Extraction of Medical Pathways from Electronic Patient Records

With the introduction of electronic medical records, a large amount of patients’ medical data has been available. An actual problem in this domain is to perform reverse engineering of the medical treatment process to highlight medical pathways typically adopted for specific health conditions. This chapter addresses the ability of sequential data mining techniques to reconstruct the actual medical pathways followed by patients. Detected medical pathways are in the form of sets of exams frequently done together, sequences of exam sets frequently followed by patients and frequent correlations between exam sets. The analysis shows that the majority of the extracted pathways are consistent with the medical guidelines, but also reveals some unexpected results, which can be useful both to enrich existing guidelines and to improve the public sanitary service.

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Erratum to: Dose-Specific Adverse Drug Reaction Identification in Electronic Patient Records: Temporal Data Mining in an Inpatient Psychiatric Population

Drug Safety ◽

10.1007/s40264-014-0158-7 ◽

2014 ◽

Vol 37 (5) ◽

pp. 379-379 ◽

Cited By ~ 1

Author(s):

Robert Eriksson ◽

Thomas Werge ◽

Lars Juhl Jensen ◽

Søren Brunak

Keyword(s):

Data Mining ◽

Adverse Drug Reaction ◽

Drug Reaction ◽

Patient Records ◽

Temporal Data Mining ◽

Temporal Data ◽

Electronic Patient Records ◽

Psychiatric Population

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Management of Medical Data in Emerging Threats

Studia Universitatis Babeş-Bolyai Bioethica ◽

10.24193/subbbioethica.2021.spiss.57 ◽

2021 ◽

Vol 66 (Special Issue) ◽

pp. 92-92

Author(s):

Sorin Hostiuc ◽

◽

Keyword(s):

Ethical Issues ◽

Optimal Solution ◽

Medical Problem ◽

Medical Data ◽

Fake News ◽

Public Authorities ◽

Actual Problem ◽

The Public ◽

Moral Duties ◽

Professional Codes

"Emerging viral or bacterial threats pose significant medical and ethical issues, caused not only by the management of the disease, but also by the uncontrolled dissemination of information, both true and fake. Even the most correct and impartially presented piece of information can be understood by the patients or by the public at large in ways that are opposite to those intended by the communicators. This, associated with the increased prevalence of fake news, may cause havoc and decrease the efficacy of the needed preventive measures that have to be taken to tackle the actual medical problem. Within the context of the coronavirus outbreak, this has been coined as “infodemic”, increasing the difficulty of finding an optimal solution to the actual problem. Medical data about an emerging medical threat is disseminated through mass – and social media, especially by public authorities and physicians. The latter have specific duties, appertaining to their professional codes of morals, toward minimizing the harms generated by diseases, both at a personal and at a populational level. In infodemics, the management of the information they present to the public is extremely important, as each wording can be improperly interpreted and cause opposite effects. In this paper, we will discuss whether and which healthcare professionals should be involved in disseminating information about emerging healthcare threats, and which moral duties should prevail in these instances. "

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Data-mining of 110 172 electronic patient records with the ConSoRe tool: An analysis of second primary cancer in a comprehensive cancer center

Annals of Oncology ◽

10.1093/annonc/mdy294.009 ◽

2018 ◽

Vol 29 ◽

pp. viii482

Author(s):

P. Heudel ◽

T. Durand ◽

B. Fervers ◽

F. Gomez ◽

M. Rivoire ◽

...

Keyword(s):

Data Mining ◽

Comprehensive Cancer Center ◽

Cancer Center ◽

Second Primary ◽

Patient Records ◽

Primary Cancer ◽

Second Primary Cancer ◽

Electronic Patient Records

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Dose-Specific Adverse Drug Reaction Identification in Electronic Patient Records: Temporal Data Mining in an Inpatient Psychiatric Population

Drug Safety ◽

10.1007/s40264-014-0145-z ◽

2014 ◽

Vol 37 (4) ◽

pp. 237-247 ◽

Cited By ~ 24

Author(s):

Robert Eriksson ◽

Thomas Werge ◽

Lars Juhl Jensen ◽

Søren Brunak

Keyword(s):

Data Mining ◽

Adverse Drug Reaction ◽

Drug Reaction ◽

Patient Records ◽

Temporal Data Mining ◽

Temporal Data ◽

Electronic Patient Records ◽

Psychiatric Population

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Usability and feasibility of a mobile health system to provide comprehensive antenatal care in low-income countries: PANDA mHealth pilot study in Madagascar

Journal of Telemedicine and Telecare ◽

10.1177/1357633x16653540 ◽

2016 ◽

Vol 23 (5) ◽

pp. 536-543 ◽

Cited By ~ 10

Author(s):

Anne Caroline Benski ◽

Giovanna Stancanelli ◽

Stefano Scaringella ◽

Josea Léa Herinainasolo ◽

Jéromine Jinoro ◽

...

