Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Shameek Ghosh"'
Publikováno v:
BMC Bioinformatics, Vol 19, Iss S13, Pp 91-104 (2019)
Abstract Background In silico prediction of potential drug side-effects is of crucial importance for drug development, since wet experimental identification of drug side-effects is expensive and time-consuming. Existing computational methods mainly f
Externí odkaz:
https://doaj.org/article/2b08c28dcb7343b8bb518d849d1a4c5e
Publikováno v:
PRICAI 2019: Trends in Artificial Intelligence ISBN: 9783030298937
PRICAI (3)
PRICAI (3)
Document classification (DC) is one of the broadly investigated natural language processing tasks. Medical document classification can support doctors in making decision and improve medical services. Since the data in document classification often ap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d5c93c5d8d259aa9c49ebcdcc7450fb8
https://doi.org/10.26686/wgtn.14676090.v1
https://doi.org/10.26686/wgtn.14676090.v1
Publikováno v:
CEC
Document classification (DC) is the task of assigning pre-defined labels to unseen documents by utilizing a model trained on the available labeled documents. DC has attracted much attention in medical fields recently because many issues can be formul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3feca124ae09f0c622bf6bb7a1b5e66e
https://doi.org/10.26686/wgtn.14676087.v1
https://doi.org/10.26686/wgtn.14676087.v1
Publikováno v:
WI/IAT
Medical document classification is one of the prominent research problems in document classification domain. As medical discharge notes are collected from real patients, they are often imbalanced. Moreover, these datasets are usually too small for da
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e893bd6bc2635580756d5b9c69a06f09
https://doi.org/10.1109/wiiat50758.2020.00051
https://doi.org/10.1109/wiiat50758.2020.00051
Publikováno v:
ICMLA
Clinical knowledge graphs (KG) are often incomplete in their early stages of development, prone to human error, and have limited quality control. Due to continuous human updates to these clinical large-scale graphs, data errors are very high and cost
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ceaa219e8c08a84a31dbf53129d70ddb
https://doi.org/10.1109/icmla51294.2020.00181
https://doi.org/10.1109/icmla51294.2020.00181
Publikováno v:
CIKM
Differential diagnostic systems provide a ranked list of highly prob-able diseases given a patient's profile and symptoms. Evaluation of diagnostic algorithms in literature has been limited to a small set of hand-crafted patient vignettes. Testing wi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cbcfbfba6f197f6ae999c87d240dc71c
https://doi.org/10.1145/3340531.3412693
https://doi.org/10.1145/3340531.3412693
Publikováno v:
EMBC
In clinical conversational applications, extracted entities tend to capture the main subject of a patient's complaint, namely symptoms or diseases. However, they mostly fail to recognize the characterizations of a complaint such as the time, the onse
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ae9dfce4ce7b2f68d0f5d9836f9aa9b
https://doi.org/10.1109/embc44109.2020.9175577
https://doi.org/10.1109/embc44109.2020.9175577
Publikováno v:
Artificial Intelligence in Medicine ISBN: 9783030591366
AIME
AIME
Extracting meaningful features from unstructured text is one of the most challenging tasks in medical document classification. The various domain specific expressions and synonyms in the clinical discharge notes make it more challenging to analyse th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7568e32fe97d5739b3834ff8893d3102
https://hdl.handle.net/10453/148370
https://hdl.handle.net/10453/148370
Publikováno v:
Artificial Intelligence in Medicine. 120:102167
Biomedical natural language processing (NLP) has an important role in extracting consequential information in medical discharge notes. Detecting meaningful features from unstructured notes is a challenging task in medical document classification. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::753565cf22302114fe1595006d735150
https://doi.org/10.1016/j.artmed.2021.102167
https://doi.org/10.1016/j.artmed.2021.102167
The supplementary figures for this work. Figure S1: Scatter plots of F1-scores for different classifiers using the ChemTar similarity on balanced and imbalanced dataset. Figure S2: Scatter plots of F1-scores for ComNegative and ChemTarRandom using di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::025bff6f92a3fddd60e099c96d0c6b83