scholarly journals Clinical Trials for Artificial Intelligence in Cancer Diagnosis: A Cross-Sectional Study of Registered Trials in ClinicalTrials.gov

2020 ◽  
Vol 10 ◽  
Author(s):  
Jingsi Dong ◽  
Yingcai Geng ◽  
Dan Lu ◽  
Bingjie Li ◽  
Long Tian ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trials ◽  
Cancer Diagnosis ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional

scholarly journals Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional study in Tsukuba Science City, Japan

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046265
Author(s):  
Shotaro Doki ◽  
Shinichiro Sasahara ◽  
Daisuke Hori ◽  
Yuichi Oi ◽  
Tsukasa Takahashi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Psychological Distress ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Explanatory Variables ◽  
The Neural Network ◽  
Significant Difference ◽  
Total Data

ObjectivesPsychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists.DesignCross-sectional study.SettingWe conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists.ParticipantsAn AI model of the neural network and six psychiatrists.Primary outcomeThe accuracies of the AI model and psychiatrists for predicting psychological distress.MethodsIn total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model.ResultsThe accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy.ConclusionsA machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views.

scholarly journals Agreement between patients’ and radiation oncologists’ cancer diagnosis and prognosis perceptions: A cross sectional study in Japan

PLoS ONE ◽  
2018 ◽  
Vol 13 (6) ◽  
pp. e0198437 ◽  
Author(s):  
Lisa Jane Mackenzie ◽  
Mariko Leanne Carey ◽  
Eiji Suzuki ◽  
Robert William Sanson-Fisher ◽  
Hiromi Asada ◽  
...  
Keyword(s):  
Cancer Diagnosis ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Diagnosis And Prognosis ◽  
Radiation Oncologists ◽  
Cancer Diagnosis And Prognosis

Clinical trials knowledge and attitudes of Vietnamese‐ and Anglo‐Australian cancer patients: A cross‐sectional study

2020 ◽  
Vol 16 (5) ◽  
Author(s):  
Allan B. Smith ◽  
Anita Y. Niu ◽  
Joseph Descallar ◽  
Geoff P. Delaney ◽  
Verena S. Wu ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Cancer Patients ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Knowledge And Attitudes

scholarly journals Cross-sectional study using primary care and cancer registration data to investigate patients with cancer presenting with non-specific symptoms

BMJ Open ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. e033008 ◽  
Author(s):  
Clare Pearson ◽  
Veronique Poirier ◽  
Karen Fitzgerald ◽  
Greg Rubin ◽  
Willie Hamilton
Keyword(s):  
Primary Care ◽  
Cancer Diagnosis ◽  
Cross Sectional Study ◽  
Cancer Registration ◽  
Sectional Study ◽  
Cross Sectional ◽  
Site Specific ◽  
Registration Data ◽  
Referral Criteria ◽  
Specific Symptoms

IntroductionPatients presenting to primary care with site-specific alarm symptoms can be referred onto urgent suspected cancer pathways, whereas those with non-specific symptoms currently have no dedicated referral routes leading to delays in cancer diagnosis and poorer outcomes. Pilot Multidisciplinary Diagnostic Centres (MDCs) provide a referral route for such patients in England.ObjectivesThis work aimed to use linked primary care and cancer registration data to describe diagnostic pathways for patients similar to those being referred into MDCs and compare them to patients presenting with more specific symptoms.MethodsThis cross-sectional study linked primary care data from the National Cancer Diagnosis Audit (NCDA) to national cancer registration and Route to Diagnosis records. Patient symptoms recorded in the NCDA were used to allocate patients to one of two groups - those presenting with symptoms mirroring referral criteria of MDCs (non-specific but concerning symptoms (NSCS)) and those with at least one site-specific alarm symptom (non-NSCS). Descriptive analyses compared the two groups and regression analysis by group investigated associations with long primary care intervals (PCIs).ResultsPatients with NSCS were more likely to be diagnosed at later stage (32% stage 4, compared with 21% in non-NSCS) and via an emergency presentation (34% vs 16%). These patients also had more multiple pre-referral general practitioner consultations (59% vs 43%) and primary care-led diagnostics (blood tests: 57% vs 35%). Patients with NSCS had higher odds of having longer PCIs (adjusted OR: 1.24 (1.11 to 1.36)). Patients with lung and urological cancers also had higher odds of longer PCIs overall and in both groups.ConclusionsDifferences in the diagnostic pathway show that patients with symptoms mirroring the MDC referral criteria could benefit from a new referral pathway.

scholarly journals Empowerment from patient’s and caregiver’s perspective in cancer care

2018 ◽  
Vol 5 (2) ◽  
pp. 205510291881531 ◽  
Author(s):  
Chiara Marzorati ◽  
Luca Bailo ◽  
Ketti Mazzocco ◽  
Gabriella Pravettoni
Keyword(s):  
Cancer Diagnosis ◽  
Patient Participation ◽  
Patient Empowerment ◽  
Cross Sectional Study ◽  
Health Condition ◽  
Sectional Study ◽  
Cross Sectional ◽  
Cancer Disease ◽  
Caregiver Relationship ◽  
Level Of Knowledge

The caregivers’ perceptions of the patients’ health condition may be biased and induce them to perceive higher needs than patients actually disclose. Our aim was to assess if the level of knowledge and awareness about cancer disease and treatment, and patient participation and assistance differs between caregivers and patients. A descriptive, cross-sectional study was conducted across five countries (Italy, United Kingdom, Spain, France and Germany) on a total of 510 participants who directly (patient) or indirectly (caregiver) faced a cancer diagnosis. Investigating this divergence could help to identify possible difficulties in patient–caregiver relationship, eventually improving patient empowerment.

Depression and anxiety symptoms following cancer diagnosis: a cross-sectional study

2015 ◽  
Vol 21 (5) ◽  
pp. 562-570 ◽  
Author(s):  
Graça Cardoso ◽  
Joao Graca ◽  
Catarina Klut ◽  
Bruno Trancas ◽  
Ana Papoila
Keyword(s):  
Cancer Diagnosis ◽  
Cross Sectional Study ◽  
Anxiety Symptoms ◽  
Depression And Anxiety ◽  
Sectional Study ◽  
Cross Sectional
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