Psychiatric Disorder in the General Hospital

1986 ◽  
Vol 149 (2) ◽  
pp. 172-190 ◽  
Author(s):  
Richard Mayou ◽  
Keith Hawton
Keyword(s):  
Psychiatric Disorder ◽  
General Hospital ◽  
Psychiatric Morbidity ◽  
Routine Clinical Practice ◽  
Standard Methods ◽  
Different Types ◽  
Patient Groups ◽  
Hospital Doctors ◽  
The Many

There have been many reports of psychiatric disorder in medical populations, but few have used standard methods on representative patient groups. Even so, there is consistent evidence for considerable psychiatric morbidity in in-patient, out-patient and casualty department populations, much of which is unrecognised by hospital doctors. We require a better classification of psychiatric disorder in the general hospital, improved research measures, and more evidence about the nature and course of the many different types of problem so that we can provide precise advice for their management of routine clinical practice.

scholarly journals The present state of consultation and liaison psychiatry

1990 ◽  
Vol 14 (6) ◽  
pp. 321-325 ◽  
Author(s):  
Richard Mayou ◽  
Helen Anderson ◽  
Charlotte Feinmann ◽  
Gail Hodgson ◽  
Peter L. Jenkins
Keyword(s):  
General Hospital ◽  
Psychiatric Care ◽  
Community Care ◽  
Psychiatric Morbidity ◽  
Liaison Psychiatry ◽  
Major Pathway ◽  
Lower Priority ◽  
Strategic Plans ◽  
Hospital Patients ◽  
Hospital Doctors

Although referral by general hospital doctors is a major pathway to specialist psychiatric care, and there is known to be much clinically unrecognised psychiatric morbidity among general hospital patients, consultation and liaison services have received much less emphasis than community care. A 1984 survey found that consultation liaison services were haphazard (Mayou & Lloyd, 1985). Despite recent evidence of increasing clinical and academic interest, few local strategic plans refer to consultation and liaison services; even when mentioned they are given a lower priority than community developments (Kingdon, 1989).

The Classification of Psychiatric Morbidity in Attenders at a Dermatology Clinic

1989 ◽  
Vol 155 (5) ◽  
pp. 686-691 ◽  
Author(s):  
S. C. Wessely ◽  
G. H. Lewis
Keyword(s):  
Psychiatric Disorder ◽  
Random Sample ◽  
Skin Disease ◽  
Psychiatric Illness ◽  
Psychiatric Morbidity ◽  
Self Report ◽  
Aetiological Factor ◽  
Psychological Care ◽  
Dermatology Clinic

Of a random sample of new attenders at a dermatology out-patient clinic, 40% were classified as suffering from a psychiatric disorder. There was no correlation between psychiatric morbidity and the severity or site of skin disease. Self-report measures of the behavioural impact of skin disease and attitudes to appearance were related to psychological morbidity. Except in subjects without visible skin pathology (5%) there was no evidence that psychiatric illness was an aetiological factor in the development of skin disease. Self-report measures were used to distinguish between those patients in whom psychiatric morbidity was closely related to skin disease (75%), and those in whom it may be coincidental (20%). Psychological care for the former group is most appropriately provided by physicians, who should be encouraged to improve their detection and management of psychiatric morbidity.

2. The different types of trust

2018 ◽  
Author(s):  
Richard Clements ◽  
Ademola Abass
Keyword(s):  
Modern World ◽  
Family Wealth ◽  
Wide Range ◽  
The Family ◽  
Different Types ◽  
Complete Series ◽  
Explanatory Text ◽  
The Many ◽  
Primary Materials

Titles in the Complete series combine extracts from a wide range of primary materials with clear explanatory text to provide readers with a complete introductory resource. This chapter examines the different types of trust, how they are used, and the nature of a trust. The many uses of trusts in the modern world, from pensions to the ownership of the family home and the preservation of family wealth are explained. The discussions cover the meanings of trust and property; what trusts are used for; what an equitable interest is; classification of trusts; resulting trusts; constructive trusts; implied trusts; Quistclose-type trusts; and wills and intestacies.

