Doctor-patient confidentiality - right and duty of a doctor in law regulations

Physician’s professional secrecy is one of the most important duties of a doctor and should be provided with confidentiality regarding his or her health. Generally speaking, there is no legal definition of "physician’s professional secrecy" in Poland, although this concept already appears in the oath of Hippocrates: ‘I will keep secret anything I see or hear professionally which ought not to be told’. The issue of medical confidentiality (physician’s professional secrecy) has been regulated in several legal acts such as: The Patient Rights and Patients Ombudsman Act, The Constitution of the Republic of Poland, The Medical Profession Act, The Civil Code Act, The Criminal Code Act and Code of Medical Ethics which is not considered as a legal act. The patient has the right to require confidentiality of the information concerning him and the obligation to keep medical confidentiality will apply to every representative of the medical profession, who obtained certain information by various professional activities

A novel ensemble learning approach to unsupervised record linkage

© 2017 Record linkage is a process of identifying records that refer to the same real-world entity. Many existing approaches to record linkage apply supervised machine learning techniques to generate a classification model that classifies a pair of records as either match or non-match. The main requirement of such an approach is a labelled training dataset. In many real-world applications no labelled dataset is available hence manual labelling is required to create a sufficiently sized training dataset for a supervised machine learning algorithm. Semi-supervised machine learning techniques, such as self-learning or active learning, which require only a small manually labelled training dataset have been applied to record linkage. These techniques reduce the requirement on the manual labelling of the training dataset. However, they have yet to achieve a level of accuracy similar to that of supervised learning techniques. In this paper we propose a new approach to unsupervised record linkage based on a combination of ensemble learning and enhanced automatic self-learning. In the proposed approach an ensemble of automatic self-learning models is generated with different similarity measure schemes. In order to further improve the automatic self-learning process we incorporate field weighting into the automatic seed selection for each of the self-learning models. We propose an unsupervised diversity measure to ensure that there is high diversity among the selected self-learning models. Finally, we propose to use the contribution ratios of self-learning models to remove those with poor accuracy from the ensemble. We have evaluated our approach on 4 publicly available datasets which are commonly used in the record linkage community. Our experimental results show that our proposed approach has advantages over the state-of-the-art semi-supervised and unsupervised record linkage techniques. In 3 out of 4 datasets it also achieves comparable results to those of the supervised approaches

Privacy preserving record linkage in the presence of missing values

© 2017 The problem of record linkage is to identify records from two datasets, which refer to the same entities (e.g. patients). A particular issue of record linkage is the presence of missing values in records, which has not been fully addressed. Another issue is how privacy and confidentiality can be preserved in the process of record linkage. In this paper, we propose an approach for privacy preserving record linkage in the presence of missing values. For any missing value in a record, our approach imputes the similarity measure between the missing value and the value of the corresponding field in any of the possible matching records from another dataset. We use the k-NNs (k Nearest Neighbours in the same dataset) of the record with the missing value and their distances to the record for similarity imputation. For privacy preservation, our approach uses the Bloom filter protocol in the settings of both standard privacy preserving record linkage without missing values and privacy preserving record linkage with missing values. We have conducted an experimental evaluation using three pairs of synthetic datasets with different rates of missing values. Our experimental results show the effectiveness and efficiency of our proposed approach

Hotspot identification for Mapper graphs

Mapper algorithm can be used to build graph-based representations of high-dimensional data capturing structurally interesting features such as loops, flares or clusters. The graph can be further annotated with additional colouring of vertices allowing location of regions of special interest. For instance, in many applications, such as precision medicine, Mapper graph has been used to identify unknown compactly localized subareas within the dataset demonstrating unique or unusual behaviours. This task, performed so far by a researcher, can be automatized using hotspot analysis. In this work we propose a new algorithm for detecting hotspots in Mapper graphs. It allows automatizing of the hotspot detection process. We demonstrate the performance of the algorithm on a number of artificial and real world datasets. We further demonstrate how our algorithm can be used for the automatic selection of the Mapper lens functions.Comment: Topological Data Analysis and Beyond Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020

The gardener’s house – the form, the value, the state of behaviour (on the example of buildings from the area of the former rejencja opolska)

The article covers issues relating to the19th-century buildings which accompanied the park complex, called the gardener’s house. They presented the most important contemporary trends in the formation of residential-park complexes, as well as examples of preserved objects in the former rejencja opolska. It also presents the form, present values, and opportunities for adapting them

Definition of medical error and physicians’ interest in changes in the law

The main purpose of the paper is to discuss the definition of medical error. Moreover, the statistical analysis was aimed at demonstrating at what level is the legal knowledge of the professional group of doctors, in relation to the performed profession. The research group consisted of doctors of different specializations, of different ages, with diverse work experiences, performing their profession in the Lubelskie voivodeship. These were people working on the basis of contract of employment, civil law contracts or individual medical practice, employed in provincial hospitals, clinics, district hospitals, outpatient’s clinics, ambulances or medical centers. The author's questionnaire survey consisted of questions and answers for 298 doctors. Damage resulting from a widely understood medical error can be caused not only by the physician but by all medical staff or due to the organizational failure of the medical establishment. It must be stated with all conviction that the formulation of the concept of medical error is still ongoing and will evolve with the development of medicine as well as the law, which will strive to delineate the framework of its occurrence. The majority of doubts, which results from the obtained research, raises the issue of provision of medical help without the consent of the patient and the right to refuse treatment, but above all, what is quite surprising is the question of accepting gifts from pharmaceutical companies. The most frequently cited reasons influencing the lack of updating legal knowledge in the field of the performed profession were lack of time and a large number of duties, whereas the research group, in order to deepen their knowledge on that matter, most frequently used the Internet resources, industry articles, and on the third place was the training related to the subject matter of the medical law