Toward precise and preventive healthcare with computational tools

Chiu, Hung Wen; Li, Yu-Chuan

doi:10.1016/s0169-2607(17)31385-8

Cited by 2 publications

(3 citation statements)

References 3 publications

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“…Since all other values {A(d); d ̸ = 10} are greater than one σ A below the optimal value A o , we also here conclude that the result of the automatic diagnosis points to the correct disease being 10: otitis media. (symptom -s, indicator -i, weight -W (10, s, i)) D (10) (2,1,2) (3,2,1) (4,1,1) (6,4,2) (11,2,3) (29,1,1) patient Y (2,1,2) (3,2,1) (4,1,1) (6,4,2) (11,2,3) Weights at other indices are 0.…”

Section: Clinical Testsmentioning

confidence: 99%

“…In the past symptoms were chiefly evaluated by direct observations, while nowadays ever more instrumental techniques, and with them related quantitative variables, are utilized for this purpose [1,2,3]. Along with this development various information processing techniques are invented and introduced into the medical practice with the aim of supporting doctors decision making for diagnoses, as well as in cases where people are interested in assessing their health states [2,3,4,5]. Consequently, the research, innovations and new developments in this field are widely supported by various foundations at national and international levels.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Automatic Medical Diagnosis

Grabec

Švegl

Sok

2019

Stat., optim. inf. comput.

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This research paper presents a new method for the automatic diagnosis of diseases using a personal computer. Forming a basis for the characterization of diseases, a wide set of symptoms is introduced, and a particular disease is characterized by a set of statistical weights assigned to those symptoms. Information about the patient state is provided by a graphic interface in which the user confirms symptom indicators. Agreement between these symptoms and classified symptoms of a particular disease is then estimated by the sum of corresponding weights, where the disease corresponding to the maximal agreement is proposed as the result of the diagnosis. A disease likelihood estimator is calculated and presented to assess the reliability of the diagnosis. With regard to the automatic assessment of the diagnosis the corresponding algorithm and the properties of the computer program are included. Finally, the effectiveness of this method of medical diagnosis is demonstrated through four typical examples involving differently expressed symptoms. The diagnostic system resembles semantically driven sensory-neural network.

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Section: Clinical Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method for Automatic Medical Diagnosis

Grabec

Švegl

Sok

2019

Stat., optim. inf. comput.

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“…Precise preventive health services emphasize managing chronic diseases and developing individualized risk prediction to improve the quality and effectiveness of health care [1]. The ideal disease prevention should start with screening risk factors for predicting risk and then promote health throughout a patient's entire life.…”

Section: Introductionmentioning

confidence: 99%

Developing and Validating Risk Scores for Predicting Major Cardiovascular Events Using Population Surveys Linked with Electronic Health Insurance Records

Chang

Fang

Huang

et al. 2022

IJERPH

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A risk prediction model for major cardiovascular events was developed using population survey data linked to National Health Insurance (NHI) claim data and the death registry. Another set of population survey data were used to validate the model. The model was built using the Nutrition and Health Survey in Taiwan (NAHSIT) collected from 1993–1996 and linked with 10 years of events from NHI data. Major adverse cardiovascular events (MACEs) were identified based on hospital admission or death from coronary heart disease or stroke. The Taiwanese Survey on Hypertension, Hyperglycemia, and Hyperlipidemia (TwSHHH), conducted in 2002 was used for external validation. The NAHSIT data consisted of 1658 men and 1652 women aged 35–70 years. The incidence rates for MACE per 1000 person-years were 13.77 for men and 7.76 for women. These incidence rates for the TwSHHH were 7.27 for men and 3.58 for women. The model had reasonable discrimination (C-indexes: 0.76 for men; 0.75 for women), thus can be used to predict MACE risks in the general population. NHI data can be used to identify disease statuses if the definition and algorithm are clearly defined. Precise preventive health services in Taiwan can be based on this model.

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Toward precise and preventive healthcare with computational tools

Cited by 2 publications

References 3 publications

A Method for Automatic Medical Diagnosis

A Method for Automatic Medical Diagnosis

Developing and Validating Risk Scores for Predicting Major Cardiovascular Events Using Population Surveys Linked with Electronic Health Insurance Records

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