scholarly journals Analyzing Surgical Treatment of Intestinal Obstruction in Children with Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wang-Ren Qiu ◽  
Gang Chen ◽  
Jin Wu ◽  
Jun Lei ◽  
Lei Xu ◽  
...  
Keyword(s):  
Intestinal Obstruction ◽  
Clinical Decision Making ◽  
Rapid Development ◽  
Clinical Decision ◽  
Activation Function ◽  
Text Data ◽  
Clinical Text ◽  
Intestinal Tube ◽  
Diagnostic Measures ◽  
Deep Learning Neural Network

Intestinal obstruction is a common surgical emergency in children. However, it is challenging to seek appropriate treatment for childhood ileus since many diagnostic measures suitable for adults are not applicable to children. The rapid development of machine learning has spurred much interest in its application to medical imaging problems but little in medical text mining. In this paper, a two-layer model based on text data such as routine blood count and urine tests is proposed to provide guidance on the diagnosis and assist in clinical decision-making. The samples of this study were 526 children with intestinal obstruction. Firstly, the samples were divided into two groups according to whether they had intestinal obstruction surgery, and then, the surgery group was divided into two groups according to whether the intestinal tube was necrotic. Specifically, we combined 63 physiological indexes of each child with their corresponding label and fed them into a deep learning neural network which contains multiple fully connected layers. Subsequently, the corresponding value was obtained by activation function. The 5-fold cross-validation was performed in the first layer and demonstrated a mean accuracy (Acc) of 80.04%, and the corresponding sensitivity (Se), specificity (Sp), and MCC were 67.48%, 87.46%, and 0.57, respectively. Additionally, the second layer can also reach an accuracy of 70.4%. This study shows that the proposed algorithm has direct meaning to processing of clinical text data of childhood ileus.

Improving the evidence-based clinical decision-making process: Interactive classification and topic discovery on diabetes-related biomedical literature (Preprint)

2021 ◽  
Author(s):  
Adrian Ahne ◽  
Guy Fagherazzi ◽  
Xavier Tannier ◽  
Thomas Czernichow ◽  
Francisco Orchard
Keyword(s):  
Decision Making ◽  
Active Learning ◽  
Hierarchical Clustering ◽  
Domain Knowledge ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Biomedical Literature ◽  
Decision Making Process ◽  
Evidence Based ◽  
Text Data

BACKGROUND The amount of available textual health data such as scientific and biomedical literature is constantly growing and it becomes more and more challenging for health professionals to properly summarise those data and in consequence to practice evidence-based clinical decision making. Moreover, the exploration of large unstructured health text data is very challenging for non experts due to limited time, resources and skills. Current tools to explore text data lack ease of use, need high computation efforts and have difficulties to incorporate domain knowledge and focus on topics of interest. OBJECTIVE We developed a methodology which is able to explore and target topics of interest via an interactive user interface for experts and non-experts. We aim to reach near state of the art performance, while reducing memory consumption, increasing scalability and minimizing user interaction effort to improve the clinical decision making process. The performance is evaluated on diabetes-related abstracts from Pubmed. METHODS The methodology consists of four parts: 1) A novel interpretable hierarchical clustering of documents where each node is defined by headwords (describe documents in this node the most); 2) An efficient classification system to target topics; 3) Minimized users interaction effort through active learning; 4) A visual user interface through which a user interacts. We evaluated our approach on 50,911 diabetes-related abstracts from Pubmed which provide a hierarchical Medical Subject Headings (MeSH) structure, a unique identifier for a topic. Hierarchical clustering performance was compared against the implementation in the machine learning library scikit-learn. On a subset of 2000 randomly chosen diabetes abstracts, our active learning strategy was compared against three other strategies: random selection of training instances, uncertainty sampling which chooses instances the model is most uncertain about and an expected gradient length strategy based on convolutional neural networks (CNN). RESULTS For the hierarchical clustering performance, we achieved a F1-Score of 0.73 compared to scikit-learn’s of 0.76. Concerning active learning performance, after 200 chosen training samples based on these strategies, the weighted F1-Score over all MeSH codes resulted in satisfying 0.62 F1-Score of our approach, compared to 0.61 of the uncertainty strategy, 0.61 the CNN and 0.45 the random strategy. Moreover, our methodology showed a constant low memory use with increased number of documents but increased execution time. CONCLUSIONS We proposed an easy to use tool for experts and non-experts being able to combine domain knowledge with topic exploration and target specific topics of interest while improving transparency. Furthermore our approach is very memory efficient and highly parallelizable making it interesting for large Big Data sets. This approach can be used by health professionals to rapidly get deep insights into biomedical literature to ultimately improve the evidence-based clinical decision making process.

