scholarly journals Elusive adenomyosis: a plea for an international classification system to allow artificial intelligence approaches to reset our clinical management

2018 ◽  
Vol 110 (6) ◽  
pp. 1039-1040 ◽  
Author(s):  
Michel Canis ◽  
Anne Sophie Gremeau ◽  
Nicolas Bourdel
Keyword(s):  
Artificial Intelligence ◽  
Classification System ◽  
Clinical Management ◽  
International Classification

An artificial intelligence-enabled industry classification and its interpretation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Daejin Kim ◽  
Hyoung-Goo Kang ◽  
Kyounghun Bae ◽  
Seongmin Jeon
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Classification System ◽  
Standard Industrial Classification ◽  
American Industry ◽  
Content Type ◽  
Global Industry ◽  
Machine Learning Methods ◽  
Industry Classification ◽  
Traditional Industry

PurposeTo overcome the shortcomings of traditional industry classification systems such as the Standard Industrial Classification Standard Industrial Classification, North American Industry Classification System North American Industry Classification System, and Global Industry Classification Standard Global Industry Classification Standard, the authors explore industry classifications using machine learning methods as an application of interpretable artificial intelligence (AI).Design/methodology/approachThe authors propose a text-based industry classification combined with a machine learning technique by extracting distinguishable features from business descriptions in financial reports. The proposed method can reduce the dimensions of word vectors to avoid the curse of dimensionality when measuring the similarities of firms.FindingsUsing the proposed method, the sample firms form clusters of distinctive industries, thus overcoming the limitations of existing classifications. The method also clarifies industry boundaries based on lower-dimensional information. The graphical closeness between industries can reflect the industry-level relationship as well as the closeness between individual firms.Originality/valueThe authors’ work contributes to the industry classification literature by empirically investigating the effectiveness of machine learning methods. The text mining method resolves issues concerning the timeliness of traditional industry classifications by capturing new information in annual reports. In addition, the authors’ approach can solve the computing concerns of high dimensionality.

scholarly journals The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors

2019 ◽  
Vol 53 (10) ◽  
pp. 954-964 ◽  
Author(s):  
Trehani M Fonseka ◽  
Venkat Bhat ◽  
Sidney H Kennedy
Keyword(s):  
Artificial Intelligence ◽  
At Risk ◽  
Risk Prediction ◽  
Clinical Management ◽  
Behavior Management ◽  
Suicide Risk ◽  
English Language ◽  
Case Reports ◽  
Mental Healthcare ◽  
Health Concern

Objective: Suicide is a growing public health concern with a global prevalence of approximately 800,000 deaths per year. The current process of evaluating suicide risk is highly subjective, which can limit the efficacy and accuracy of prediction efforts. Consequently, suicide detection strategies are shifting toward artificial intelligence platforms that can identify patterns within ‘big data’ to generate risk algorithms that can determine the effects of risk (and protective) factors on suicide outcomes, predict suicide outbreaks and identify at-risk individuals or populations. In this review, we summarize the role of artificial intelligence in optimizing suicide risk prediction and behavior management. Methods: This paper provides a general review of the literature. A literature search was conducted in OVID Medline, EMBASE and PsycINFO databases with coverage from January 1990 to June 2019. Results were restricted to peer-reviewed, English-language articles. Conference and dissertation proceedings, case reports, protocol papers and opinion pieces were excluded. Reference lists were also examined for additional articles of relevance. Results: At the individual level, prediction analytics help to identify individuals in crisis to intervene with emotional support, crisis and psychoeducational resources, and alerts for emergency assistance. At the population level, algorithms can identify at-risk groups or suicide hotspots, which help inform resource mobilization, policy reform and advocacy efforts. Artificial intelligence has also been used to support the clinical management of suicide across diagnostics and evaluation, medication management and behavioral therapy delivery. There could be several advantages of incorporating artificial intelligence into suicide care, which includes a time- and resource-effective alternative to clinician-based strategies, adaptability to various settings and demographics, and suitability for use in remote locations with limited access to mental healthcare supports. Conclusion: Based on the observed benefits to date, artificial intelligence has a demonstrated utility within suicide prediction and clinical management efforts and will continue to advance mental healthcare forward.

Prospective Applicability Study of the New Dengue Classification System for Clinical Management in Children

2014 ◽  
Vol 33 (9) ◽  
pp. 933-935 ◽  
Author(s):  
Dolores Lovera ◽  
Soraya Araya ◽  
Maria Jose Mesquita ◽  
Cinthia Avalos ◽  
Silvia Ledesma ◽  
...  
Keyword(s):  
Classification System ◽  
Clinical Management

scholarly journals Visibility of Community Nursing Within an Administrative Health Classification System: Evaluation of Content Coverage (Preprint)

2018 ◽  
Author(s):  
Lorraine J Block ◽  
Leanne M Currie ◽  
Nicholas R Hardiker ◽  
Gillian Strudwick
Keyword(s):  
Classification System ◽  
Policy Development ◽  
Phase 1 ◽  
Death And Dying ◽  
Health Information System ◽  
Nursing Intervention ◽  
International Classification ◽  
World Health ◽  
Nursing Interventions ◽  
Community Nursing

