Clinical and financial outcomes analysis with existing hospital patient records

Rao, R. Bharat; Sandilya, Sathyakama; Niculescu, Radu Stefan; Germond, Colin; Rao, H.R.

doi:10.1145/956750.956798

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…Earlier approaches to medical coding include rulebased engines [1,10], automatic or semi-automatic methods [6,8]. Several companies, such as CodeRite 1 and A-Life 2 , provide semi-automatic solutions based on keywords in the disease name.…”

Section: Introductionmentioning

confidence: 99%

Automatic medical coding of patient records via weighted ridge regression

Yucesoy

Öğüdücü

2007

Sixth International Conference on Machine Learning and Applications (ICMLA 2007)

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In this paper, we apply weighted ridge regression to tackle the highly unbalanced data issue in automatic largescale ICD-9 coding of medical patient records. Since most of the ICD-9 codes are unevenly represented in the medical records, a weighted scheme is employed to balance positive and negative examples. The weights turn out to be associated with the instance priors from a probabilistic interpretation, and an efficient EM algorithm is developed to automatically update both the weights and the regularization parameter. Experiments on a large-scale real patient database suggest that the weighted ridge regression outperforms the conventional ridge regression and linear support vector machines (SVM).

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Section: Introductionmentioning

confidence: 99%

Automatic medical coding of patient records via weighted ridge regression

Yucesoy

Öğüdücü

2007

Sixth International Conference on Machine Learning and Applications (ICMLA 2007)

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“…Applications of data mining have already been proven to provide benefits to many areas of medicine, including diagnosis, prognosis and treatment [7] . Data mining techniques have been applied in various medical fields, amongst with are health administration [8,9] , adverse drug reactions [10][11][12] , drug safety [13,14] , predicting breast cancer survivability [15] , predicting survival time for kidney dialysis patients [16] , knowledge discovery in hypoplastic left heart syndrome [17] and predicting protein function [18] . Because of these issues, we think there is a need of aid for health practitioners during treatment process, as well as consideration of patient's well being.…”

Section: Introductionmentioning

confidence: 99%

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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Identifying Risk Groups Associated with Colorectal Cancer

Chen

Jin

et al. 2006

Lecture Notes in Computer Science

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In this paper, we explore data mining techniques for the task of identifying and describing risk groups for colorectal cancer (CRC) from population based administrative health data. Association rule discovery, association classification and scalable clustering analysis are applied to the colorectal cancer patients' profiles in contrast to background patients' profiles. These data mining methods enable us to identify the most common characteristics of the colorectal cancer patients. The knowledge discovered by data mining methods which are quite different from traditional survey approaches. Although it is heuristic, the data mining methods may identify risk groups for further epidemiological study, such as older patients living near health facilities yet seldom utilising those facilities, and with respiratory and circulatory diseases.

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Clinical and financial outcomes analysis with existing hospital patient records

Cited by 8 publications

References 13 publications

Automatic medical coding of patient records via weighted ridge regression

Automatic medical coding of patient records via weighted ridge regression

Generating Treatment Plan in Medicine: A Data Mining Approach

Identifying Risk Groups Associated with Colorectal Cancer

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