Application of Data Mining Techniques in Clinical Decision Making

2017 ◽  
pp. 257-295
Author(s):  
Hakimeh Ameri ◽  
Somayeh Alizadeh ◽  
Elham Akhond Zadeh Noughabi
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Treatment Effectiveness ◽  
Average Length ◽  
Clinical Decision ◽  
Drug Dosage ◽  
Classification Model ◽  
Data Mining Techniques ◽  
Average Length Of Stay

Data mining techniques are increasingly used in clinical decision making and help the physicians to make more accurate and effective decisions. In this chapter, a classification of data mining applications in clinical decision making is presented through a systematic review. The applications of data mining techniques in clinical decision making are divided into two main categories: diagnosis and treatment. Early prediction of medical conditions, detecting multi-morbidity and complications of diseases, identifying and predicting the chronic diseases and medical imaging are the subcategories which are defined in the diagnosis part. The Treatment category is composed of treatment effectiveness and predicting the average length of stay in hospital. The majority of the reviewed articles are related to diagnosis and there is only one article which discusses the determination of drug dosage in successful treatment. The classification model is the most commonly practical model in the clinical decision making.

scholarly journals Factors Influencing Physicians' Clinical Decision-making at Upazila Health Complexes in Bangladesh

2020 ◽  
Vol 3 (4) ◽  
pp. 125-133
Author(s):  
M. Aminul Islam ◽  
M. Abdul Awal
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Clinical Decision Making ◽  
Average Length ◽  
Clinical Decision ◽  
Primary Data ◽  
Decision Making Process ◽  
Cross Sectional ◽  
The Right ◽  
Consultation Session

ABSTRACT Introduction Selecting the most appropriate treatment for each patient is the key activity in patient-physician encounters and providing healthcare services. Achieving desirable clinical goals mostly depends on making the right decision at the right time in any healthcare setting. But little is known about physicians' clinical decision-making in the primary care setting in Bangladesh. Therefore, this study explored the factors that influence decisions about prescribing medications, ordering pathologic tests, counseling patients, average length of patient visits in a consultation session, and referral of patients to other physicians or hospitals by physicians at Upazila Health Complexes (UHCs) in the country. It also explored the structure of physicians' social networks and their association with the decision-making process. Methods This was a cross-sectional descriptive study that used primary data collected from 85 physicians. The respondents, who work at UHCs in the Rajshahi Division, were selected purposively. The collected data were analyzed with descriptive statistics including frequency, percentage, one-way analysis of variance, and linear regression to understand relationships among the variables. Results The results of the study reveal that multiple factors influence physicians' decisions about prescribing medications, ordering pathologic tests, length of visits, counseling patients, and referring patients to other physicians or hospitals at the UHCs. Most physicians prescribe drugs to their patients, keeping in mind their purchasing capacity. Risk of violence by patients' relatives and better management are the two key factors that influence physicians' referral decisions. The physicians' professional and personal social networks also play an influential role in the decision-making process. It was found that physicians dedicate on average 16.17 minutes to a patient in a consultation session. The length of visits is influenced by various factors including the distance between the physicians' residence and their workplace, their level of education, and the number of colleagues with whom they have regular contact and from whom they can seek help. Conclusion The results of the study have yielded some novel insights about the complexity of physicians' everyday tasks at the UHCs in Bangladesh. The results would be of interest to public health researchers and policy makers.

scholarly journals Necrotizing fasciitis: presentation, microbiology and outcomes in a community hospital

2020 ◽  
Vol 7 (8) ◽  
pp. 2471
Author(s):  
Mercy N. Jimenez ◽  
Emily S. Seltzer ◽  
Bhavana Devanabanda ◽  
Martine Louis ◽  
Nageswara Mandava
Keyword(s):  
New York ◽  
Necrotizing Fasciitis ◽  
Clinical Decision Making ◽  
Community Hospital ◽  
Average Length ◽  
Retrospective Chart Review ◽  
Patient Characteristics ◽  
Clinical Decision ◽  
Blood Cultures ◽  
Average Length Of Stay