BACKGROUND The World Health Organization is in the process of developing an international administrative classification for health called the International Classification of Health Interventions (ICHI). The purpose of ICHI is to provide a tool for supporting intervention reporting and analysis at a global level for policy development and beyond. Nurses represent the largest resource carrying out clinical interventions in any health system. With the shift in nursing care from hospital to community settings in many countries, it is important to ensure that community nursing interventions are present in any international health information system. Thus, an investigation into the extent to which community nursing interventions were covered in ICHI was needed. OBJECTIVE The objectives of this study were to examine the extent to which International Classification for Nursing Practice (ICNP) community nursing interventions were represented in the ICHI administrative classification system, to identify themes related to gaps in coverage, and to support continued advancements in understanding the complexities of knowledge representation in standardized clinical terminologies and classifications. METHODS This descriptive study used a content mapping approach in 2 phases in 2018. A total of 187 nursing intervention codes were extracted from the ICNP Community Nursing Catalogue and mapped to ICHI. In phase 1, 2 coders completed independent mapping activities. In phase 2, the 2 coders compared each list and discussed concept matches until consensus on ICNP-ICHI match and on mapping relationship was reached. RESULTS The initial percentage agreement between the 2 coders was 47% (n=88), but reached 100% with consensus processes. After consensus was reached, 151 (81%) of the community nursing interventions resulted in an ICHI match. A total of 36 (19%) of community nursing interventions had no match to ICHI content. A total of 100 (53%) community nursing interventions resulted in a broader ICHI code, 9 (5%) resulted in a narrower ICHI code, and 42 (23%) were considered equivalent. ICNP concepts that were not represented in ICHI were thematically grouped into the categories family and caregivers, death and dying, and case management. CONCLUSIONS Overall, the content mapping yielded similar results to other content mapping studies in nursing. However, it also found areas of missing concept coverage, difficulties with interterminology mapping, and further need to develop mapping methods.

scholarly journals Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0248809
Author(s):  
Anna Lind ◽  
Ehsan Akbarian ◽  
Simon Olsson ◽  
Hans Nåsell ◽  
Olof Sköldenberg ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Knee Joint ◽  
Classification System ◽  
Plain Radiograph ◽  
Treatment Strategies ◽  
University Hospital ◽  
Complication Rates ◽  
Radiographic Images ◽  
Femur Fractures

Background Fractures around the knee joint are inherently complex in terms of treatment; complication rates are high, and they are difficult to diagnose on a plain radiograph. An automated way of classifying radiographic images could improve diagnostic accuracy and would enable production of uniformly classified records of fractures to be used in researching treatment strategies for different fracture types. Recently deep learning, a form of artificial intelligence (AI), has shown promising results for interpreting radiographs. In this study, we aim to evaluate how well an AI can classify knee fractures according to the detailed 2018 AO-OTA fracture classification system. Methods We selected 6003 radiograph exams taken at Danderyd University Hospital between the years 2002–2016, and manually categorized them according to the AO/OTA classification system and by custom classifiers. We then trained a ResNet-based neural network on this data. We evaluated the performance against a test set of 600 exams. Two senior orthopedic surgeons had reviewed these exams independently where we settled exams with disagreement through a consensus session. Results We captured a total of 49 nested fracture classes. Weighted mean AUC was 0.87 for proximal tibia fractures, 0.89 for patella fractures and 0.89 for distal femur fractures. Almost ¾ of AUC estimates were above 0.8, out of which more than half reached an AUC of 0.9 or above indicating excellent performance. Conclusion Our study shows that neural networks can be used not only for fracture identification but also for more detailed classification of fractures around the knee joint.

scholarly journals Recycled Clothing Classification System Using Intelligent IoT and Deep Learning with AlexNet

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Sun-Kuk Noh
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Object Recognition ◽  
Environmental Pollution ◽  
Classification System ◽  
Raw Material ◽  
Raspberry Pi ◽  
Utilization Rate ◽  
Automation System ◽  
Labor Costs

Recently, Internet of Things (IoT) and artificial intelligence (AI), led by machine learning and deep learning, have emerged as key technologies of the Fourth Industrial Revolution (4IR). In particular, object recognition technology using deep learning is currently being used in various fields, and thanks to the strong performance and potential of deep learning, many research groups and Information Technology (IT) companies are currently investing heavily in deep learning. The textile industry involves a lot of human resources in all processes, such as raw material collection, dyeing, processing, and sewing, and the wastage of resources and energy and increase in environmental pollution are caused by the short-term waste of clothing produced during these processes. Environmental pollution can be reduced to a great extent through the use of recycled clothing. In Korea, the utilization rate of recycled clothing is increasing, the amount of used clothing is high with the annual consumption being at $56.2 billion, but it is not properly utilized because of the manual recycling clothing collection system. It has several problems such as a closed workplace environment, workers’ health, rising labor costs, and low processing speed that make it difficult to apply the existing clothing recognition technology, classified by deformation and overlapping of clothing shapes, when transporting recycled clothing to the conveyor belt. In this study, I propose a recycled clothing classification system with IoT and AI using object recognition technology to the problems. The IoT device consists of Raspberry pi and a camera, and AI uses the transfer-learned AlexNet to classify different types of clothing. As a result of this study, it was confirmed that the types of recycled clothing using artificial intelligence could be predicted and accurate classification work could be performed instead of the experience and know-how of working workers in the clothing classification worksite, which is a closed space. This will lead to the innovative direction of the recycling clothing classification work that was performed by people in the existing working worker. In other words, it is expected that standardization of necessary processes, utilization of artificial intelligence, application of automation system, various cost reduction, and work efficiency improvement will be achieved.

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