Detection and classification of seizures using noisy clinical data and convolutional neural networks (Preprint)

2019 ◽  
Author(s):  
Tomas Iesmantas ◽  
Robertas Alzbutas
Keyword(s):  
Clinical Data ◽  
Seizure Detection ◽  
False Positives ◽  
High Rate ◽  
Physical Injury ◽  
Different Types ◽  
Patient Groups ◽  
Low Sensitivity ◽  
Clinical Conditions

UNSTRUCTURED Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usually are patient non-specific. Epilepsy patients suffer from severe detrimental effects like physical injury or depression due to unpredictable seizures and no potential to bring themselves into the management of their own care. However, even in hospitals due to the high rate of false positives the seizure alert systems are of poor help for patients as tools of seizure detection are mostly trained on unrealistically clean data, containing little noise and obtained under controlled laboratory conditions, where patient groups are homogeneous, e.g. in terms of age or type of seizures. In this study, authors consider this important problem and techniques relevant in connected health and present the approach for detection and classification of a seizure using noisy clinical data of electroencephalograms and a convolutional neural network trained on features of brain synchronisation and power spectrum. Various deep learning methods were applied, and the network was trained on very heterogeneous clinical electroencephalogram dataset. In total, eight different types of seizures were considered, and the patients were of various ages, health conditions and they were observed under clinical conditions. Despite this, classifier presented in this paper achieved sensitivity and specificity equal to 0.68 and 0.67, accordingly, which is a significant improvement as compared to the known results for clinical data.

scholarly journals Pattern classification of interstitial lung disease in high resolution clinical datasets: A systematic review

2018 ◽  
Vol 7 (2.7) ◽  
pp. 114
Author(s):  
S Ummay Atiya ◽  
N V.K Ramesh
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Lung Diseases ◽  
Interstitial Lung Diseases ◽  
High Resolution Computed Tomography ◽  
Different Types ◽  
Deep Convolution Neural Network ◽  
Paper Review ◽  
The Many

Automated tissues characterization helps to diagnosis the various diseases including Interstitial lung diseases (ILD). The various features and the several classifiers are used in categorize the different layers depend on the pattern presented in the image. The different types of diseases may occur in the lungs and some of the diseases happen to leave the scars. These scars can be found in the High Resolution Computed Tomography (HRCT) and have different pattern. The different diseases cause the different pattern in the images and these is classified using the efficient classifier that helps to diagnosis the diseases. In this paper, review for the many researches regarding to the classification of the different pattern from the Computed Tomography (CT) images is presented. The evaluation of the efficiency of the methods in terms of classifier and database used for the research is made. The Deep Convolution Neural Network (CNN) provides the promising classifier efficiency compared to the other researches for different pattern. In general, there are five types of pattern is classified: Healthy, ground glass, honeycomb, Fibrosis, and emphysema.

The Classification of Psychiatric Morbidity in Attenders at a Dermatology Clinic

1989 ◽  
Vol 155 (05) ◽  
pp. 686-691 ◽  
Author(s):  
S. C. Wessely ◽  
G. H. Lewis
Keyword(s):  
Psychiatric Disorder ◽  
Random Sample ◽  
Skin Disease ◽  
Psychiatric Illness ◽  
Psychiatric Morbidity ◽  
Self Report ◽  
Aetiological Factor ◽  
Psychological Care ◽  
Dermatology Clinic

Of a random sample of new attenders at a dermatology out-patient clinic, 40% were classified as suffering from a psychiatric disorder. There was no correlation between psychiatric morbidity and the severity or site of skin disease. Self-report measures of the behavioural impact of skin disease and attitudes to appearance were related to psychological morbidity. Except in subjects without visible skin pathology (5%) there was no evidence that psychiatric illness was an aetiological factor in the development of skin disease. Self-report measures were used to distinguish between those patients in whom psychiatric morbidity was closely related to skin disease (75%), and those in whom it may be coincidental (20%). Psychological care for the former group is most appropriately provided by physicians, who should be encouraged to improve their detection and management of psychiatric morbidity.

Dual stain kit for the microscopic gram classification of ocular infection bacteria

1993 ◽  
Vol 51 ◽  
pp. 248-249
Author(s):  
Jacob S. Hanker ◽  
Dale N. Holdren ◽  
Kenneth L. Cohen ◽  
Beverly L. Giammara
Keyword(s):  
Contact Lenses ◽  
Ocular Infection ◽  
Gram Negative Bacteria ◽  
Gram Negative ◽  
Gram Staining ◽  
Ocular Infections ◽  
Different Types ◽  
Culture Positive ◽  
Increased Susceptibility

Keratitis and conjunctivitis (infections of the cornea or conjunctiva) are ocular infections caused by various bacteria, fungi, viruses or parasites; bacteria, however, are usually prominent. Systemic conditions such as alcoholism, diabetes, debilitating disease, AIDS and immunosuppressive therapy can lead to increased susceptibility but trauma and contact lens use are very important factors. Gram-negative bacteria are most frequently cultured in these situations and Pseudomonas aeruginosa is most usually isolated from culture-positive ulcers of patients using contact lenses. Smears for staining can be obtained with a special swab or spatula and Gram staining frequently guides choice of a therapeutic rinse prior to the report of the culture results upon which specific antibiotic therapy is based. In some cases staining of the direct smear may be diagnostic in situations where the culture will not grow. In these cases different types of stains occasionally assist in guiding therapy.