Keyword(s):

Health Care ◽

Pilot Study ◽

Antenatal Care ◽

Health System ◽

Pregnant Women ◽

Mobile Health ◽

Medical Data ◽

Patient Records ◽

Electronic Patient Records ◽

High Quality

Background Madagascar’s maternal health mortality ratio in 2013 was 478 deaths per 100,000 live births. Most deaths are related to direct complications during pregnancy and childbirth and could be reduced by providing comprehensive antenatal care (ANC). Objective The objective of the study was to assess the usability and feasibility of a mobile health system (mHealth) to provide high-quality ANC, according to World Health Organization (WHO) recommendations. Methods PANDA (Pregnancy And Newborn Diagnostic Assessment) is an easy-to-use mHealth system that uses affordable communications technology to support diagnosis and health care worker decision-making regarding ANC. From January to March 2015, a cross-sectional pilot study was conducted in Ambanja District, Madagascar, in which ANC using PANDA was provided to 100 pregnant women. The collected data were transmitted to a database in the referral hospital to create individual electronic patient records. Accuracy and completeness of the data were closely controlled. The PANDA software was assessed and the number of abnormal results, treatments performed, and participants requiring referral to health care facilities were monitored. Results The PANDA system facilitated creation of individual electronic patient records that included socio-demographic and medical data for 100 participants. Duration of ANC visits averaged 29.6 min. Health care providers were able to collect all variables (100%) describing personal and medical data. No major technical problems were encountered and no data were lost. During 17 ANC visits (17%), an alert function was generated to highlight abnormal clinical results requiring therapy or referral to an affiliated hospital. Participants’ acceptability of the system was very high. Conclusion This pilot study proved the usability and feasibility of the PANDA mHealth system to conduct complete and standardised ANC visits according to WHO guidelines, thus providing a promising solution to increase access to high-quality and standardised ANC for pregnant women in remote areas.

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Electronic Patient Records in Medical Practice: A Multidisciplinary Endeavor

Methods of Information in Medicine ◽

10.1055/s-0038-1634416 ◽

1999 ◽

Vol 38 (04/05) ◽

pp. 287-288 ◽

Cited By ~ 1

Author(s):

J. van der Lei ◽

P. W. Moorman ◽

M. A. Musen

Keyword(s):

Medical Practice ◽

Patient Records ◽

Electronic Patient Records

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Postmarketing Surveillance Based on Electronic Patient Records: The IPCI Project

Methods of Information in Medicine ◽

10.1055/s-0038-1634402 ◽

1999 ◽

Vol 38 (04/05) ◽

pp. 339-344 ◽

Cited By ~ 88

Author(s):

J. van der Lei ◽

B. M. Th. Mosseveld ◽

M. A. M. van Wijk ◽

P. D. van der Linden ◽

M. C. J. M. Sturkenboom ◽

...

Keyword(s):

General Practitioner ◽

General Practitioners ◽

Validation Studies ◽

Resource Planning ◽

Routine Practice ◽

Patient Records ◽

Postmarketing Surveillance ◽

Electronic Patient Records ◽

Use Of Data ◽

Data Requirements

AbstractResearchers claim that data in electronic patient records can be used for a variety of purposes including individual patient care, management, and resource planning for scientific research. Our objective in the project Integrated Primary Care Information (IPCI) was to assess whether the electronic patient records of Dutch general practitioners contain sufficient data to perform studies in the area of postmarketing surveillance studies. We determined the data requirements for postmarketing surveil-lance studies, implemented additional software in the electronic patient records of the general practitioner, developed an organization to monitor the use of data, and performed validation studies to test the quality of the data. Analysis of the data requirements showed that additional software had to be installed to collect data that is not recorded in routine practice. To avoid having to obtain informed consent from each enrolled patient, we developed IPCI as a semianonymous system: both patients and participating general practitioners are anonymous for the researchers. Under specific circumstances, the researcher can contact indirectly (through a trusted third party) the physician that made the data available. Only the treating general practitioner is able to decode the identity of his patients. A Board of Supervisors predominantly consisting of participating general practitioners monitors the use of data. Validation studies show the data can be used for postmarketing surveillance. With additional software to collect data not normally recorded in routine practice, data from electronic patient record of general practitioners can be used for postmarketing surveillance.

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Reverse Sequential Covering Algorithm for Medical Data Mining

SSRN Electronic Journal ◽

10.2139/ssrn.3509481 ◽

2014 ◽

Author(s):

Aneeshkumar A. S. ◽

C Jothi Venkateswaran

Keyword(s):

Data Mining ◽

Medical Data ◽

Medical Data Mining ◽

Sequential Covering

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OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽

10.3390/data6010001 ◽

2020 ◽

Vol 6 (1) ◽

pp. 1

Author(s):

Ahmed Elmogy ◽

Hamada Rizk ◽

Amany M. Sarhan

Keyword(s):

Data Mining ◽

Image Processing ◽

Intrusion Detection ◽

Real Time ◽

Outlier Detection ◽

Real World ◽

Medical Data ◽

Experimental Results ◽

Real Time Applications ◽

Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

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