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NIH 2013 Program - University of the Philippines Manila

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4.3 Normal lung sound enhancement and characterization for automatic<br />

identification <strong>of</strong> lung pathologies<br />

(1) Department <strong>of</strong> Computer Technology, De La Salle <strong>University</strong><br />

(2) Philippine General Hospital, <strong>University</strong> <strong>of</strong> <strong>the</strong> <strong>Philippines</strong> <strong>Manila</strong><br />

Cadwallader C Chua (1), Kevin Lloyd D Cocuaco (1), Macario O Cordel II (1), Joel P Ilao (1),<br />

Alexis Jamie R Lao (1), Eldridge Sherwin S Tan (1), Adrian Paul J Rabe (2)<br />

Respiratory sounds are considered as non-invasive measurement <strong>of</strong> airway condition. Using a<br />

stethoscope, physicians can determine abnormalities by observing <strong>the</strong> patterns in lung sounds;<br />

this method is called auscultation. However, in a typical environment, auscultation is performed in<br />

rooms susceptible to different sounds such as vocal sound, ventilation machines, and ambient<br />

noise, which may impede <strong>the</strong> subjective evaluation <strong>of</strong> patterns heard from <strong>the</strong> lung sounds.<br />

This work presented a simple signal enhancement scheme for normal lung sounds in order to<br />

successfully extract <strong>the</strong> key signal patterns or features. Results showed that <strong>the</strong> enhanced signal<br />

had features (bandwidth, peak frequency, and center frequency) in <strong>the</strong> 300 to 700 Hz range while<br />

<strong>the</strong> raw and ‘denoised’ signals had features below 300 Hz. Listening test showed improved score<br />

in enhanced signals over <strong>the</strong> raw and ‘denoised’ signals with an average score <strong>of</strong> 1.3 (out <strong>of</strong> 3) as<br />

compared with 1.03 in raw and 0.82 in ‘denoised’ signals.<br />

5.1 Economics <strong>of</strong> health for productivity: role <strong>of</strong> economic evaluation in policy<br />

Department <strong>of</strong> Clinical Epidemiology, College <strong>of</strong> Medicine, <strong>University</strong> <strong>of</strong> <strong>the</strong> <strong>Philippines</strong> <strong>Manila</strong><br />

Carlo Irwin A Panelo<br />

Department <strong>of</strong> Clinical Epidemiology, College <strong>of</strong> Medicine, <strong>University</strong> <strong>of</strong> <strong>the</strong> <strong>Philippines</strong> <strong>Manila</strong><br />

Policy makers, service providers, and even families make use <strong>of</strong> economic tools to choose health<br />

care interventions, given <strong>the</strong> scarce resources. These tools range from <strong>the</strong> fairly sophisticated to<br />

<strong>the</strong> simple, intuitive or back <strong>of</strong> <strong>the</strong> envelope calculations.<br />

This presentation focuses on <strong>the</strong> application <strong>of</strong> economic tools for healthcare decision-making by<br />

governments or large insurance systems. Examples will be provided on how <strong>the</strong>se economic<br />

evaluation tools are applied to arrive at specific health care policy decisions and <strong>the</strong>ir impacts on<br />

health outcomes. These specific examples will also allude to <strong>the</strong> papers that will be presented as<br />

part <strong>of</strong> this panel. The role <strong>of</strong> economic evaluation will also be contextualized in <strong>the</strong> light <strong>of</strong> o<strong>the</strong>r<br />

considerations and bases that influence decision-making.<br />

SCIENCE AND HEALTH FOR SOCIAL AND ECONOMIC DEVELOPMENT 33

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