Best practices for portfolio optimization by quantum computing, experimented on real quantum devices.

In finance, portfolio optimization aims at finding optimal investments maximizing a trade-off between return and risks, given some constraints. Classical formulations of this quadratic optimization problem have exact or heuristic solutions, but the complexity scales up as the market dimension increases. Recently, researchers are evaluating the possibility of facing the complexity scaling issue by employing quantum computing.

A Novel COVID-19 Data Set and an Effective Deep Learning Approach for the De-Identification of Italian Medical Records.

In the last years, the need to de-identify privacy-sensitive information within Electronic Health Records (EHRs) has become increasingly felt and extremely relevant to encourage the sharing and publication of their content in accordance with the restrictions imposed by both national and supranational privacy authorities. In the field of Natural Language Processing (NLP), several deep learning techniques for Named Entity Recognition (NER) have been applied to face this issue, significantly improving the effectiveness in identifying sensitive information in EHRs written in English. However, the lack of data sets in other languages has strongly limited their applicability and performance evaluation.

Indoor Radon Concentration and Risk Assessment in 27 Districts of a Public Healthcare Company in Naples, South Italy.

Radon is a major source of ionizing radiation exposure for the general population. It is known that exposure to radon is a risk factor for the onset of lung cancer. In this study, the results of a radon survey conducted in all districts of a Public Healthcare in Italy, are reported.

Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set.

The COrona VIrus Disease 19 (COVID-19) pandemic required the work of all global experts to tackle it. Despite the abundance of new studies, privacy laws prevent their dissemination for medical investigations: through clinical de-identification, the Protected Health Information (PHI) contained therein can be anonymized so that medical records can be shared and published. The automation of clinical de-identification through deep learning techniques has proven to be less effective for languages other than English due to the scarcity of data sets.

Subject identification via ECG fiducial-based systems: influence of the type of QT interval correction.

Electrocardiography (ECG) has been recently proposed as biometric trait for identification purposes. Intra-individual variations of ECG might affect identification performance. These variations are mainly due to Heart Rate Variability (HRV).

A study on textual features for medical records classification.

Healthcare domain is characterized by a huge amount of data, contained in medical records, reports, test results and so on. In order to give support to healthcare workers and manage relevant data in effective and efficient way, it is important to correctly classify the unstructured parts of text, embedded in the medical documents. In this paper, we propose a classification system for medical records categorization, focused on the combination of different methodologies, based on lexical, syntactical and semantic analysis of the documents.

Lifestyle educational program strongly increases compliance to nonpharmacologic intervention in hypertensive patients: a 2-year follow-up study.

The authors investigated the efficacy of a lifestyle educational program, organized in small group meetings, in improving the outcome of a nonpharmacologic intervention. One hundred and eighty-eight hypertensive patients with stable blood pressure (BP) levels and drug therapy in the previous 6 months were randomly divided into educational care (EC) and usual care (UC) groups. They were followed at 3-month intervals up to 2 years.