Interpretable time-aware and co-occurrence-aware network for medical prediction

Background Disease prediction based on electronic health records (EHRs) is essential for personalized healthcare. But it’s hard due to the special data structure and the interpretability requirement of methods. The structure of EHR is hierarchical: each patient has a sequence of admissions, and each admission has some co-occurrence diagnoses. However, the existing methods only partially model these characteristics and lack the interpretation for non-specialists. Methods This work proposes a time-aware and co-occurrence-aware deep learning network (TCoN), which is not only suitable for EHR data structure but also interpretable: the co-occurrence-aware self-attention (CS-attention) mechanism and time-aware gated recurrent unit (T-GRU) can model multilevel relations; the interpretation path and the diagnosis graph can make the result interpretable. Results The method is tested on a real-world dataset for mortality prediction, readmission prediction, disease prediction, and next diagnoses prediction. Experimental results show that TCoN is better than baselines with 2.01% higher accuracy. Meanwhile, the method can give the interpretation of causal relationships and the diagnosis graph of each patient. Conclusions This work proposes a novel model—TCoN. It is an interpretable and effective deep learning method, that can model the hierarchical medical structure and predict medical events. The experiments show that it outperforms all state-of-the-art methods. Future work can apply the graph embedding technology based on more knowledge data such as doctor notes.

DEVELOPMENT OF A SUBSYSTEM FOR DIAGNOSTICS OF GYNECOLOGICAL DISEASES BASED ON STATISTICAL AND SIMULATION MODELING

В статье рассматриваются методы кластерного и дискриминантного анализа для построения математических моделей диагностики гинекологических заболеваний. Гинекологические патологии занимают значительное место в структуре заболеваемости у женщин. Между тем, точная дифференциальная диагностика патологий зачастую бывает, невозможна, так как гинекологические заболевания носят вероятностный характер, большинство диагностических признаков выражаются качественными показателями, которые индивидуальны для каждой пациентки. Лечащему врачу приходится решать сложную задачу по анализу клинических, лабораторных и инструментальных признаков для постановки точного диагноза. С применением аппарата сетей Петри произведено построения модели дифференциальной диагностики гинекологических заболеваний. На основе полученных математических моделей, сформирована структура и информационно-программное обеспечение для системы диагностики гинекологических заболеваний в медицинских организациях. Внедрение разработанного программного продукта в медицинскую структуру позволит уменьшить вероятность врачебной ошибки, а также повысить эффективность и точность постановки диагноза пациенткам The article discusses the methods of cluster and discriminant analysis for constructing mathematical models for the diagnosis of gynecological diseases. Gynecological pathologies occupy a significant place in the structure of morbidity in women. Meanwhile, accurate differential diagnosis of pathologies is often impossible, since gynecological diseases are of a probabilistic nature, most of the diagnostic signs are expressed in qualitative indicators that are individual for each patient. The attending physician has to solve a complex task of analyzing clinical, laboratory and instrumental signs to make an accurate diagnosis. Using the apparatus of Petri nets, a model for the differential diagnosis of gynecological diseases was constructed. On the basis of the obtained mathematical models, the structure and information software for the system of diagnostics of gynecological diseases in medical organizations was formed. The introduction of the developed software product into the medical structure will reduce the likelihood of medical error, as well as increase the efficiency and accuracy of diagnosing patients

Establishing a COVID-19 treatment centre in Israel at the initial stage of the outbreak: challenges, responses and lessons learned

Anticipating the need for a COVID-19 treatment centre in Israel, a designated facility was established at Sheba Medical Center—a quaternary referral centre. The goals were diagnosis and treatment of patients with COVID-19 while protecting patients and staff from infection and ensuring operational continuity and treatment of patients with non-COVID. Options considered included adaptation of existing wards, building a tented facility and converting a non-medical structure. The option chosen was a non-medical structure converted to a hospitalisation facility suited for COVID-19 with appropriate logistic and organisational adaptations. Operational principles included patient isolation, unidirectional workflow from clean to contaminated zones and minimising direct contact between patients and caregivers using personal protection equipment (PPE) and a multimodal telemedicine system. The ED was modified to enable triage and treatment of patients with COVID-19 while maintaining a COVID-19-free environment in the main campus. This system enabled treatment of patients with COVID-19 while maintaining staff safety and conserving the operational continuity and the ability to continue delivery of treatment to patients with non-COVID-19.

Information that (de)motivate women’s decision making on Planned Home Birth

ABSTRACT Objective: To understand how information about Planned Home Birth motivates or discourages women’s decisions on this location of birth. Method: Descriptive exploratory study, qualitative approach. Data collection carried out from February to April 2019, through semi-structured interviews with 14 women and documentary sources. The data were analyzed using Bardin’s content analysis process, with the help of ATLAS.ti 8.0. Results: The motivations for choosing Planned Home Birth are: respect for the autonomy and natural process of childbirth and delivery, support from a partner and trust in professionals. Aspects that discourage this choice are fear of complications, the need for a hospital medical structure, opinions that value risk. Conclusion: Women’s choices are based not only on information, but also on how that information is processed. This study demonstrated that the perception pertaining to the safety of Planned Home Birth is essential for making this decision.

New Visions, New Interpretations of Identity

By mid-December 1945, the U.S. Navy incorporated Okinawans into the administration of military government. As the Okinawans assumed their new role, the Navy constructed a feasible and sustainable local government structure dependant on Okinawan custom and participation. Okinawans, serving in positions of administrative influence, demonstrated their ability to govern and the power of their leadership. Seaman, no longer under the stress and fear of combat conditions, formed both formal and informal relationships with the Okinawans within the context of their duties. Through such close interactions, Naval troops reassessed Okinawan identity as not only separate from Japan, but also free from congenial comparisons with America. The Nay devised military government policy that led to Okinawan influence in government, medical structure, education, and crime management.

Polyp Shape Recovery from Single Endoscope Image using Medical Suture

Background:Polyp shapes play an important role in colorectal diagnosis. However, endoscopy images are usually composed of nonrigid objects such as a polyp. Hence, it is challenging for polyp shape recovery. It is demanded to establish a support system of the colorectal diagnosis system based on polyp shape.Introduction:Shape from Shading (SFS) is one valuable approach based on photoclinometry for polyp shape recovery. SFS and endoscope image are compatible on the first sight, but there are constraints for applying SFS to endoscope image. Those approaches need some parameters like a depth from the endoscope lens to the surface, and surface reflectance factor . Furthermore, those approaches assume the whole surface which has the same value of for the Lambertian surface.Methods:This paper contributes to mitigating constraint for applying SFS to the endoscope image based on a cue from the medical structure. An extracted medical suture is used to estimate parameters, and a method of polyp shape recovery method is proposed using both geometric and photometric constraint equations. Notably, the proposed method realizes polyp shape recovery from a single endoscope image.Results:From experiments it was confirmed that the approximate polyp model shape was recovered and the proposed method recovered absolute size and shape of polyp using medical suture information and obtained parameters from a single endoscope image.Conclusion:This paper proposed a polyp shape recovery method which mitigated the constraint for applying SFS to the endoscope image using the medical suture. Notably, the proposed method realized polyp shape recovery from a single endoscope image without generating uniform Lambertian reflectance.