Background: Necrotizing fasciitis (NF) is an aggressive and often fatal, soft tissue infection. Delayed surgical therapy leads to worsened outcomes. This study evaluates the mortality, outcomes, and characteristics of patients with NF in a diverse New York City Community Hospital Network.Methods: Retrospective chart review from 2012 to 2019 using ICD-9 and ICD-10 codes of gas gangrene, Fournier’s gangrene, and necrotizing fasciitis was done. Of the 297 patients reviewed 28 met inclusion criteria of imaging findings, operative reports, and clinical diagnosis of NF by an attending surgeon.Results: On average patients in ER were seen by the surgical team within less than 12 hours. Most patients were debrided within 10 hours of surgical consultation and on average received 2.2 procedures. Of the wound cultures obtained 65.38% were polymicrobial in nature. The average length of stay was 17.4 days and 32% of patients required ICU admission. The surgical mortality rate was 7.61%.Conclusions: Necrotizing fasciitis is a rare entity and increasing provider knowledge on patient characteristics as well as the complexity of these patients and the types and number of procedures they require may help guide clinical decision making. We identified that while most of our patients had negative blood cultures on admission, those that had positive blood cultures had multiple organisms growing. Knowing that these patients are complex and likely require multiple procedures, prompt operative intervention is key.

Combined data mining techniques based patient data outlier detection for healthcare safety

2016 ◽  
Vol 9 (1) ◽  
pp. 42-68 ◽  
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Zhongshi He ◽  
Dawit Haile
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Patient Safety ◽  
Outlier Detection ◽  
Clinical Data ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Healthcare Services ◽  
Content Type ◽  
Outliers Detection

Purpose – Among the growing number of data mining (DM) techniques, outlier detection has gained importance in many applications and also attracted much attention in recent times. In the past, outlier detection researched papers appeared in a safety care that can view as searching for the needles in the haystack. However, outliers are not always erroneous. Therefore, the purpose of this paper is to investigate the role of outliers in healthcare services in general and patient safety care, in particular. Design/methodology/approach – It is a combined DM (clustering and the nearest neighbor) technique for outliers’ detection, which provides a clear understanding and meaningful insights to visualize the data behaviors for healthcare safety. The outcomes or the knowledge implicit is vitally essential to a proper clinical decision-making process. The method is important to the semantic, and the novel tactic of patients’ events and situations prove that play a significant role in the process of patient care safety and medications. Findings – The outcomes of the paper is discussing a novel and integrated methodology, which can be inferring for different biological data analysis. It is discussed as integrated DM techniques to optimize its performance in the field of health and medical science. It is an integrated method of outliers detection that can be extending for searching valuable information and knowledge implicit based on selected patient factors. Based on these facts, outliers are detected as clusters and point events, and novel ideas proposed to empower clinical services in consideration of customers’ satisfactions. It is also essential to be a baseline for further healthcare strategic development and research works. Research limitations/implications – This paper mainly focussed on outliers detections. Outlier isolation that are essential to investigate the reason how it happened and communications how to mitigate it did not touch. Therefore, the research can be extended more about the hierarchy of patient problems. Originality/value – DM is a dynamic and successful gateway for discovering useful knowledge for enhancing healthcare performances and patient safety. Clinical data based outlier detection is a basic task to achieve healthcare strategy. Therefore, in this paper, the authors focussed on combined DM techniques for a deep analysis of clinical data, which provide an optimal level of clinical decision-making processes. Proper clinical decisions can obtain in terms of attributes selections that important to know the influential factors or parameters of healthcare services. Therefore, using integrated clustering and nearest neighbors techniques give more acceptable searched such complex data outliers, which could be fundamental to further analysis of healthcare and patient safety situational analysis.

scholarly journals Predicting Cardiovascular Risk in Athletes: Resampling Improves Classification Performance

2020 ◽  
Vol 17 (21) ◽  
pp. 7923
Author(s):  
Davide Barbieri ◽  
Nitesh Chawla ◽  
Luciana Zaccagni ◽  
Tonći Grgurinović ◽  
Jelena Šarac ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Cardiovascular Risk ◽  
Clinical Decision Making ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Classification Performance ◽  
Biomedical Data ◽  
Sport Practice

