Data mining for indicators of early mortality in a database of clinical records

Richards, Graeme; Rayward‐Smith, V. J.; Sönksen, Peter H.; Carey, Steve; Weng, C.

doi:10.1016/s0933-3657(00)00110-x

Cited by 95 publications

(77 citation statements)

References 8 publications

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“…6. All item type were found to be the same as --full blood count Figure 5 the large sums of data, data mining can and should, be used to gain more insight into the diseases, generate knowledge that can potentially fuel lead to further research in many areas of medicine [6] . In order to increase the efficient and practical use of the generated treatment plan, it is best to integrate it with existing clinical information system.…”

Section: Resultsmentioning

confidence: 99%

“…In certain cases where changes occur maybe due to environmental changes, new medical findings and so forth, healthcare providers' service are still needed. Data mining can provide assistance in making the diagnosis or prescribing the treatment, but it still cannot replace the physician's intuition and interpretive skills [6] . Proposed items to be given to J06.9 patients seem to narrow down the probability of making mistakes and reduce the complexity of treatment process.…”

Section: Resultsmentioning

confidence: 99%

“…In spite of that, data mining techniques which are part of knowledge discovery in databases (KDD), have become popular research tools for medical researchers who seek to identify and exploit patterns and relationships among large number of variables and be able to predict the outcome of a disease using the historical cases stored within datasets [5,6] . Applications of data mining have already been proven to provide benefits to many areas of medicine, including diagnosis, prognosis and treatment [7] .…”

Section: Introductionmentioning

confidence: 99%

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Generating Treatment Plan in Medicine: A Data Mining Approach

Razali¹,

Ali

2009

American Journal of Applied Sciences

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This study reports on a research effort on generating treatment plan to handle the error and complexity of treatment process for healthcare providers. Focus has been given for outpatient and was based on data collected from various health centers throughout Malaysia. These clinical data were recorded using SOAP (Subjective, Objective, Assessment and Plan) format approach as being practiced in medicine and were recorded electronically via Percuro Clinical Information System (Percuro). Cross-Industry Standard Process for Data Mining (CRISP-DM) model has been utilized for the entire research. We used data mining analysis through decision trees technique with C5 algorithm. The scopes that have been set are patient's complaint, gender, age, race, type of plan and detailed item given to patient. Acute upper respiratory infection disease or identified as J06.9 in International Classification of Diseases 10 by World Health Organization has been selected as it was the most common problem encountered. The model created for J06.9 disease is that type of plan recommended through giving drug to patients without the need to consider patient's complaint, gender, age and race, with accuracy obtained for the model is 94.73%. Inspite of that, we also identified detailed items that have been given to J06.9 patients and the occurancy of them. This can be as a guideline for future treatment with item recommendation is less than 0.078% compared to item inventory in Percuro database. The research is expected to aid healthcare provider as well as to minimize error during treatment process while benefited from technology information to increase the health care delivery.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generating Treatment Plan in Medicine: A Data Mining Approach

Razali¹,

Ali

2009

American Journal of Applied Sciences

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“…1996) Therefore Knowledge Data Discovery (KDD), which includes data mining techniques, has become a popular research tool for medical researchers who seek to identify and exploit patterns and relationships among large number of variables, and be able to predict the outcome of a disease using the historical cases stored in data warehouses. (Richards, 2001) …”

Section: Antônio Augusto Gonçalves E José Geraldo Pereira Barbosamentioning

confidence: 99%

The development of an ICT framework for business intelligence at the Brazilian National Cancer Institute: a case study of organizational learning and innovation

Gonçalves¹,

Barbosa²

2017

ReA UFSM

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“…The authors of [15] present a study on generation of rules that describe associations among attributes. The experiments conducted on real medical data and their correctness verified with the use of statistical measures and physicians' evaluations.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Data Mining Algorithms and Techniques in Medicine

Madadipouya

2017

JOIV : Int. J. Inform. Visualization

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Abstract-Medical Decision Support Systems (MDSS) industry collects a huge amount of data, which is not properly mined and not put to the optimum use. This data may contain valuable information that awaits extraction. The knowledge may be encapsulated in various patterns and regularities that may be hidden in the data. Such knowledge may prove to be priceless in future medical decision making. Available medical decision support systems are based on static data, which may be out of date. Thus, a medical decision support system that can learn the relationships between patient histories, diseases in the population, symptoms, pathology of a disease, family history, and test results, would be useful to physicians and hospitals.

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Data mining for indicators of early mortality in a database of clinical records

Cited by 95 publications

References 8 publications

Generating Treatment Plan in Medicine: A Data Mining Approach

Generating Treatment Plan in Medicine: A Data Mining Approach

The development of an ICT framework for business intelligence at the Brazilian National Cancer Institute: a case study of organizational learning and innovation

A Survey on Data Mining Algorithms and Techniques in Medicine

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