Why Machine Learning Should Be Taught in Medical Schools (Preprint)

2021 ◽  
Author(s):  
Matthew Nagy ◽  
Nathan Radakovich ◽  
Aziz Nazha
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Rapid Development ◽  
Clinical Care ◽  
Clinical Decision ◽  
Medical Schools ◽  
Medical Workforce ◽  
Ethical Concerns ◽  
Clinical Tools

UNSTRUCTURED The rapid development of machine learning (ML) applications in healthcare promises to transform the landscape of healthcare. In order for ML advancements to be effectively utilized in clinical care, it is necessary for the medical workforce to be prepared to handle these changes. As physicians in training are exposed to a wide breadth of clinical tools during medical school, this offers an ideal opportunity to introduce ML concepts. A foundational understanding of ML will not only be practically useful for clinicians, but will also address ethical concerns for clinical decision making. While select medical schools have made effort to integrate ML didactics and practice into their curriculum, we argue that foundational ML principles should be taught to broadly to medical students across the country.

scholarly journals Doctors, families and the industry in the clinic: the management of ‘intersex’ children in Swiss paediatric medicine (1945–1970)

2021 ◽  
Vol 65 (3) ◽  
pp. 286-305
Author(s):  
Mirjam Janett ◽  
Andrea Althaus ◽  
Marion Hulverscheidt ◽  
Rita Gobet ◽  
Jürg Streuli ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Rapid Development ◽  
Rapid Change ◽  
Clinical Decision ◽  
New Drugs ◽  
Diagnostic Tools ◽  
Diagnostic Categories ◽  
Paediatric Medicine ◽  
The University

AbstractThis manuscript investigates clinical decisions and the management of ‘intersex’ children at the University Children’s Hospital Zurich between 1945 and 1970. This was an era of rapid change in paediatric medicine, something that was mirrored in Zurich. Andrea Prader, the principal figure in this paper, started his career during the late 1940s and was instrumental in moving the hospital towards focusing more on expertise in chronic diseases. Starting in 1950, he helped the Zurich hospital to become the premier centre for the treatment of so-called ‘intersex’ children. It is this treatment, and, in particular, the clinical decision-making that is the centre of our article. This field of medicine was itself not stable. Rapid development of diagnostic tools led to the emergence of new diagnostic categories, the availability of new drugs changed the management of the children’s bodies and an increased number of medical experts became involved in decision-making, a particular focus lay with the role of the children themselves and of course with their families. How involved were children or their families in an era widely known as the golden age of medicine?

scholarly journals Update for Anaesthetists on Clinical Features of COVID-19 Patients and Relevant Management

2020 ◽  
Vol 9 (5) ◽  
pp. 1495 ◽  
Author(s):  
Benedikt Preckel ◽  
Marcus J. Schultz ◽  
Alexander P. Vlaar ◽  
Abraham H. Hulst ◽  
Jeroen Hermanides ◽  
...  
Keyword(s):  
Clinical Features ◽  
Clinical Decision Making ◽  
Rapid Development ◽  
Antiviral Treatment ◽  
Daily Practice ◽  
Clinical Decision ◽  
Daily Basis ◽  
Specific Patient ◽  
Subjective Analysis ◽  
Immunosuppressed Patients

When preparing for the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the coronavirus infection disease (COVID-19) questions arose regarding various aspects concerning the anaesthetist. When reviewing the literature it became obvious that keeping up-to-date with all relevant publications is almost impossible. We searched for and summarised clinically relevant topics that could help making clinical decisions. This is a subjective analysis of literature concerning specific topics raised in our daily practice (e.g., clinical features of COVID-19 patients; ventilation of the critically ill COVID-19 patient; diagnostic of infection with SARS-CoV-2; stability of the virus; Covid-19 in specific patient populations, e.g., paediatrics, immunosuppressed patients, patients with hypertension, diabetes mellitus, kidney or liver disease; co-medication with non-steroidal anti-inflammatory drugs (NSAIDs); antiviral treatment) and we believe that these answers help colleagues in clinical decision-making. With ongoing treatment of severely ill COVID-19 patients other questions will come up. While respective guidelines on these topics will serve clinicians in clinical practice, regularly updating all guidelines concerning COVID-19 will be a necessary, although challenging task in the upcoming weeks and months. All recommendations during the current extremely rapid development of knowledge must be evaluated on a daily basis, as suggestions made today may be out-dated with the new evidence available tomorrow.

scholarly journals Rapid development of a Clinical Decision-Making Committee in a UK paediatric hospital during the COVID-19 pandemic

2020 ◽  
Vol 20 (2) ◽  
pp. 167-180 ◽  
Author(s):  
Rick Turnock ◽  
Will Weston ◽  
Nicki Murdock ◽  
Amel Alghrani ◽  
Conor Mallucci ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Ethical Decision Making ◽  
Rapid Development ◽  
Clinical Decision ◽  
Paediatric Hospital ◽  
Ethical Frameworks ◽  
National Guidance ◽  
The Government ◽  
Set Up