Cardiovascular diseases are the main cause of death worldwide. The aim of the present study is to verify the performances of a data mining methodology in the evaluation of cardiovascular risk in athletes, and whether the results may be used to support clinical decision making. Anthropometric (height and weight), demographic (age and sex) and biomedical (blood pressure and pulse rate) data of 26,002 athletes were collected in 2012 during routine sport medical examinations, which included electrocardiography at rest. Subjects were involved in competitive sport practice, for which medical clearance was needed. Outcomes were negative for the largest majority, as expected in an active population. Resampling was applied to balance positive/negative class ratio. A decision tree and logistic regression were used to classify individuals as either at risk or not. The receiver operating characteristic curve was used to assess classification performances. Data mining and resampling improved cardiovascular risk assessment in terms of increased area under the curve. The proposed methodology can be effectively applied to biomedical data in order to optimize clinical decision making, and—at the same time—minimize the amount of unnecessary examinations.

scholarly journals Automatic Identification of High Impact Relevant Articles to Support Clinical Decision Making Using Attention-Based Deep Learning

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1364
Author(s):  
Beomjoo Park ◽  
Muhammad Afzal ◽  
Jamil Hussain ◽  
Asim Abbas ◽  
Sungyoung Lee
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
High Impact ◽  
Biomedical Literature ◽  
Classification Model ◽  
Evidence Based ◽  
Proposed Model

To support evidence-based precision medicine and clinical decision-making, we need to identify accurate, appropriate, and clinically relevant studies from voluminous biomedical literature. To address the issue of accurate identification of high impact relevant articles, we propose a novel approach of attention-based deep learning for finding and ranking relevant studies against a topic of interest. For learning the proposed model, we collect data consisting of 240,324 clinical articles from the 2018 Precision Medicine track in Text REtrieval Conference (TREC) to identify and rank relevant documents matched with the user query. We built a BERT (Bidirectional Encoder Representations from Transformers) based classification model to classify high and low impact articles. We contextualized word embedding to create vectors of the documents, and user queries combined with genetic information to find contextual similarity for determining the relevancy score to rank the articles. We compare our proposed model results with existing approaches and obtain a higher accuracy of 95.44% as compared to 94.57% (the next best performer) and get a higher precision by about 14% at P@5 (precision at 5) and about 12% at P@10 (precision at 10). The contextually viable and competitive outcomes of the proposed model confirm the suitability of our proposed model for use in domains like evidence-based precision medicine.

Preliminary Findings on the Use of Session Questionnaires to Improve Clinician-Client Alliance With Special Consideration to Clinical Education

2015 ◽  
Vol 25 (1) ◽  
pp. 50-60
Author(s):  
Anu Subramanian
Keyword(s):  
Graduate Students ◽  
Clinical Education ◽  
Clinical Decision Making ◽  
Rating Scale ◽  
Treatment Effectiveness ◽  
Clinical Decision ◽  
Added Value ◽  
Therapeutic Outcomes ◽  
Specific Feedback ◽  
Stakeholder Perspective

ASHA's focus on evidence-based practice (EBP) includes the family/stakeholder perspective as an important tenet in clinical decision making. The common factors model for treatment effectiveness postulates that clinician-client alliance positively impacts therapeutic outcomes and may be the most important factor for success. One strategy to improve alliance between a client and clinician is the use of outcome questionnaires. In the current study, eight parents of toddlers who attended therapy sessions at a university clinic responded to a session outcome questionnaire that included both rating scale and descriptive questions. Six graduate students completed a survey that included a question about the utility of the questionnaire. Results indicated that the descriptive questions added value and information compared to using only the rating scale. The students were varied in their responses regarding the effectiveness of the questionnaire to increase their comfort with parents. Information gathered from the questionnaire allowed for specific feedback to graduate students to change behaviors and created opportunities for general discussions regarding effective therapy techniques. In addition, the responses generated conversations between the client and clinician focused on clients' concerns. Involving the stakeholder in identifying both effective and ineffective aspects of therapy has advantages for clinical practice and education.

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