Explanatory Power for Medical Expert Systems: Studies in the Representation of Causal Relationships for Clinical Consultations

1982 ◽  
Vol 21 (03) ◽  
pp. 127-136 ◽  
Author(s):  
J. W. Wallis ◽  
E. H. Shortliffe
Keyword(s):  
Explanatory Power ◽  
Prototype System ◽  
Inference Rules ◽  
Medical Expert ◽  
Comprehensive Knowledge ◽  
Different Types ◽  
Medical Expert Systems ◽  
Explanation System ◽  
Systems Studies

This paper reports on experiments designed to identify and implement mechanisms for enhancing the explanation capabilities of reasoning programs for medical consultation. The goals of an explanation system are discussed, as is the additional knowledge needed to meet these goals in a medical domain. We have focussed on the generation of explanations that are appropriate for different types of system users. This task requires a knowledge of what is complex and what is important; it is further strengthened by a classification of the associations or causal mechanisms inherent in the inference rules. A causal representation can also be used to aid in refining a comprehensive knowledge base so that the reasoning and explanations are more adequate. We describe a prototype system which reasons from causal inference rules and generates explanations that are appropriate for the user.

Effectiveness of Supervised Classification Models for Hate Speech on Twitter

2020 ◽  
Author(s):  
Kunal Srivastava ◽  
Ryan Tabrizi ◽  
Ayaan Rahim ◽  
Lauryn Nakamitsu
Keyword(s):  
Supervised Classification ◽  
Hate Speech ◽  
The Internet ◽  
Classification Models ◽  
Personal Attack ◽  
Political Beliefs ◽  
The Many ◽  
Primary Medium ◽  
Offensive Speech

<div> <div> <div> <p>Abstract </p> <p>The ceaseless connectivity imposed by the internet has made many vulnerable to offensive comments, be it their physical appearance, political beliefs, or religion. Some define hate speech as any kind of personal attack on one’s identity or beliefs. Of the many sites that grant the ability to spread such offensive speech, Twitter has arguably become the primary medium for individuals and groups to spread these hurtful comments. Such comments typically fail to be detected by Twitter’s anti-hate system and can linger online for hours before finally being taken down. Through sentiment analysis, this algorithm is able to distinguish hate speech effectively through the classification of sentiment. </p> </div> </div> </div>

Sam Domains in Multiple Diseases

2020 ◽  
Vol 27 (3) ◽  
pp. 450-476 ◽  
Author(s):  
Marian Vincenzi ◽  
Flavia Anna Mercurio ◽  
Marilisa Leone
Keyword(s):  
Disease Onset ◽  
Protein Domain ◽  
Sam Domain ◽  
Different Types ◽  
Protein Functions ◽  
Helical Protein ◽  
The Many ◽  
Novel Therapeutic Strategies ◽  
Novel Drug ◽  
Pathological Event

Background: The sterile alpha motif (Sam) domain is a small helical protein module, able to undergo homo- and hetero-oligomerization, as well as polymerization, thus forming different types of protein architectures. A few Sam domains are involved in pathological processes and consequently, they represent valuable targets for the development of new potential therapeutic routes. This study intends to collect state-of-the-art knowledge on the different modes by which Sam domains can favor disease onset and progression. Methods: This review was build up by searching throughout the literature, for: a) the structural properties of Sam domains, b) interactions mediated by a Sam module, c) presence of a Sam domain in proteins relevant for a specific disease. Results: Sam domains appear crucial in many diseases including cancer, renal disorders, cataracts. Often pathologies are linked to mutations directly positioned in the Sam domains that alter their stability and/or affect interactions that are crucial for proper protein functions. In only a few diseases, the Sam motif plays a kind of "side role" and cooperates to the pathological event by enhancing the action of a different protein domain. Conclusion: Considering the many roles of the Sam domain into a significant variety of diseases, more efforts and novel drug discovery campaigns need to be engaged to find out small molecules and/or peptides targeting Sam domains. Such compounds may represent the pillars on which to build novel therapeutic strategies to cure different pathologies.

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