To date, the Government has not issued any national ethical guidance to support clinical decision-making in England during periods of potentially reduced healthcare resources in the context of the evolving COVID-19 1 pandemic at the time of writing. In the ensuing vacuum left by a lack of national guidance, ethical frameworks and approaches have been drafted by professional bodies, individual hospitals and trusts. It is clear that in delivering healthcare during this pandemic, more specific guidance is needed to ensure fair and consistent allocation policies, to attain public trust and confidence and to support clinicians so that decisions do not fall on them to make alone and unsupported. This article sets out how we in our institution, a UK tertiary and secondary level stand-alone paediatric provider Trust, set up a Clinical Decision-Making Committee to inform proactive clinical and ethical decision-making, to ensure that all patients are treated appropriately and fairly during these unprecedented times.

Preliminary Findings on the Use of Session Questionnaires to Improve Clinician-Client Alliance With Special Consideration to Clinical Education

2015 ◽  
Vol 25 (1) ◽  
pp. 50-60
Author(s):  
Anu Subramanian
Keyword(s):  
Graduate Students ◽  
Clinical Education ◽  
Clinical Decision Making ◽  
Rating Scale ◽  
Treatment Effectiveness ◽  
Clinical Decision ◽  
Added Value ◽  
Therapeutic Outcomes ◽  
Specific Feedback ◽  
Stakeholder Perspective

ASHA's focus on evidence-based practice (EBP) includes the family/stakeholder perspective as an important tenet in clinical decision making. The common factors model for treatment effectiveness postulates that clinician-client alliance positively impacts therapeutic outcomes and may be the most important factor for success. One strategy to improve alliance between a client and clinician is the use of outcome questionnaires. In the current study, eight parents of toddlers who attended therapy sessions at a university clinic responded to a session outcome questionnaire that included both rating scale and descriptive questions. Six graduate students completed a survey that included a question about the utility of the questionnaire. Results indicated that the descriptive questions added value and information compared to using only the rating scale. The students were varied in their responses regarding the effectiveness of the questionnaire to increase their comfort with parents. Information gathered from the questionnaire allowed for specific feedback to graduate students to change behaviors and created opportunities for general discussions regarding effective therapy techniques. In addition, the responses generated conversations between the client and clinician focused on clients' concerns. Involving the stakeholder in identifying both effective and ineffective aspects of therapy has advantages for clinical practice and education.

Clinical Decision-Making for Stroke and Aphasia in the Older Adult

2009 ◽  
Vol 14 (1) ◽  
pp. 4-11 ◽  
Author(s):  
Jacqueline Hinckley
Keyword(s):  
Executive Function ◽  
Cerebrovascular Disease ◽  
Cognitive Abilities ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Cognitive Profile ◽  
Focal Lesion ◽  
Cognitive Profiles ◽  
Supportive Communication ◽  
Spaced Retrieval

Abstract A patient with aphasia that is uncomplicated by other cognitive abilities will usually show a primary impairment of language. The frequency of additional cognitive impairments associated with cerebrovascular disease, multiple (silent or diagnosed) infarcts, or dementia increases with age and can complicate a single focal lesion that produces aphasia. The typical cognitive profiles of vascular dementia or dementia due to cerebrovascular disease may differ from the cognitive profile of patients with Alzheimer's dementia. In order to complete effective treatment selection, clinicians must know the cognitive profile of the patient and choose treatments accordingly. When attention, memory, and executive function are relatively preserved, strategy-based and conversation-based interventions provide the best choices to target personally relevant communication abilities. Examples of treatments in this category include PACE and Response Elaboration Training. When patients with aphasia have co-occurring episodic memory or executive function impairments, treatments that rely less on these abilities should be selected. Examples of treatments that fit these selection criteria include spaced retrieval and errorless learning. Finally, training caregivers in the use of supportive communication strategies is helpful to patients with aphasia, with or without additional cognitive complications.

Debates in Dysphagia Management: How Do You Use Evidence-Based Practice in Your Dysphagia Patient Care?

2011 ◽  
Vol 20 (4) ◽  
pp. 121-123
Author(s):  
Jeri A. Logemann
Keyword(s):  
Decision Making ◽  
Patient Care ◽  
Clinical Outcomes ◽  
Clinical Judgment ◽  
Clinical Decision Making ◽  
Evidence Based Practice ◽  
Clinical Decision ◽  
Evidence Based ◽  
Clinical Method ◽  
Do So

Evidence-based practice requires astute clinicians to blend our best clinical judgment with the best available external evidence and the patient's own values and expectations. Sometimes, we value one more than another during clinical decision-making, though it is never wise to do so, and sometimes other factors that we are unaware of produce unanticipated clinical outcomes. Sometimes, we feel very strongly about one clinical method or another, and hopefully that belief is founded in evidence. Some beliefs, however, are not founded in evidence. The sound use of evidence is the best way to navigate the debates within our field of practice.

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