kernel based support vector machine via semidefinite programming: Application to medical diagnosis

Support vector machine (SVM) is a well sound learning method and a robust classification procedure. Choosing a suitable kernel function in SVM is crucial for obtaining good performance; the difficulty is how to choose a suitable data transformation for the given problem. To this end, multiple kernel matrices, each of them corresponding to a given similarity measure, can be linearly combined. In this paper, the optimal kernel matrix, obtained as linear combination of known kernel matrices, is generated using a semidefinite programming approach. A suitable model formulation assures that the obtained kernel matrix is positive semidefinite and is optimal with respect to the dataset under consideration. The proposed approach has been applied to some very important medical diagnostic decision making problems and the results obtained by carrying out preliminary numerical experiments demonstrated the effectiveness of the proposed solution approach.

A two-stage stochastic programming model for electric energy producers

The bilateral contract selection and bids definition constitute a strategic issue for electric energy producers that operate in competitive markets, as the liberalized electricity ones. In this paper we propose a two-stage stochastic integer programming model for the integrated optimization of power production and trading which include a specific measure accounting for risk management. We solve the model by means of a novel enumerative solution approach that exploits the particular problem structure. Finally, we report some preliminary computational experiments.

An optimal decision making model for supporting week hospital management

Week Hospital is an innovative inpatient health care organization and management, by which hospital stay services are planned in advance and delivered on week-time basis to elective patients. In this context, a strategic decision is the optimal clinical management of patients, and, in particular, devising efficient and effective admission and scheduling procedures, by tackling different requirements such as beds’ availability, diagnostic resources, and treatment capabilities. The main aim is to maximize the patient flow, by ensuring the delivery of all clinical services during the week. In this paper, the optimal management of Week Hospital patients is considered. We have developed and validated an innovative integer programming model, based on clinical resources allocation and beds utilization. In particular, the model aims at scheduling Week Hospital patients’ admission/discharge, possibly reducing the length of stay on the basis of an available timetable of clinical services. The performance of the model has been evaluated, in terms of efficiency and robustness, by considering real data coming from a Week Hospital Rheumatology Division. The experimental results have been satisfactory and demonstrate the effectiveness of the proposed approach.

Constrained auction clearing in the Italian electricity market

Abstract.Most of the liberalized electricity systems use the auction as a market model. The complexity of the underlying optimization formulation depends on the technical and regulatory constraints that must be considered. In Italy, the auction clearing should include not only congestion management limitations, but also a challenging regulatory constraint imposing that, while the zonal prices are allowed on the selling side, a uniform purchasing price has to be applied for all the zones of the Italian system. Such constraint introduces several complexities such as non-linearity and integrality. In this paper we discuss the modeling issues arising in the Italian context and we propose, in addition, a mechanism for the priority management of the offers/bids acceptance. We test the behavior of the models developed on a set of problems that represent all the possible scenarios that can be met in practice. The numerical results demonstrate the validity and the effectiveness of the proposed models.

Personal Interaction in the Vegetative State

Background and purpose: Brain processing at varying levels of functional complexity and emotional reactions to relatives are anecdotally reported by the caregivers of patients in a vegetative state. In this study, computer-assisted machine-learning procedures were applied to identify heart rate variability changes or galvanic skin responses to a relative’s presence. Methods: The skin conductance (galvanic skin response) and heart beats were continuously recorded in 12 patients in a vegetative state, at rest (baseline) and while approached by a relative (usually the mother; test condition) or by a nonfamiliar person (control condition). The cardiotachogram (the series of consecutive intervals between heart beats) was analyzed in the time and frequency domains by computing the parametric and nonparametric frequency spectra. A machine-learning algorithm was applied to sort out the significant spectral parameter(s). For all patients, each condition (baseline, test, control) was characterized by the values of ...

Heart Rate Response to Music An Artificial Intelligence Study on Healthy and Traumatic Brain-Injured Subjects

Background and rationale: Investigation of the brain’s emotional response to music is limited by methodological problems mainly related to the characterization of the emotions and concomitant brain conditions. In this study, artificial intelligence procedures were applied to identify significant music-induced changes in heart rate variability and to classify autonomic reactions to stimuli requiring complex brain operations. Both healthy subjects and traumatic brain-injury (TBI) patients were studied in order to test the method’s validity. Methods: 16 TBI patents and 26 healthy subjects were requested to listen to selected music samples while the heart beat was continuously recorded. The parametric and nonparametric frequency spectra were computed on the heart rate and the spectra descriptors were entered into a 1-R rules (very simple classification rules) data-mining procedure. Data-mining procedures independently classified the heart-rate spectral patterns and the emotions reported by subjects as positiv...